Data and AI consultancy

Data, decoded.

We put your data in order, build analytics and AI on it, and run it with the controls your auditors expect.

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The problem we solve

Most AI projects stall on the data underneath.

Data sits in systems that do not connect, departments define things differently, and nobody owns quality. Models built on that do not hold up.

So we fix the data first, build analytics and AI on it, and put the controls in place to run it.

Services

What we do.

Six services to plan, build, and run data and AI. Start with one and add others as you need them.

Strategy

Decide what to build, in what order, and why.

A data and AI plan that finance can fund, engineers can build, and leadership can hold to account.

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Modern Data

Build the data platform your analytics and AI depend on.

Data platforms, pipelines, and master data management that give everyone one reliable version of the core data.

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Analytics

Reporting and self-service analytics people actually use.

Consistent metrics, dashboards people open, and self-service that does not sprawl.

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AI & ML

Machine learning that runs reliably in production.

Forecasting, prediction, and anomaly detection, with the engineering to keep models accurate after launch.

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GenAI & Agents

Generative AI and agents built on your data, with controls.

Assistants, copilots, and agents connected to your systems, tested before launch, and limited to what they are allowed to do.

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Governance & Operations

Controls and operations for data and AI you can defend.

Data governance, AI governance, security, and day-to-day operations for your data and AI platforms.

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Partners

Partnerships that mean faster, lower-risk delivery.

How we work

Every engagement runs on GIST.

Ground, Iterate, Ship, Trust. Four steps we follow on every project.

GISTTHE DECISIONat the centerHow GIST works

  1. We agree on the decision before we build.

    A one-page Decision Brief names the decision, its owner, and how we will measure the result.

  2. We build in short cycles with AI agents on the team.

    Each cycle delivers a working slice on real data. Our people review every change the agents make.

  3. We deploy to production from the first cycle.

    Behind feature flags or in shadow mode, with monitoring and rollback ready.

  4. We build controls as we go.

    Every model and agent has an owner, an evaluation record, and defined permissions, so audits are answered from the record.

Insights

What we have learned.

All insights
Guide

Modernizing Informatica PowerCenter: what to settle before you convert a mapping

Most PowerCenter programs start by converting mappings. Here is what to settle first, covering what really runs, where the logic lives, how to prove the new pipelines match, and how you will operate them.

· 6 min read
Article

Building an AI assistant that respects document permissions

Connect a model to SharePoint or Confluence and it can answer from files the person asking was never allowed to open. Here is how to carry permissions through to every answer.

· 3 min read
Guide

Check the data before you fund the project

Data and AI projects are often approved on a slide and discover the data problems weeks into the build. A short readiness check on the real data behind one decision avoids that.

· 4 min read

Talk to us about your data and AI plans.

Tell us what you want to achieve. We will tell you what it would take and whether we are the right fit.

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