Use-case scoping
A bounded problem with defined inputs, outputs and failure behaviour before engineering starts.
Custom AI engineering
Artificial Intelligence Development
Custom AI applications built around your data, your systems and a measurable quality bar.
What this is
This is end-to-end AI application engineering: the data pipeline, the model layer, the integrations, the interface your users touch and the evaluation harness that proves it works. We build on your infrastructure, in your repositories, with code you own.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Off-the-shelf AI tools cannot reach your data or follow your business rules.
- A prototype works in a notebook but nobody can deploy or maintain it.
- AI features are bolted on as a separate product instead of fitting the existing system.
- There is no way to tell whether the AI output is good enough to rely on.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- A working AI application deployed in your environment
- Data pipeline covering ingestion, validation and transformation
- Model layer with routing, retry handling and fallback behaviour
- Integration into your existing authentication, permissions and systems
- Evaluation harness with a fixed test set and an agreed quality bar
- Observability: cost, latency, error rate and output quality tracking
- Documentation, runbook and a handover session for your team
Capabilities
What the system does
Data engineering
Ingestion, cleaning, validation and storage designed for the access pattern the application needs.
Model selection and routing
Each task routed to the cheapest model that clears its quality bar, with structured outputs where correctness matters.
Application engineering
The full stack: APIs, business logic, interface, background jobs and queues.
Evaluation
Automated scoring against a fixed set, run on every change, so regressions surface before release.
Deployment and operations
Containerised deployment, CI/CD, monitoring, alerting and cost controls.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Document understanding
Extract structured data from contracts, invoices or forms and post it into the system of record.
02
Intelligent triage
Classify and route incoming work by content, urgency and ownership rules.
03
Content generation at scale
Produce product descriptions, summaries or reports from structured source data, with review gates.
04
Decision support
Surface the relevant context and a recommended action, leaving the decision with a person.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Data sources
Databases, APIs, file stores, event streams and document repositories.
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02
Ingestion and processing
Validation, normalisation, enrichment and storage with lineage retained.
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03
Model layer
Prompt and model management, structured output schemas, routing and fallback chains.
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04
Application layer
Business logic, permissions, queues, interface and API surface.
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05
Integration layer
Writes back into your CRM, ERP, helpdesk or database through supported interfaces.
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06
Operations
Evaluation runs, tracing, cost dashboards, alerting and rollback paths.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Data boundary agreed up front: provider APIs with no-training terms, or deployment inside your own cloud.
- Existing authentication and authorisation reused rather than replaced.
- Input validation and output schema enforcement on every model call.
- Audit logging of inputs, outputs and actions for traceability.
- Dependency and model version pinning so behaviour does not drift silently.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
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Questions
Artificial Intelligence Development — FAQs
The questions we are asked most often about this service.
Do we own the code?
Yes. All code, prompts, configuration, evaluation sets and documentation are yours. We hand over repositories and infrastructure at the end of the engagement.
Can you work in our existing codebase?
Yes, and we prefer it. Following your conventions, review process and deployment pipeline is cheaper to maintain than a separate system.
Which models do you use?
Whichever fits the task and your constraints. We benchmark candidates against your evaluation set and route per task rather than standardising on one model.
What happens after launch?
You can take it in-house with the runbook and handover, or keep us on a support retainer for monitoring, tuning and cost control.
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