A defined use case
Identify the input, expected output and the points that need human review.
AI Integrations
Add useful AI to a defined workflow, with controls around it.
AI is useful when it has a clear task, relevant context and a review path. We integrate provider APIs into existing workflows for tasks such as drafting, classifying or retrieving information, with the limits made explicit.
What we deliver
A scope you can review.
The exact deliverables depend on the requirements. These are the foundations we discuss when planning the work.
Provider integration
Keep credentials server-side and connect the model through a replaceable service layer.
Fallbacks and evaluation
Handle unavailable providers and test output quality against the intended task.
Who this helps
Start with your situation.
A useful solution responds to a specific problem, scale and way of working.
Content workflows
Drafting and adapting source-grounded material with review before publication.
Internal assistance
Interfaces that help staff work with approved business information.
Workflow triage
Classification or extraction that can be checked and corrected.
From requirements to release
Know what happens next.
We clarify requirements, agree the scope and review the build in stages. Testing and deployment are part of delivery, with ongoing maintenance agreed separately.
Planning the build
Put AI inside a useful process, with a clear review path.
An AI integration needs a narrower objective than “make the system intelligent.” We define the task, the context it may use and the way an acceptable result will be judged. Examples include drafting from approved product information, extracting fields from a document or helping a team retrieve relevant internal material.
The application should make the output reviewable and provide a fallback when the provider is unavailable. For a content workflow, that can mean showing a draft for approval before publication. For data extraction, it can mean asking a person to verify uncertain values before they affect an important record.
We keep the provider connection behind a service layer so your entire product is not tied unnecessarily to one model. Usage costs, data handling and access to business information are part of the integration discussion. AI does not replace the need for reliable application permissions or useful source material.
Implementation priorities
The details that make this service useful.
These decisions turn a broad capability into a project your team can review and operate.
Grounded inputs
Use information you supply or authorize, with clear rules about the facts a generated answer or draft may include.
Reviewable outputs
Show drafts, extracted fields or recommendations in a form that can be checked and corrected before the next action.
Provider boundaries
Keep API keys private, handle service failures and agree what happens when the optional AI step cannot run.
Common questions
Before you start.
Answers to the questions that shape the scope.
Will AI publish or act automatically?
That depends on the agreed workflow. We recommend a review path for newly generated material or consequential actions. Automatic execution should be a deliberate scoped decision with suitable controls.
Can we choose the AI provider?
Yes. We can review providers against the task, access requirements and budget. A consumer subscription is not assumed to include the API access needed by an application.
Can the application work without AI?
Where practical, the core workflow should continue without the optional generation or classification step. We define the fallback as part of the scope rather than leaving the interface stuck on an unavailable provider.
Let’s make it work
What would you like to build?
Tell us what is getting in the way, what you need and where you want to go. We’ll help turn that into a practical scope.