LLM integration
Provider-agnostic model layer so you can switch or route without rewriting the application.
LLM applications
Generative AI Development
LLM-powered applications and copilots grounded in your content, with evaluation before launch.
What this is
Generative AI is useful when it is grounded, bounded and measured. We build applications where the model works from your approved content, returns structured output your systems can consume, and is scored against a fixed test set before anyone outside the team sees it.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Generic AI writing tools produce text that is fluent, generic and off-brand.
- The model invents details, and no one can tell which parts of an answer to trust.
- Output is free-form prose when the system downstream needs structured fields.
- Prompts live in scattered files with no versioning, testing or review.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- A generative application grounded in your content and rules
- Prompt library under version control with a documented change process
- Structured output schemas validated on every response
- Model evaluation comparing candidates on your tasks, with the data behind the choice
- Human review and approval workflow where output is customer-facing
- Cost and token monitoring per feature
Capabilities
What the system does
Enterprise AI applications
Generation embedded in the tools your teams already use, inside existing permissions.
AI copilots
In-product assistants that draft, summarise and act with the user in control of what ships.
Content and knowledge generation
Descriptions, summaries, reports and drafts produced from your structured data and documents.
Model selection and evaluation
Candidate models scored on your tasks for quality, latency and cost before commitment.
Guardrails
Input validation, output schemas, refusal handling and escalation when confidence is low.
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
Proposal and quote drafting
Assembles a first draft from your templates, pricing rules and the specific client context.
02
Product content at scale
Generates descriptions and metadata from specification data, consistently and on-brand.
03
Report summarisation
Turns long operational or financial data into a written summary in your house format.
04
Internal copilots
Drafts replies, tickets and documents inside existing tools, with a person approving before send.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
-
01
Content and data sources
Templates, brand guidelines, product data, documents and transactional records.
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02
Grounding layer
Retrieval of approved source material so generation works from fact, not memory.
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03
Prompt and model layer
Versioned prompts, structured output schemas, model routing and fallbacks.
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04
Review workflow
Draft, human review, approval and publish states for anything customer-facing.
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05
Evaluation and monitoring
Automated scoring, regression tests on prompt changes, cost and latency tracking.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Generation grounded in approved sources; the system declines rather than inventing when context is missing.
- Human approval required before customer-facing output is published, by default.
- Prompt changes versioned and regression-tested like any other code change.
- Sensitive fields redacted before they reach a model provider where required.
- Provider terms reviewed for training use and data retention before selection.
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
Generative AI Development — FAQs
The questions we are asked most often about this service.
How do you stop it inventing facts?
Grounding and evaluation. The model answers from retrieved approved content, output schemas constrain the shape of answers, and a fixed test set measures accuracy before launch. Where context is missing the system says so.
Can it match our brand voice?
Yes, from your existing material and style rules rather than an abstract description. We tune against examples you approve and review output with your team.
Which model should we use?
We benchmark candidates on your actual tasks and show you the quality, latency and cost trade-offs. The answer differs per feature, which is why we route per task.
What does it cost to run?
Driven by volume and model choice. We route each task to the cheapest model that passes your quality bar, cache repeated work and give you a per-feature cost dashboard.
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