Classification inside a workflow
Categorise incoming requests into an agreed structure, with a fallback for ambiguous cases and validation before any downstream action.
AI, connected.
To the way you already work.
Add a useful AI capability to an existing product or business process. We connect model APIs, approved information and your software, with the checks, permissions and fallback behaviour a working system needs.
Discuss an AI integration- Fit your existing software
- Validate model outputs
- Plan for costs and failures
A good result in a chat window still needs a route into the business.
Your team may already use AI to summarise a request or draft a response, then copy the result between tools. Integrating that step can reduce handling, but only if the surrounding system knows what to send, what to accept and what to do when the answer is unusable.
We focus on the boundary between the AI capability and your existing application. That includes authentication, structured inputs and outputs, validation, usage limits, monitoring and the review step. Ordinary record synchronisation belongs in systems integration; this service adds model behaviour and the evaluation it requires.
Put intelligence at the point where it is useful.
Drafting inside your software
Prepare a response or summary using the relevant permitted records, then present it to the user in the application where they already work.
Knowledge retrieval in a product
Add a scoped search or question-answering capability using approved sources and the application’s existing user permissions.
Structured extraction APIs
Turn an agreed input into a validated object that another system can consume, rejecting incomplete or unexpected outputs.
A support request becomes a reviewed draft.
The starting point
An adviser copies a message and product notes into a separate AI tool, then copies the draft back into the support system.
A better way forward
The support interface prepares the permitted context, requests a draft and displays the source notes beside it. The adviser edits and approves the response in one place.
- 1
Prepare context
Send only the approved information required for the task.
- 2
Validate the result
Check the response format and route unsupported outputs for review.
- 3
Complete the handover
Display or store the draft with its status, source references and reviewer controls.
An illustrative engagement. We agree the actual scope, access and success measures with your business.
Something useful.
Yours to put to work.
The engagement has a clear scope and a tangible handover. We agree the deliverables before work starts.
Discuss the scopeAn integration design
Data flows, identities, model-provider considerations and the supported input and output contract.
A tested AI capability
The agreed endpoint or application feature, with validation and human review where needed.
Failure and cost controls
Timeouts, bounded retries, usage limits and a fallback experience for unavailable services.
A maintenance handover
Configuration guidance, evaluation cases and a process for testing changes to prompts, models or connected systems.
How we work with you.
Inspect the interface
Check the existing software, access methods and constraints before choosing an approach.
Define the contract
Specify permitted context, expected output and the fallback when it is not usable.
Build and evaluate
Test model quality alongside authentication, retries, limits and downstream behaviour.
Release with visibility
Agree monitoring, ownership and how changes are evaluated after launch.
A few things
you might be wondering.
Questions about your own setup?
Let’s talk it through
What are AI integration services?
They connect an AI capability to an existing system or workflow. The work includes the API connection, data preparation, access controls, output validation, evaluation and the user experience around the result.
How is this different from systems integration?
Systems integration connects software and moves records between tools. AI integration adds a model-based step, such as classification or drafting, which also needs quality evaluation and a plan for uncertain output. A project may need both.
Can you add AI without replacing our software?
Often, if the software provides suitable APIs, extension points or supported imports. We check those capabilities before recommending a design. Where access is limited, we explain the trade-offs rather than promising an unsupported connection.
Which model provider will you use?
We choose against the task, evaluation results, data handling requirements, latency and expected cost. The provider and account arrangements are agreed during scoping; no single model is assumed to be the right choice for every application.
What happens when the model API is unavailable?
The application needs a defined fallback, such as a manual queue or an unavailable state that preserves the user’s work. We agree retry limits and prevent repeated attempts from causing duplicate actions.
Can our developers maintain the integration?
We can scope documentation and handover for your team, including configuration, expected data structures and evaluation cases. Ownership, access to source code and ongoing support arrangements are made explicit in the proposal.
A little clarity before the next step.
Make AI part of the work, not another place to copy it.
Tell us what you’re working on. We’ll help you find a practical next step.
Discuss an AI integration