Start with the sources your first question needs and add more when there's a useful reason. It's built in your cloud, and you keep the code, definitions and documentation. We connect the data to reporting and agreed workflow actions, with Azure as a focus and other platforms scoped around your existing systems.
What’s included.
Three steps.
Frame
Agree the task, people, systems and acceptance criteria.
Build
Develop and review the scoped changes with your team.
Transfer
Verify realistic scenarios and hand over the working materials.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Build a dependable shared view
What you may be seeing
- Teams combine exports by hand every week.
- The same customer has different identifiers in different systems.
We connect the sources needed for your first useful business question, prepare consistent reporting data and document how the records relate. You keep the code and definitions in the agreed environment.
Implementation & verification
We connect the sources needed for your first useful business question, prepare consistent reporting data and document how the records relate. You keep the code and definitions in the agreed environment.
How we verify the work
- Map website, product, customer relationship and operational records with their access and retention requirements.
- Build connectors through APIs (controlled connections between applications), controlled interfaces between applications, or approved exports; test missing, duplicate and late records.
- Use versioned transformations, code that converts raw records into agreed reporting tables, with freshness checks and visible failures.
02Turn insight into a reviewed action
What you may be seeing
- Our dashboards identify a problem, but follow-up is still manual.
- Customer groups never reach the tools where the team works.
We connect agreed insights to a useful next step: a reviewed customer list, an operations task or a permitted update in an existing tool. Exceptions stay visible and consequential actions can require approval.
Implementation & verification
We connect agreed insights to a useful next step: a reviewed customer list, an operations task or a permitted update in an existing tool. Exceptions stay visible and consequential actions can require approval.
How we verify the work
- Start with a named business decision, owner and acceptance measure.
- Define audience rules, exclusions and permissions; test a small representative group before wider use.
- Prevent duplicate actions on retries, log what changed and provide a pause and recovery procedure.
03Ask business questions in plain English
What you may be seeing
- People cannot answer a simple question without waiting for a specialist query.
- We worry that an AI answer could use the wrong definition or expose the wrong records.
We build a question interface over an agreed dataset and business glossary. Answers show the underlying evidence and respect the access of the person asking.
Implementation & verification
We build a question interface over an agreed dataset and business glossary. Answers show the underlying evidence and respect the access of the person asking.
How we verify the work
- Define a semantic layer, shared business meanings such as customer, net revenue and active subscription, before adding an assistant.
- Restrict permitted datasets and operations; check example questions against known answers and access roles.
- Display the query basis, freshness and limitations. Route unsupported questions to an analyst rather than generating a confident guess.
Connect the answer to an action.
AI can explain approved data and prepare a next step. Workflow rules, permissions and review determine whether that step can proceed.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Trust
Reconciliation, event completeness and data freshness.
Decisions
Time to answer a defined operational or customer question.
Outcomes
Qualified conversions, retention and experiment uncertainty.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Can this support our existing team?
Yes. The engagement can combine a focused implementation with paired work and practical handover. We agree responsibilities, access and availability before starting.
What determines the scope and duration?
The systems involved, quality of existing evidence, required integrations and time your team can provide. The proposal states deliverables and acceptance criteria before work begins.