Intelligence.
With a purpose.1
Less searching. More context. A person still in control.
01 / The opportunity
A specific task. A reason to use AI.
Start with the work that could be easier. We test whether AI helps, connect it to the right context and design the experience around what a person needs to judge.
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01
Find the answer
InApproved knowledgeOutAn answer with its source
Search approved knowledge and keep the source close enough to check.
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02
Structure information
InDocumentsOutFields to inspect and correct
Extract or classify documents into an output someone can inspect and correct.
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03
Assist the workflow
InA requestOutA draft, for review
Draft, summarise or suggest a next step, with a clear boundary for human review.
02 / A closer look
Follow the answer back to its source.
An answer is more useful when its basis is visible. Point at a claim, tap it or tab to it, and follow it to the passage that supports it.
QuestionWhat happens before a new supplier can start work?
Draft answer
StatusA draft for review. A person checks the sources before anything happens.
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Supplier onboarding policy§2 · Approval
New suppliers are requested through the purchasing form. The budget owner approves each request before any work starts or an order is placed. Requests above the agreed limit also need finance approval.
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New supplier checklistStep 3 · Finance
Once the request is approved, the supplier record is created. Before the first payment, finance verifies the supplier’s bank details and tax form. Payments are only released to verified accounts.
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Third-party security guideline§4 · Data
A supplier that will process customer data completes a security review before it is given access. The review covers where data is stored and who can reach it.
03 / How it takes shape
Evidence before assumptions.
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01 · Task / Evaluation
Define what useful means.
Choose a narrow task and representative examples. Set a baseline so the prototype has something real to improve on.
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02 · Sources / Integration
Connect the right context.
Choose models and retrieval around the data, permissions, latency and cost of the actual use case.
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03 · Review / Operation
Design for uncertainty.
Test missing context, unsupported claims and exceptions. Put review points where the result could have consequences.
04 / Beyond the visible
Useful output. Visible boundaries.
- 01
Grounding
Approved sources and a traceable route back to the material.
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Evaluation
Representative examples, failure cases and a clear baseline.
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Control
Review points, access rules and defined action boundaries.
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Operation
A plan for model changes, cost, latency and ongoing checks.
05 / A useful starting point
A task. A clear boundary.
Tell us about it- 01 The task
What happens today, how often, and what a better result would look like.
- 02 The examples
Representative input and output, including the difficult cases.
- 03 The review
Who checks the result and which actions need explicit approval.
Before we begin
Good questions. Clear answers.
Can AI work inside our existing product?
Yes. We can integrate with your authentication, permissions and data model, so the feature fits the product rather than becoming a separate tool to manage.
How do you choose the model?
We compare options against the task, evaluation results, response time, running cost and data policy.
The model is one part of the system, and the choice should be easy to revisit.
How do you handle incorrect answers?
We ground responses in relevant information, validate structured outputs and define fallback or human review paths.
Evaluation tracks the remaining errors; no model can be assumed to be correct every time.
Can we test the idea before a full build?
That is where we start. A focused prototype uses representative data and an agreed evaluation set, so there is evidence for the next decision.
One connected practice
The other possibilities.
Start a project