Vertice

Designing Reliable Agent Flows on Oracle AI Data Platform Workbench: Practical Lessons from the Field

colmmccarthy — Tue, 14 Jul 2026 12:02:11 +0000

Author: Philip Godfrey

An introduction to Agent Flows

Oracle AI Data Platform Workbench introduced Agent Flows on 14^th July 2026, providing a space for users to build, test and deploy custom Agent Flows.

This addition to the workbench allows customers and businesses alike to build Agent Flows to help you achieve ROI on AI, through successful building, testing and deployment of Agent Flows.

This blog aims to guide users on best practices, based on our experience in the field, to create Agent Flows that deliver value.

What is Agent Flows in AIDP Workbench?

Agent flows are end-to-end applications incorporating AI agents, tools, and access to data.

What you build depends on the business problem you are trying to solve, but some key points to be aware of around the make-up of an Agent Flow:

Triggers

The starting point of your Agent Flow, the chat trigger. This is the entry point where you would like the user communication to begin. These typically connect to guardrails or supervisor agents next.

Nodes

The second layer in your Agent Flow, which is made up of a Supervisor Agent, which is your orchestrator; an Executor Agent underneath, to perform your specific tasks, and Guardrails (optional) are your policy and safety layer, assisting with PII & content moderation.

Tools

The final layer in your flow, and come in the shape of tool templates, with 6 tools available for inclusion, out of the box.

What Tools are available?

Tool templates are accessible by the Agent Flow and can be leveraged to help build flows.

SQL Tool

Provide SQL commands to interrogate data from connected database.

RAG Tool

Retrieve information from connected knowledge bases and aims to respond with relevant and context-aware information in natural language.

Prompt Tool

Uses a parameterised prompt to invoke a chosen Large Language Model, enabling text generation and transformation tasks such as summarisation, translation, drafting and code explanation.

HTTP Tool

Allows your agent to call any HTTPS REST API. You simply configure the request, and the agent invokes at runtime.

Remote MCP Server

Developers can connect agents to Remote Model Context Protocol (MCP) Servers.

Custom Tool

Provide custom Python code as a tool package for your agent to access, allowing you to extend AIDP functionality effortlessly

For more information on tooling options, you can explore here:

Practical Design Lessons for Building Reliable Agent Flows

Agent Flows are very powerful tools, but when used incorrectly can be costly, not only of tokens and money, but also valuable time.

Vertice has a long history of best practice and understanding technology and how it works, so want to provide our expertise to make sure that you can make best use of Agent Flows, as quickly as possible.

These lessons reflect patterns we’ve observed across customer engagements, combined with hands-on experience building, testing, and refining Agent Flows in the field.

Lesson 1: Don’t Design a Single Agent Flow to Do Everything

It can be tempting to start with the most difficult and complex use case (high risk / high rewards). You need to break these down into smaller tasks (or sub-tasks) – design an agent to do one job, and do it very well.

Why is this important?

Debugging complex agent flows with multiple tools & actions included can become very complex, very quickly, and small changes can have unintended consequences downstream.

Lesson 2: Test What the Agent Flow Is Not Designed to Do

Imagine the scenario, you have built and test an Agent Flow, and it’s working well and you think it might be ready for a production deployment. The problem here is not what the agent is doing correctly, but what it’s setup not to do.

Why is this important?

To properly assess if your agent flow is ready for production, you need to design test strategies to handle the edge cases.

What happens if someone passes in an ambiguous comment?

What happens if there is data missing at the beginning of the chain or part-way through?

Build Agent Flows to handle missing data is imperative. Pilot data may always be available, but production isn’t.

Lesson 3: Builders Should Not Be the Only Testers

When the same people who build Agent Flows also test them, the flow almost always passes, why is this? Because builders understand the data, intent of the agent and the expected behaviour of how it should work and behave.

Why is this important?

Your end-users likely aren’t builders and will therefore do unexpected things that provide unexpected results. This is where many production failures often occur, so including this as part of your testing is key.

Lesson 4: Agent Flows Are Systems, Not Prompt Engineering

A common mistake is placing too much logic inside prompts or agent instructions. Agent Flows should be designed as systems, not large prompts.

Why is this important?

Agent Flows incorporates multiple tooling (Prompt / SQL / RAG tools) and must be designed with this in mind. Each tool will perform a distinct task and will do it well. Also keep in mind what is downstream.

You wouldn’t pass all your data to a prompt and ask it to summarise, so don’t do the same with Agent tooling.

Lesson 5: Build complex agents incrementally, one tool at a time

Complex agents rarely work correctly when built all at once. Adding multiple SQL tools, prompts, and branching logic before testing makes failures difficult to diagnose.

Instead, treat agent design as incremental system construction:

Add one tool
Test it
Validate outputs
Then add the next capability

Why is this important?

Agent Flows behave more like software systems than prompts, therefore building them incrementally reduces risk and improves reliability.

Operational Considerations Often Overlooked Early

While not unique to Agent Flows, the following operational practices become increasingly important as usage grows:

Monitor session metrics and token usage
Agent Flows can consume tokens quickly, especially when prompts grow or multiple tools are invoked. Reviewing session metrics helps identify inefficiencies early.
Turn off AI compute when not in use
Dedicated AI compute required for Agent Flows, which can remain active longer than needed. Deactivating compute when not required prevents unnecessary spend.
Revisit prompt size regularly
Larger prompts increase latency and cost without always improving results.
Validate usage patterns before production rollout
Pilot usage rarely reflects real production load.

Interested to know more?

We’d be more than happy to discuss your requirements or questions in more detail, please get in touch with us here.

What about training?

Vertice also provides tailored training around AI Data Platform Workbench, inclusive of Agent Flows, please check availability here.

connect@verticecloud.com

Philip Godfrey

Principal AI & Data Analytics Consultant

The post Designing Reliable Agent Flows on Oracle AI Data Platform Workbench: Practical Lessons from the Field appeared first on Vertice.

Oracle AI Data Platform for Life Sciences Case Study: A Patient Companion App for Asthma and COPD

colmmccarthy — Tue, 07 Jul 2026 08:20:00 +0000

Author: John Fitts

TLDR

University College Dublin Clinical Research Centre partnered with Vertice to build a patient-led companion app for people living with asthma and Chronic Obstructive Pulmonary Disease (COPD). The app lets patients record their experience in their own voice, in the moment a flare-up happens, and uses retrieval-augmented generation (RAG) to blend that narrative with clinical data and open environmental data such as pollen counts, weather, humidity, and air quality. It was built on Oracle AI Data Platform (AIDP) and taken from concept to a working pilot in a matter of weeks, using fully synthetic patient data and open data sets. As one of the earliest deployments of its kind on Oracle AIDP, it gives patients a way to be heard between appointments and gives clinicians a richer, structured, longitudinal dataset to support better care. The pilot went live in November of 2025, and UCD CRC and Vertice are now scaling the same architecture to further chronic disease and clinical trial use cases.

The UCD Clinical Research Centre and Vertice partnership

UCD Clinical Research Centre was established in 2006 to design and conduct high-quality, patient-centred clinical research. Vertice is an Oracle implementation partner specializing in end-to-end Data & AI solutions, and the two organizations have worked together for several years across a range of research projects. When Vertice gained early access to Oracle AI Data Platform, clinical research was a natural place to start, and UCD CRC’s Executive Director, Colm McMahon, brought Vertice a respiratory care challenge worth solving.

The clinical perspective came from two leading respiratory medicine experts, Cormac McCarthy and Dr Alex Franciosi, working alongside UCD CRC’s respiratory team and its partnership with St Vincent’s Hospital Group. They selected asthma and COPD as the first use case. UCD CRC designed the study and owned the research requirements, while Vertice handled the technical AI-Applied solution design and delivery, allowing clinicians to focus on the nuances of the patient experience.

The Challenge: Capturing the Patient Voice in a 10-Minute Appointment

For someone living with a chronic respiratory condition, the condition is with them every day, yet the window to describe it to a clinician is small. Appointments are often only five or ten minutes long, and weeks or months can pass between them. Compressing months of symptoms, flare-ups, and daily experience into that short window is genuinely difficult. Patients do not always remember the full picture, and clinicians are under pressure to capture as much as possible in the time available.

The result is an information gap. Patients can feel frustrated and unheard, and clinicians work from data that is inconsistent and incomplete. Capturing the patient’s voice accurately and completely in such a condensed setting is the core problem and yet blending that voice with clinical treatment data and environmental factors is exactly what better care depends on.

The Solution: a Patient-Led Companion App Built on Oracle AIDP

UCD CRC and Vertice built a patient-led companion app that captures the patient’s experience naturally, anytime and anywhere, in the moment it happens. Because people are now comfortable talking to conversational interfaces, speaking to the app about a flare-up feels natural. A patient can simply say something like “I felt breathless this morning,” and the app records it.

The app goes well beyond a simple diary. It combines the patient’s qualitative narrative with quantitative open data such as pollen counts, weather, humidity, and air quality. Using retrieval-augmented generation (RAG), it correlates what the patient describes with clinical and environmental data held in the platform, and offers personalized, real-time guidance. For example, it might explain that air quality was good locally but the pollen count was higher than normal, suggest staying indoors until conditions improve, and share a trusted resource such as the asthma action plan from the Asthma Society of Ireland.

This creates a complete and clinically useful picture of a patient’s health over time, blending the human experience of living with the condition with the clinical and environmental context around it.

Why Oracle AI Data Platform Was Key

Vertice chose Oracle AI Data Platform because the solution needed both breadth and depth: a conversational, agentic experience for patients, blended with clinical treatment data and several sets of environmental data, all under proper governance. AIDP brought several things together that made the build possible in the timeframe.

The platform’s AI tooling accelerated development of the chatbot and the analysis and reporting functionality. Its open framework let the team standardize diverse data types using common data models, which matters when you are combining spoken patient narratives with structured clinical and environmental data. Its lakehouse foundation unified that structured and unstructured data in one governed place. And because the work involves sensitive patient information, the security and governance built into AIDP, running on Oracle Cloud Infrastructure, gave the team a trusted platform to build on and to scale.

How the Oracle AIDP Deployment Was Delivered

Vertice managed the governance and compliance aspects of the platform and prototyped a working demonstrator within a number of weeks. The pilot used fully synthetic patient data alongside open data sets, which allowed the team to develop a genuine decision-support tool that can later scale in a fully governed and compliant way once real patient data is introduced.

The application was built on the underlying Oracle AI Data Platform, before the dedicated Oracle Life Sciences AI Data Platform variant was announced. That makes it one of the earliest deployments of its kind, and reflects Vertice’s position as the first to implement Oracle AIDP at scale. The story was featured on Oracle TV at Oracle AI World 2025.

The Impact: Empowered Patients, Better Care

For patients, the app provides a way to be heard between appointments and to take a more active role in managing their own condition. They can capture their experience as it happens and bring a fuller, more accurate account to their clinicians.

For clinicians, the value is a rich, structured dataset. The app converts conversational input into structured data in a common data model, which means the spoken records of many patients can be brought together into a unique dataset of patient-reported outcomes. That complements clinical data and supports longitudinal analysis, helping clinicians track the bigger picture over time, spot patterns, trends, and anomalies, and ask questions of the data in natural language to inform care pathways in chronic disease.

The longer-term impact extends to how clinical time is used. By understanding how patients can be supported in the community using this data, the model supports a shift toward community-based care for chronic conditions, while keeping clinicians connected to what is happening in patients’ real lives. In Colm McMahon’s words, it is all about a better standard of care for patients.

Scaling The Solution: Further Chronic Disease and Clinical Trial Use Cases

The pilot is live and the partnership is ongoing. UCD CRC and Vertice are now scaling the solution and building out an innovation library of reusable use cases. Respiratory disease is the first use case, and the same architecture and framework can be applied to many other chronic conditions and to clinical trials, where capturing the patient experience during a trial is a recurring challenge. Every one of those future use cases relies on the same governed AIDP foundation the partners have already built.

Oracle AIDP for Life Sciences: Use Cases and Faster Time to Value

This project was delivered on the underlying Oracle AI Data Platform, which Oracle launched in October 2025. In January 2026, Oracle announced the Oracle Life Sciences AI Data Platform, a version shaped specifically for pharmaceutical, medical device, clinical research, and healthcare organizations, pre-populated with more than 129 million de-identified longitudinal real-world data records.

For organizations starting out now, that pre-built foundation changes the economics of getting going. Because the Life Sciences platform arrives with curated healthcare datasets, established clinical ontologies, industry-specific tooling, and fully managed infrastructure already in place, much of the groundwork that a team would otherwise build from scratch is ready on day one. That shortens proof-of-concept timelines and speeds up time to value, so a project like the one UCD CRC and Vertice delivered on the underlying platform could move from idea to working use case even faster on the Life Sciences variant. If you want to understand what that platform is, what it does, and what it changes for clinical research, pharma, and MedTech leaders, our plain-English guide explains it in full: What is Oracle AIDP for Life Sciences?

Frequently Asked Questions

What did UCD Clinical Research Centre and Vertice build?

Phil Godfrey Named an Oracle Analytics and AI Ambassador for 2026

Conor Kehoe — Mon, 22 Jun 2026 13:19:49 +0000

Vertice Oracle ACE Pro joins the global group of community leaders recognised by Oracle for their contributions to analytics and AI.

We are proud to share that Phil Godfrey, Principal AI & Data Analytics Consultant at Vertice and an Oracle ACE Pro, has been recognised as one of Oracle’s Analytics and AI Ambassadors for 2026.

The Ambassador program, announced by Oracle’s Analytics and AI Data Platform Product Strategy team, celebrates community members whose expertise, advocacy, and willingness to share knowledge help shape the future of Oracle Analytics and the Oracle AI Data Platform. Each year, a select group is chosen from across the global Oracle community for their active contributions through forums, blogs, events, Idea Labs, and data storytelling work.

Phil joins a group of practitioners and innovators from around the world whose contributions strengthen the wider Oracle Analytics and AI community. The Ambassador recognition adds to an already significant standing in the Oracle ecosystem, with Phil also serving as an Oracle Advisory partner alongside his ACE Pro status. You can read Oracle’s full announcement and see the complete list of 2026 Ambassadors on the Oracle Analytics blog.

Interested to know more?

Vertice is a specialist Oracle AI Data Platform implementation firm helping organisations across financial services, life sciences, and healthcare turn their data into a foundation for AI. To learn more about how Vertice helps organisations get the most from Oracle Analytics and the Oracle AI Data Platform, get in touch at connect@verticecloud.com

connect@verticecloud.com

The post Phil Godfrey Named an Oracle Analytics and AI Ambassador for 2026 appeared first on Vertice.

Oracle AI Data Platform for Financial Services

colmmccarthy — Mon, 15 Jun 2026 14:43:25 +0000

Author: John Fitts

What Is Oracle AI Data Platform for Financial Services

Oracle AI Data Platform (AIDP) is a cloud-based platform that brings enterprise data, generative AI models, and AI agents together in a single, governed environment. When applied to financial services, it gives banks, insurers, and capital markets firms a foundation to unify their data and apply AI across origination, payments, risk, compliance, fraud, and customer service.

Oracle launched the general AI Data Platform in October 2025 and introduced its agentic platform for banking on 3 February 2026 at the Oracle Financial Services Summit in New York. Throughout this article, “AIDP” refers to the underlying platform. Where the distinction matters, the financial services and banking capabilities are named directly.

Why does the Oracle AI Data Platform for Financial Services conversation matter now?

Oracle AIDP for Financial Services is designed to help institutions make faster and more consistent credit decisions, respond to fraud in real time, produce more accurate regulatory reporting, and deliver hyper-personalized service whether a customer is banking online, on a mobile device, or in a branch. The outcomes it targets are measurable, covering shorter time to insight, lower operational cost, and stronger risk and compliance posture.

Delivering against those outcomes depends on one thing first, which is the data sitting underneath the AI.

This is the issue that stalls most financial services AI programes. Data readiness can be framed in three parts. The first is that the data is rarely AI-ready or consistently trusted, because it sits locked inside siloed CRM, risk, and ERP systems, where duplication, inconsistent formats, missing metadata, and residency requirements create incomplete views and complicate regulatory reporting. The second is that off-the-shelf models lack banking context, so they misread complex agreements, exposures, and events, and terminology that varies by region and business type erodes the accuracy of term sheets, KYC files, and regulatory outputs. The third is that even good insight fails when it cannot integrate with core banking, payment, AML/KYC, trading, and decision engines, so it arrives too late and forces manual workarounds.

Until the underlying data is unified, governed, and given financial meaning, AI projects tend to stall before they reach production. That is the problem AIDP for Financial Services is built to address.

We saw this play out firsthand in a recent deployment we built an AI-powered patient companion for respiratory conditions on Oracle AIDP. The data challenge there was patient data governance, and honestly, the pattern is the same one you see in banking and insurance, just with different labels. Swap patient records for customer financial data, transaction histories, or underwriting records, and the bottleneck is identical: the AI and analytics layer is ready long before the data is. One of the things that saved us roughly 12 to 14 weeks was using synthetic data to validate the use case before anyone had to touch production data. And if you think about a bank trying to prove out a fraud detection model or a credit decisioning agent, that same approach applies, because the conversation you need to have with your compliance team is a lot easier when you can show them a working proof of concept built on synthetic data rather than asking for access to live customer records on day one.

What is Oracle AI Data Platform?

Oracle AI Data Platform combines four capabilities in a single environment:

A unified data foundation

AIDP provides a lakehouse architecture that brings structured and unstructured data into one place and removes duplication, with a unified catalog providing governance across all data and AI assets. For a financial institution, this means connecting deposit, loan, payment, CRM, risk, and ERP financials into a single trusted, real-time dataset that can serve Customer 360, risk modeling, and portfolio decisioning from one source of truth. Because AI needs data presented in a form models can reason over, the foundation also includes feature stores and vector stores alongside the established data lake and warehouse layers.

Built-in generative AI and agentic capability

The platform includes integrated access to generative AI models, vector indexing, retrieval-augmented generation (RAG), and the tooling to build, deploy, and manage AI agents. It supports open standards including Agent2Agent (A2A) and Model Context Protocol (MCP), which allow agents to talk to each other and to combine a bank’s private data with foundational language models safely. An intelligence layer sits between the agents and those models, handling orchestration, runtime, hallucination checks, and model risk management so that agentic behaviour stays inside the institution’s risk tolerance.

Integration with the wider Oracle ecosystem

AIDP connects to existing enterprise systems through Zero-ETL and Zero Copy capabilities and supports multicloud and hybrid environments, with the ability to process data from on-premises and edge sources. For banking specifically, Oracle provides an Interconnect capability to bring data from legacy mainframe core systems into the modern platform, alongside a Cloud Native Fabric that lets each product run in the deployment model of choice, whether that is multi-tenant OCI SaaS, a bring-your-own-cloud strategy, or Oracle’s cloud-at-customer running inside the bank’s own data centre.

Enterprise-grade security and governance

The platform is built on Oracle Cloud Infrastructure (OCI) with the security, compliance, and governance controls required for regulated industries, including audit trails, role-based access, and a control-by-design layer that cuts across the whole architecture to keep entitlement, governance, and security consistent.

How does Oracle AIDP for Financial Services build on Oracle AIDP

Institutions can now start with connected and trusted banking data, a financial context layer, pre-built agents, and fully managed infrastructure. This shortens the path to proof of concept, analytics deployment, and AI experimentation, because much of the foundational work is already in place.

Because the data is centralized, standardized, and given financial meaning, the same platform can be reused across many domains. Once the governed banking data foundation exists, new applications become composable, AI becomes reusable, and experimentation becomes faster.

What does this mean for Financial Services?

The financial services capability is a version of AIDP shaped for the data, workflows, and regulatory environment of banks, insurers, and capital markets firms. Three things distinguish it from a generic implementation

A financial services context layer

Oracle applies industry models that treat customers, accounts, products, transactions, exposures, and compliance metrics as first-class concepts, keeping insight such as liquidity, credit risk, and capital adequacy consistent across teams and regions and supporting frameworks such as Basel and IFRS from a single governed foundation.

A pre-built agentic banking architecture

Industry tooling and extensibility

Users work through familiar Oracle services such as Autonomous Data Warehouse, OCI Data Science, Oracle Analytics Cloud, Oracle Machine Learning, and OCI Data Catalog, and a financial services studio lets institutions extend the shipped agents and capabilities to suit their own products and processes.

Oracle AIDP for Financial Services use cases

Oracle’s financial services materials point to a number of areas the platform is designed to support. Each represents a business outcome rather than a technical capability.

Origination and credit decisioning

Experience and domain agents work across the originations lifecycle to speed application completion, predict delays, and produce faster, more consistent credit decisions, with human bankers kept in the loop for oversight.

Collections

Agents summarise collector calls to reduce handling time and check call tone and sentiment against regulatory rules, supporting a faster, lower-risk, and more compliant collections environment.

Fraud detection and financial crime investigation

AI agents collect and correlate data from across systems, generate investigation narratives, and surface the highest-risk cases, helping investigators cut the time spent on each alert while keeping a human decision-maker in control.

Risk, stress testing, and regulatory reporting

The platform supports scenario analysis and stress testing such as CCAR by retrieving requirements, building baseline and adverse scenarios, and running them against the bank’s own data and calculation engines, with results made available to leadership. Industry models keep capital, liquidity, and credit-risk reporting consistent across frameworks such as Basel and IFRS.

Corporate banking, cash flow forecasting, and supply chain finance

In corporate banking, agents consolidate balances and invoice data to forecast cash flow, then coordinate across banks and geographies using agent-to-agent protocols to arrange credit lines and onboard suppliers into supply chain finance programes.

Hyper-personalised customer service

Experience agents anticipate evolving customer needs and deliver tailored service across online, mobile, and branch channels, supported by predictive analytics for needs such as anticipating customer churn, identifying potential fraud, predicting credit defaults, and optimizing cash flow forecasting.

In addition to these defined use cases, the platform supports open-ended analysis. Teams can pose questions in natural language and let AI agents propose analyses for review and act within user-defined guardrails, with full visibility into how the data was used.

Oracle AIDP for Life Sciences: Use Cases and Faster Time to Value

Is Oracle AI Data Platform for Financial Services right for your organization?

There are three practical tests for whether AIDP for Financial Services belongs on your roadmap now.

The first is whether you can point to a concrete problem with a number attached to it, such as a regulatory reporting cycle you need to shorten, fraud losses you want to bring down, or a credit decision you need to make faster.

The second is whether you have a working picture of your own data, meaning you know what you hold, where it sits, and how well it is governed. When one is absent, the more valuable starting point is getting the data foundation in order before any technology is bought.

The third test that I think gets overlooked, and it comes from what we learned scaling the UCD programme. That project started with one engaged champion who used the system daily, someone who understood both the clinical and the operational context well enough to validate the output against their own real-world judgment. Over time, that one person’s credibility drove the programme from a pilot towards multi-year expansion. The same pattern applies in financial services, because adoption scales from a single credible internal user, but the trust-building looks different. In banking or insurance, that champion has to bring compliance and risk stakeholders into the room from the outset, because their sign-off is part of what “trusted” means in a regulated financial environment. So if you’re evaluating AIDP for Financial Services, it’s worth asking yourself: do you have that person? Someone senior enough to sponsor the use case and close enough to the operational detail to know whether the output is right? If you do, you probably have your starting point

To read more on these readiness signals, why data governance is so often the sticking point, and how we assess it, check out our companion piece: Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not).

What to Do Next

To explore whether AIDP for Financial Services fits your organization, take a look at our Oracle AI Data Platforms Services, or Contact Us.

Frequently Asked Questions

What is Oracle's agentic platform for banking?

UCD AI-Driven Regulatory Guidance

colmmccarthy — Fri, 12 Jun 2026 15:23:18 +0000

Is Critical Regulatory Knowledge Slowing Down Clinical Trial Decisions?

Give teams instant access to trusted guidance and identify compliance risks before they become obstacles.

Regulatory requirements are vital for clinical trial success, but important information is often hidden in lengthy documents or held by a few specialists. Our Trial Regulations AI Agent, built on Oracle AI Data Platform, simplifies access to regulatory knowledge. It enables clinical teams to ask questions in natural language and receive immediate, context-aware guidance, reducing reliance on experts, speeding up regulation interpretation, identifying compliance risks, and ensuring consistent decision-making across clinical operations.

The post UCD AI-Driven Regulatory Guidance appeared first on Vertice.

Oracle AIDP Adoption: The Teams, Roles and Skills That Make It Work

colmmccarthy — Thu, 04 Jun 2026 13:02:09 +0000

Author: John Fitts

TLDR

Successful Oracle AIDP adoption depends as much on how your organization works as on the platform itself. The question of whether the business or IT should own the platform has a simple answer: both, with the two sides moving closer together rather than handing work back and forth across a gap. In practice, that means business users working with data directly, asking questions in natural language and getting real-time visibility into things they used to wait weeks for, while IT shifts from gatekeeper to enabler with governance and security staying firmly in place. From a skills perspective, most organizations don’t need to hire a new team to get there, because the right person for the job is usually already in the building and just needs upskilling. This guide is drawn from Vertice’s delivery experience, including our work with University College Dublin Clinical Research Centre.

What Does Successful Oracle AIDP Adoption Involve?

Oracle AI Data Platforms (AIDPs) is Oracle’s unified environment for building, governing, and operationalizing AI and data workloads on enterprise data. We’ve covered the platform itself in our practitioner’s guide to Oracle AIDP, and we’ve written separately about how to assess Oracle AIDPs readiness before you invest.

This piece picks up where the readiness conversation ends. Once you’ve decided AIDP is the right move, the question becomes what actually has to change inside the business to get the value out of it. In our experience, the technology is the easy part. The organizations that get the most from AIDP are the ones that treat it as a change in how they work, and the ones that struggle are the ones that treat it as an installation.

Who Should Own Oracle AIDP: The Business or IT?

Short answer, both. But maybe not in the way people expect.

Rather than one side taking over or the other stepping back, the shift is about the two sides getting closer together in how they work. Traditionally, the business defines what it needs, hands it to IT, IT builds something and hands it back. There’s a gap in the middle where context gets lost, priorities drift, and the end result doesn’t quite land the way anyone intended.

AIDPs works best when that gap shrinks. When the business stays close enough to validate that what’s being built actually serves the outcome they’re after. And when IT understands the business context well enough to make the right technical decisions along the way.

That shift is cultural far more than it is technological. And it’s probably the hardest one to make because it means changing habits rather than tools. But the organizations we’ve seen get the most value from AIDP are the ones where business and technology are working through it together from the start, instead of handing things back and forth.

What New Habits Does a Business Need to Adopt to Get Value from Oracle AIDP?

The biggest one is probably the simplest to describe and the hardest to do. It’s treating data as a shared responsibility rather than something only IT handles.

When AIDP is working well, business users are interacting with data directly. Asking questions in natural language, getting real-time visibility into things they used to wait weeks to see reports on. That’s a meaningful shift in how people work day to day.

But it only works if the business side is willing to engage with it. That means actually getting hands-on with the platform rather than only consuming a report someone else built. And on the flip side, IT needs to shift from being the gatekeeper of data to being the enabler. The governance is still there, the security is still there. But the access model opens up.

We saw this play out in our work with University College Dublin Clinical Research Centre, where one of the goals was to give research teams direct access to trial data through conversational agents rather than waiting on manual reporting cycles. That changed how the team worked on a daily basis. They went from requesting information to having it at their fingertips.

If you work in clinical research, pharma, or healthcare and want to see how this applies in a regulated environment, we’ve written more in our guide to Oracle AIDP for life sciences.

What Roles or Skills Does a Business Need to Add for Oracle AIDP?

This is one of the most common questions we get, and the honest answer is it depends. But there are some patterns we keep seeing.

In most cases, organizations don’t need to go out and hire a whole new team. What they usually need is to upskill the people they already have. Business analysts who know the domain need to get comfortable working with AI-ready data. Data engineers need to understand the business context behind what they’re building, beyond just the technical requirements. And leadership needs enough fluency to ask the right questions and make informed decisions about where to invest.

Where we do see new responsibilities emerging is around data governance and AI operations. Someone needs to own the data quality, the cataloging, the ongoing management of what’s feeding the platform. That’s often an existing role that expands rather than a brand-new hire.

The role that probably changes the most is the analytics or BI team. They go from building static reports to managing a platform that enables self-service access across the organization. That’s a fundamentally different job, and it’s one that requires a different mindset as much as different skills.

The theme across all of it, and this probably won’t surprise anyone at this point, is that the most successful transitions happen when the business side and the technology side are learning and evolving together rather than in isolation.

Next Steps

If you’re thinking about how Oracle AIDP would change the way your teams work and you want an honest conversation about it, Contact Us.

If you’re earlier in the journey, our practitioner’s guide covers what Oracle AIDP is, including the architecture, the Workbench, and the use cases in depth. And if you’re weighing up whether to invest, our Oracle AIDP readiness guide walks through the two signals we look for and the most common gap.

Frequently Asked Questions

Do we need to hire data scientists to use Oracle AIDP?

Vertice Wins Two UKOUG Awards 2026

Conor Kehoe — Tue, 02 Jun 2026 14:08:30 +0000

We are proud to announce that Vertice was recognised with two awards at the 2026 UK Oracle User Group (UKOUG) Community Awards, held on 14 May at the De Vere Grand Connaught Rooms in London.

Vertice took home awards in two categories:

Platform Partner of the Year
Analytics Partner of the Year

What makes these awards particularly special is how the winners are determined. The UKOUG Partner of the Year Awards are decided exclusively by Oracle customers, a genuine vote of confidence from the people we work with day in, day out. To be recognised in two categories is a direct reflection of the work our team has delivered across the Oracle stack over the past twelve months, and we are grateful to every customer who took the time to back us.

The UKOUG Community Awards bring together the Oracle ecosystem across the UK each year to celebrate excellence in partners, projects, and individuals. Being recognised alongside so many outstanding organisations in this community is something we do not take for granted.

We also want to extend a warm congratulations to all this year’s award winners, in particular to Colm McMahon and the team at UCD Clinical Research Centre, who were recognised with the AI Trailblazer Project of the Year award. Vertice was proud to partner with UCD CRC on the AI agent work that underpinned that project, and it is brilliant to see that collaboration recognised on a national stage.

Thank you to UKOUG for continuing to shine a light on the partners and projects pushing the Oracle ecosystem forward, and thank you to our customers, our team, and everyone who voted for us. These awards belong to all of you.

Interested to know more?

We’d be more than happy to discuss your requirements or questions, please get in touch here.

connect@verticecloud.com

The post Vertice Wins Two UKOUG Awards 2026 appeared first on Vertice.

UCD Ontology-driven Trial Intelligence

Conor Kehoe — Fri, 29 May 2026 08:11:30 +0000

Are Your Clinical Trials Providing the Insights You Need, When You Need Them?

Use natural language access to live trial data to accelerate decisions and improve operational oversight.

Clinical trial teams manage large amounts of operational data, such as enrolment figures and site performance metrics, often requiring manual reporting from disconnected systems. Our Trial Navigator AI Agent, built on Oracle AI Data Platform, integrates these data sources, enabling research teams to query live trial information using natural language. This allows instant identification of delays, monitoring of enrolment, and a real-time view of trial performance, enhancing decision-making and operational efficiency.

The post UCD Ontology-driven Trial Intelligence appeared first on Vertice.

What is Oracle AIDP for Life Sciences? A Plain-English Guide for Pharma, MedTech and Clinical Research Leaders

Conor Kehoe — Tue, 19 May 2026 11:52:30 +0000

Author: John Fitts

The 30-second answer

Oracle AI Data Platform (AIDP) is a cloud-based platform that brings enterprise data, generative AI models, and AI agents together in a single, governed environment. When applied to life sciences, it provides a foundation for organizations across pharmaceuticals, medical devices, clinical research, and healthcare to unify their data and apply AI to research, clinical trials, post-market safety, and commercialization activities.

Oracle launched the general AI Data Platform in October 2025 and announced Oracle Life Sciences AI Data Platform in January 2026. The latter is pre-populated with more than 129 million de-identified longitudinal Oracle Health Real-World Data (OHRWD) records, alongside the customer’s own data and any third-party sources they choose to bring in. Throughout this article, “AIDP” refers to the underlying platform. Where the distinction matters, the Life Sciences variant is named directly.

Why does the Oracle AIDP for Life Sciences conversation matter now?

Oracle AIDP for Life Sciences is designed to help organizations accelerate drug development, run faster and smarter clinical trials, monitor post-market safety, and generate the evidence needed for commercial and regulatory decisions. The outcomes it targets are measurable: shorter time to insight, earlier identification of disease, deeper understanding of patient populations, and reduced operational cost.

Delivering against those outcomes depends on one thing first. Not the AI, but the data underneath it.

This was the central message from Oracle leadership at Oracle AI World 2025. Speaking on the impact of AI on healthcare and life sciences, Seema Verma, Executive Vice President and General Manager of Oracle Health and Life Sciences, identified data as the defining issue.

“One of the big problems that is driving those inefficiencies is data and the lack of data or too much data or understanding what the data means,” she said. “And so, this is like the perfect use case for AI.”

She went further on the relationship between data and AI: “It’s not enough to just be able to use AI. You have to be able to, first of all, bring all the data together, and you have to make sense of the data, and you have to make sure the data is clean so that the AI can work.”

The same logic applies across pharmaceuticals, medical devices, and clinical research. Until the underlying data is unified and governed, AI projects tend to stall before they reach production. That is the problem AIDP for Life Sciences is built to address.

What is Oracle AI Data Platform?

Oracle AI Data Platform combines four capabilities in a single environment:

A unified data foundation

AIDP provides a lakehouse architecture using open formats such as Delta Lake and Iceberg, designed to eliminate data duplication and bring structured and unstructured data into one place. A unified catalog provides governance across all data and AI assets.

Built-in generative AI and agentic capability

The platform includes integrated access to generative AI models, vector indexing, retrieval-augmented generation (RAG), and tools for building, deploying, and managing AI agents. Oracle’s Agent Flows allows business users to interact with multiple agents through a single interface, and the platform supports open standards including Agent2Agent (A2A) and Model Context Protocol (MCP).

Integration with the wider Oracle ecosystem

AIDP connects to Oracle Health applications and existing enterprise databases through Zero-ETL and Zero Copy capabilities. It also supports multicloud and hybrid environments, with the ability to process data from on-premises and edge sources.

Enterprise-grade security and governance

The platform is built on Oracle Cloud Infrastructure (OCI) with the security, compliance, and data governance controls required for regulated industries.

How does Oracle AIDP for Life Sciences build on Oracle AIDP

The Oracle Life Sciences AI Data Platform is a version of AIDP shaped for the data, workflows, and regulatory environment of life sciences organizations. Three things distinguish it from a generic implementation.

Pre-loaded real-world data

The platform provides access to Oracle Health Real-World Data, a dataset of more than 129 million de-identified longitudinal patient records sourced through the Oracle Learning Health Network. The data is delivered in both the Oracle Core Data Model and the Observational Medical Outcomes Partnership (OMOP) Common Data Model, with mapping to standard clinical ontologies such as ICD-10 and SNOMED. De-identification follows the Expert Determination methodology defined in the HIPAA Privacy Rule.

Industry-specific tooling

Users access the platform through Oracle Autonomous Data Warehouse for SQL-based analysis and cohort identification, Oracle Cloud Infrastructure Data Science for notebook-based modeling in Python, R, and Spark, and Oracle Analytics Cloud for visualization and reporting. OCI Object Storage supports unstructured and semi-structured data ingestion.

Service variants for different organization types

Oracle offers the platform in distinct service configurations, including a Pharma Cloud Service and a Research Institutes Cloud Service. Each is sized differently and provisioned with the OCI services appropriate to that use.

It is worth noting one specific limitation: the OHRWD Patient Dataset and the insights generated from it are not regulatory grade and are not approved for use in regulatory submissions. The platform supports the workflows around regulatory activity, but the dataset itself is intended for research, not filing.

What does this mean for Life Sciences?

Customers can start with pre-curated healthcare datasets, built-in AI tooling, existing healthcare ontologies and fully managed infrastructure. This means faster delivery of proof-of-concept timelines, analytics deployment, cohort analysis, and AI experimentation.

Because the data is centralized and standardized, organizations can reuse the same platform across many domains. Once the governed healthcare data foundation exists, new applications become composable, AI becomes reusable and experimentation becomes faster.

Oracle AIDP for Life Sciences use cases

Oracle’s announcement of the Life Sciences AI Data Platform identified a number of use cases the platform is designed to support. Each represents a distinct business outcome rather than a technical capability.

Label expansion

Organizations can use the platform to identify potential indications for existing therapies by analyzing real-world patient data alongside their own clinical evidence, supporting commercial and regulatory strategy for approved products.

Health Economics and Outcomes Research (HEOR)

The platform supports population-level analysis to evaluate the cost, effectiveness, and real-world impact of treatments, work that increasingly determines reimbursement decisions and market access.

Synthetic control arms

Using real-world data as a comparator, organizations can construct synthetic control arms for clinical trials, reducing the need for placebo cohorts in certain study designs and shortening trial timelines.

Post-market safety monitoring

AI agents can monitor disparate sources of safety data, surfacing signals earlier than manual processes typically allow.

Regulatory submission support

While the underlying patient dataset is not itself regulatory grade, the platform supports the analysis, evidence generation, and workflow management that contribute to submissions.

In addition to these defined use cases, Oracle’s platform supports open-ended hypothesis generation. A researcher can ask a question in natural language, and AI agents will clarify intent, propose analyses for review, and act within user-defined guardrails, with full visibility into data lineage.

Oracle AIDP for Life Sciences Case Study: UCD Clinical Research Centre

We built an AI-powered patient companion application for asthma and chronic obstructive pulmonary disease (COPD) on Oracle AI Data Platform, in partnership with University College Dublin Clinical Research Centre (UCD CRC), the largest clinical research centre on the island of Ireland. The application was built on the underlying Oracle AI Data Platform rather than the Oracle Life Sciences AI Data Platform variant, which was announced subsequently. UCD CRC handled the clinical study design and managed the research requirements. We handled the technical delivery, including the lakehouse, the AI-elements, and the application itself.

The application captures the patient’s experience in their own voice, in the moment it happens, and uses retrieval-augmented generation to correlate that narrative with clinical and environmental data. The pilot is live, the partnership is ongoing, and we are now working with UCD CRC to explore further use cases beyond respiratory disease using the same architecture.

Watch the full case study

Is Oracle AIDP for Life Sciences right for your organization?

Two signals tell you that AIDP for Life Sciences is worth a serious look. First, you can name a specific use case tied to a measurable business outcome, for example, whether that is shortening trial reporting cycles, or expanding the indications for an approved therapy. Second, you have a baseline understanding of what data you hold, where it lives, and some level of governance around it. If both are in place, the conversation is a productive one. If either is missing, the right first step is foundational data work, not platform procurement.

A fuller breakdown of the readiness signals, the most common gap (data governance), and the framework we use to assess it is covered in our companion piece: Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not).

What to Do Next

To explore whether AIDP for Life Sciences fits your organization. Contact Us.

Frequently Asked Questions

What is Oracle AI Data Platform?

Oracle AI Data Platform (AIDP) is a cloud-based platform that connects generative AI models with enterprise data, applications, and workflows. It is built on Oracle Cloud Infrastructure, Oracle Autonomous AI Database, and OCI Generative AI service. Oracle announced general availability in 2025.

What is Oracle Life Sciences AI Data Platform?

A generative AI-enabled solution announced in January 2026, designed for pharmaceutical, medical device, research, and life sciences organizations. It supports use cases across research and development, clinical trials, post-market safety, and commercialization.

Oracle AI Data Platform vs Oracle Life Sciences AI Data Platform

The Original AI Data Platform (AIDP Workbench) and Life Sciences AI Data Platform which we refer to as a “flavours” (FAIDP would be another flavour) differ mainly in prebuilt content. AIDP Workbench is a blank canvas where everything needs to be built from the ground up. The variants, like Life Sciences AI Data Platform, include prebuilt aspects aligned to a specific industry or line of business, such as Oracle applications, KPIs, and agents, enabling faster time-to-value.

What is Oracle Health Real-World Data?

A dataset of more than 129 million de-identified longitudinal patient records sourced through the Oracle Learning Health Network. It is delivered in the Oracle Core Data Model and the OMOP Common Data Model, with mapping to ICD-10 and SNOMED. De-identification follows the Expert Determination methodology defined in HIPAA Privacy Rule § 164.514(b)(1).

What types of patient data are included?

Demographics, vitals, test results, medications, and encounter specifics including diagnosis, symptoms, and provider details. Not all fields are completed for all patients.

Can the data be used for regulatory submissions?

No. The Patient Dataset and the insights derived from it are not regulatory grade and are not approved for use in regulatory submissions.

Can the platform process genetic data?

No. The Cloud Service cannot currently receive or process genetic data.

What use cases does the platform support?

Oracle has identified label expansion, population-level Health Economics and Outcomes Research, synthetic control arm generation, post-market safety monitoring, and regulatory submission support. The platform also supports open-ended hypothesis generation through AI agents operating within user-defined guardrails.

What tools does the platform include?

Oracle Autonomous Data Warehouse for SQL-based analysis, OCI Data Science for notebook-based modelling in Python, R, and Spark, Oracle Analytics Cloud for visualization, and OCI Object Storage for unstructured data.

What service variants are available?

Oracle offers a Pharma Cloud Service pre-populated with the Patient Dataset, and a Research Institutes Cloud Service available as a Hosted Named User service with optional Patient Dataset Records. Each is sized and provisioned differently.

How does AIDP integrate with other Oracle products?

It connects to Oracle Cloud Infrastructure, Oracle Life Sciences AI Application Suite, Oracle Fusion Cloud SCM and Sales, and Oracle Health AI Application Suite. The general AIDP also supports Zero-ETL and Zero Copy connections to Fusion, NetSuite, and other enterprise applications across multicloud and hybrid environments.

What is an example of AIDP being used in clinical research?

University College Dublin Clinical Research Centre and Vertice built an AI-powered application for patients with asthma and COPD using Oracle AI Data Platform. Oracle Health and Baylor College of Medicine have also announced a collaboration to advance research into alcohol-related liver disease using the platform and Oracle Health Real-World Data.

How long does it take to implement Oracle AIDP for Life Sciences?

Any implementation is dependent on the business, functional and technical requirements. However, the typical pilot is a 4-week sprint where we create outcome use cases. This is followed by 4-week project phases where we create a portfolio of production-ready AI use cases that can be scaled across the organization. You attain ROI via in-house AI Innovation Library of working use cases to then derive your budget internally to productionize.

How should an organization choose an AIDP implementation partner?

Look for live, in-production AIDP implementations, named customer references you can speak to, an active Oracle partnership, and vertical experience in your sector. The right partner takes you from a defined business problem to a working use case in weeks, not quarters. Vertice was the first to implement Oracle AIDP at scale and has proven delivery in life sciences and clinical research.

About John Fitts

John Fitts is a senior business strategist with 25 years translating between the boardroom and the technology stack. As US Vice President for Vertice and CEO of Fairfax Intel, he partners with C-suite leaders on the question most AI and data programs skip: not how, but why.

connect@verticecloud.com

John Fitts

Senior VP North America

The post What is Oracle AIDP for Life Sciences? A Plain-English Guide for Pharma, MedTech and Clinical Research Leaders appeared first on Vertice.

Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not)

Conor Kehoe — Tue, 19 May 2026 11:22:11 +0000

Author: John Fitts

TLDR

You’re ready for Oracle AIDP when you can name a specific use case tied to a business outcome, and you have a baseline level of data maturity sufficient to act on that use case. The most common readiness gap is data governance, and the Data8 framework is a structured way to assess and close it. The lowest-risk way to test the platform is Vertice’s Pilot, Prototype, Project approach, which lets you prove value on representative data before any infrastructure or procurement commitment. And if it turns out you’re not ready yet, foundational data work is the right next investment, not because AIDP isn’t right for you, but because it’ll work better when you eventually deploy it. This guide is drawn from Vertice’s delivery experience, including our work with University College Dublin Clinical Research Centre

What Does Oracle AIDP Readiness Actually Mean?

Oracle AI Data Platform (AIDP) is Oracle’s unified environment for building, governing, and operationalizing AI and data workloads on enterprise data. Oracle AIDP readiness is your business’s ability to successfully use that platform to deliver reliable, scalable, and business-relevant outcomes.

How do you know you are ready for Oracle AIDP?

There are two things we look for when we’re having this conversation with businesses.

A Clear Use Case Tied to a Business Outcome

The first is a clear use case tied to a business outcome. Not a general interest in AI, but something specific. We want to reduce reporting cycles. We want real-time visibility into trial performance. We want to detect fraud patterns faster. When someone can articulate that clearly, it tells us there’s a real problem to solve, and the investment has a purpose.

Basic Data Maturity

The second is a basic level of data maturity. And that doesn’t mean perfect data (nobody has perfect data). It means having a reasonable understanding of what data you have, where it lives, who is the data owner, as well as some level of governance around it.

What Are the Signs Organizations Are Not Ready for Oracle AIDP?

The clearest sign an organization is not ready for Oracle AIDP is when the conversation about AI is still in the abstract, with no specific use case attached. The language being used sounds like “we should probably do something with AI”, or “we know we need to do something with AI, and we’ve heard Oracle AIDP is the right platform,”. This typically signals that this decision is from external pressure rather than internal clarity.

That being said, there’s nothing wrong with being there, a lot of organizations are. But putting a platform in place before you know what you’re aiming to solve for tends to be where the expensive lessons happen. The value from Oracle AIDP is directly tied to a strong use case. A concrete goal like reducing trial reporting from three weeks to three days, or surfacing fraud patterns in real time, is measurable from day one. A vague goal has no benchmark for success and no clear point at which the platform has earned its cost.

We’d rather have these honest conversations up front. If someone’s ready, we’ll move fast. If they need to do some foundational work first, we’ll say that too. We’ve found that being straightforward about readiness early on builds a much stronger relationship than telling someone what they want to hear.

What's the Most Common Oracle AIDP Readiness Gap?

Data governance. Almost every time.

The instinct to skip past it is something I personally struggle with. I’m not naturally patient. If there’s a room to paint, I want to grab the roller and start covering walls because that’s where the progress feels visible. But I’ve learned the hard way that the real work is in the prep. Filling the nail holes, sanding, and taping off the edges. It feels like you’re not getting anywhere, but skipping it always shows up later. And fixing it later on takes twice as long as just doing it right the first time.

Data governance is the prep work. And part of the reason it gets skipped is that for a lot of organizations, it’s unfamiliar territory. If you haven’t been through it before, it can feel like this big, abstract thing that’s hard to get your arms around. Where do you even start?

That’s exactly why we built our Data8 framework. It gives organizations a practical way to look at their data maturity across eight key dimensions and figure out where the gaps are before they start spending on platforms. It makes governance feel less like a mountain and more like a checklist. Sometimes the answer is that AIDP is the right next step. Sometimes the answer is that there’s foundational work to do first. Either way, understanding where you stand saves a lot of time and money down the road.

Is there a Low-Risk Way to Test Oracle AIDP Before Committing?

Yes. We use a Pilot, Prototype, Project approach that lets you see results on representative data before any infrastructure or procurement commitment.

This is probably the question we enjoy answering the most, because it’s exactly how we like to work.

We use what we call a Pilot, Prototype, Project approach. We run the pilot on our own AIDP environment using synthetic or sample data, so the client isn’t standing up infrastructure or going through lengthy procurement before they’ve seen what’s possible.

In our work with University College Dublin Clinical Research Centre, we used synthetic patient data and sample datasets loaded into AIDP in a matter of days rather than the weeks or months the traditional path would have taken. The compliance and data access requirements were significantly simpler because we weren’t working with sensitive patient data at that stage. The team got to see real results before committing to anything further.

From there, if the value is clear, it moves into a prototype with more structure, and eventually into a full production project with live data and integration. But the starting point is always low risk and fast. We’d rather show someone what’s possible than spend three months talking about it.

What Should You Do If You're Not Ready for Oracle AIDP Yet?

If the readiness conversation suggests you’re not ready for AIDP yet, that’s not a setback. It’s information that saves you a significant amount of money and a significant amount of organizational pain.

The right next step in this scenario is foundational data work. That usually means some combination of cataloging what data you hold, establishing ownership and governance baselines for the datasets that matter most, and getting clarity on the specific business outcomes that would make a future AIDP investment worthwhile. The Data8 framework is the structured way we do this with clients, and the engagement is meaningfully shorter and less expensive than a full platform implementation.

The other thing worth saying is that the foundational work makes the eventual AIDP rollout faster, cheaper, and much more likely to succeed. Organizations that invest in the prep work tend to deploy AIDP in a fraction of the time it takes organizations that try to do both at once, because the platform doesn’t have to absorb the cost of unresolved data ownership and governance questions during deployment.

Next Steps

If you’re at the stage of thinking about Oracle AIDP and you want an honest readiness conversation, Contact Us.

If you’re still working out what Oracle AIDP is and how the platform is structured, our practitioner’s guide covers the architecture, the Workbench, and the use cases in depth. Our Guide: Oracle AI Data Platform

About John Fitts

connect@verticecloud.com

John Fitts

Senior VP North America

The post Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not) appeared first on Vertice.