01
The task
Who will use it, what it may read, what it should produce and which decisions it may affect.
AI integration services
Build AI tools around trusted business information
Our AI integration services connect models to approved business information and existing systems, with sources, tests, permissions and human review built into the design.
Test the system before people rely on it
The model is only one part of the system. We also check the inputs, sources, tests, approvals, monitoring and rollback.
Checks before an AI system is releasedATC / system view
Approved inputPurpose + access
RetrieveKnown sources
ModelVersioned
EvaluateTest set
Human approvalPerson responsible
ReleaseMonitored
Failed evaluationReturn to the model before release
What we decide before development
02
The tests
Real examples, what counts as a correct answer, known failures, cost and response-time limits.
03
Human control
Who reviews, approves, overrides and stops the system.
04
Support and maintenance
Access, monitoring, incident handling, version changes and rollback.
Search business information and show the source
The system finds relevant approved sources before answering. It shows where the answer came from and says when it does not have enough information.
We define what the system may remember, who can see it, how long it is kept and how it can be corrected or deleted.
Examples
Common AI projects
01
Internal knowledge
Help staff find approved policies, procedures and product information with sources attached.
02
Document understanding
Extract and classify information, then send uncertain cases to a person.
03
Decision support
Prepare a recommendation with sources while the responsible person makes the decision.
04
Tasks across systems
Coordinate specific actions across tools with approvals, limits and a clear way to stop.
05
Quality checks
Check content or records against defined rules and send failures to the right owner.
06
Customer support
Answer from current product or service information and keep actions behind explicit permissions.
How we check the answers
01
Approved sources
Each file, system and data set has an owner, access rule and freshness expectation.
02
Citations
Users can check the sources behind an answer.
03
Who can access what
Search results respect each user's access instead of exposing all stored information.
04
Missing or conflicting information
Missing or conflicting information sends the question to a person instead of guessing.
A working system needs tests, owners and a fallback
Before
Demo
After
Working system
A few clean prompts
becomes
Versioned test set
One expected path
becomes
Known limits and exception paths
No owner when the output is wrong
becomes
Named approval and rollback owners
Next step
What should the AI help with?
We'll help you define what good looks like and how you will know when it fails.
Questions
Questions people ask us
Can AI answer questions using our own documents?
Yes, and that is most of what we build here. It answers from material you have nominated, respects who is allowed to see what, and cites the source so anyone can check it. It also says when it does not know.
Will our data be used to train someone else's model?
Not with the setups we build. Which model provider you use and what their data policy says matters here, and it differs by plan, so we check it against your obligations before choosing anything.
Can you build AI into the systems we already use?
Yes, and it is usually better than giving people another tab. Adding capability inside your existing CRM, portal or internal tools means it gets used, which is the main reason these projects succeed or quietly fail.
Are you tied to one AI provider?
No. We build so the model underneath can be swapped without rebuilding what sits on top, because this market changes every few months and being locked to one vendor is an expensive place to be.
How do you stop it making things up?
It answers only from your approved sources, cites where each answer came from, and is tuned to admit a gap instead of producing something plausible. A confident wrong answer is the expensive failure and it is the one we design against.