AI features that ship into production, not a demo.
We build retrieval-augmented generation, LLM-powered workflows, and evaluation pipelines integrated into existing business systems — with the same reliability bar as the rest of your stack.
Most "AI integration" stops at a demo
A chatbot prototype is not the hard part. Grounding model output in your actual data, evaluating accuracy before it reaches a customer, and operating the system reliably in production is where most AI initiatives stall.
We treat AI features as production software: version-controlled prompts, automated evaluation, and monitoring — not a one-off script wired to an API key.
What we deliver
Retrieval-augmented generation (RAG)
Grounding LLM output in your own documents, product catalog, or knowledge base, with citation back to source.
LLM integration into existing systems
Wiring model calls into your current application — support tooling, internal ops, customer-facing features — not a standalone add-on.
Evaluation pipelines
Automated scoring against a labelled test set before any prompt or model change ships, catching regressions before customers do.
Deployment and monitoring
Latency, cost, and output-quality monitoring in production, with fallback behaviour when the model underperforms.
Technology and approach
Model-agnostic architecture
We design integrations against an abstraction layer so switching model providers is a config change, not a rewrite.
Structured output, not free text
Where the workflow demands it, we constrain model output to a validated schema rather than parsing prose.
Human-in-the-loop where it matters
For high-stakes decisions, the model assists a reviewer rather than acting unsupervised.
Cost-aware by design
Caching, prompt optimisation, and model tiering to keep inference cost proportional to the value delivered.
Engagement models
Indicative pricing below — exact scope is confirmed after a discovery call.
AI feasibility sprint
A two-week scoping engagement to validate an AI feature against your real data before committing to a build.
$8k – $15k
Fixed-scope AI feature build
A defined AI capability — RAG search, an assistant, an automation — built and evaluated.
$30k – $120k
Dedicated AI engineer
Ongoing AI engineering capacity embedded in your team.
from $11k / month
Frequently asked questions
Ready to talk about ai & llm engineering?
Tell us what you're building — we'll respond within two business days.