Data & AI · AI solutions
AI applications grounded in your data and tested before release
We design and build AI applications that answer from your governed data rather than from the model alone. Retrieval, prompts and model choice are tested against an evaluation set before each release, and monitoring shows when answers drift.
The problem
Where this usually breaks down
Prototypes that never ship
A prototype answers well in a demo, but nobody can say how often it is wrong. It never clears security, legal or operational review, so it stays a prototype.
Answers without a source
The model answers from its training rather than your documents, or quotes passages the user was not allowed to read. Either failure ends trust in the tool quickly.
No signal after launch
Once live, nobody tracks answer quality, cost or failure cases. Source documents change, prompts are edited by hand, and quality slips unnoticed until people stop using the tool.
What we do
What we set up, and how each part works
We work in your repositories and through your change process, so everything we set up can be reviewed, run and changed by your engineers after we leave.
Use cases framed with success criteria
We start from a task someone does today, such as answering policy questions or drafting a case summary, and agree what a good answer looks like, what a harmful one looks like and who judges.
Retrieval over governed, permissioned data
Documents are chunked, embedded and indexed in Azure AI Search or a comparable vector store, with source metadata and access labels kept. A query returns only passages the user may read, and every answer cites its sources.
Model choice by task
We compare hosted models in Microsoft Foundry, Gemini Enterprise Agent Platform or Amazon Bedrock against your evaluation set on quality, latency and cost. A smaller model with good retrieval is often enough.
Evaluation sets that gate each release
An evaluation set of real questions with expected answers and sources scores correctness, grounding and appropriate refusals. Any change to prompts, retrieval or model runs against it in CI, and a regression blocks the release.
Guardrails for content and data
Input and output checks handle prompt injection, personal data and off-topic requests. Prompts and responses are logged for review under your retention rules, and a person stays in the loop wherever a decision affects a customer.
Monitoring that feeds back to evaluation
In production we track answer quality signals, user feedback, retrieval misses, cost and latency. Failing cases are reviewed and added to the evaluation set, so the same failure is caught before the next release.
What you keep
What stays with your team when we finish
Everything is committed to your repositories or written into your documentation, under your access controls. Nothing depends on us to keep running.
Application and indexing code
The ingestion, indexing, retrieval and application code in your repositories, with the CI that runs the evaluation on every change.
The evaluation set and scoring
Your curated questions, expected answers and scoring rules, owned by the subject experts who judge quality and extended as new cases appear.
A model and prompt register
Which model and prompt version each release uses, the evaluation result that approved it, and how to roll it back.
Monitoring dashboards and runbooks
Dashboards for quality, cost and failures, and runbooks for reviewing flagged answers, reindexing sources and handling an incident.
How we work
How an engagement runs, phase by phase
Each phase ends with something your team reviews and signs off before the next one starts. We work inside your change control, not around it.
01
Frame one use case
We pick one task with a clear owner and real users, agree what good and harmful answers look like, and confirm that the data it needs is governed and permissioned.
02
Write the evaluation set first
Before any prompt is tuned, your subject experts write questions with expected answers and sources. That set becomes the test every later change must pass.
03
Build against the evaluation
We build retrieval, prompts and the application together, score each change against the evaluation set, and review the results with your subject experts and security team.
04
Release, monitor and hand over
The application goes to a small group of users first, with monitoring in place. Your team owns the code, the evaluation set and the monitoring, and leads the next use case with us reviewing.
Technology
The tools this work runs on
Each group says whether a tool appears in our published work, so you can tell what we have shown from what we offer.
Offered, not in published work
- Microsoft Foundry
- Azure AI Search
- Gemini Enterprise Agent Platform
- Amazon Bedrock
- Amazon SageMaker AI
- MLflow
- Microsoft Purview
- Microsoft Entra ID
Questions
What teams ask before we start
Will our data be used to train the vendor’s models?
We settle that before any data moves. The enterprise terms of Microsoft Foundry, Gemini Enterprise Agent Platform and Amazon Bedrock set out how prompts and data are handled, and we review them with your legal and security teams for your region and contract.
How do you know the answers are good enough?
Your subject experts decide, through the evaluation set. It scores whether answers are correct, grounded in cited sources and refused when they should be. Release criteria are agreed up front, and nothing is released until it meets them on that set.
Do we need to fine-tune a model?
Rarely at the start. Good retrieval, clear prompts and the right hosted model cover most question-answering and drafting tasks. Fine-tuning is worth considering when the evaluation set shows a consistent gap that retrieval cannot close, and it adds a model you must maintain.
What happens when the application gives a wrong answer?
Every answer cites its sources so users can check it, and users can flag a bad answer. Flagged cases are reviewed, the cause is fixed in retrieval, prompts or data, and the case joins the evaluation set.
Related services
What teams often need alongside this
Data platform architecture
A lakehouse or warehouse organised into landing, curated and serving zones, with data contracts, access policy and lineage your team owns.
Data engineering and pipelines
Contracted ingestion, tested transformations in dbt or Apache Spark, and lineage at every stage, run from version control through one deployment path.
Security gates inside the pipeline
SCA, SAST, image scanning, DAST and a penetration test placed as pipeline gates, each result owned by development, QA or security.
Next step
Plan your first AI use case
Tell us the task you have in mind, who does it today and which documents or data it relies on. We will come back with how we would evaluate it.