01 / GROUND
The model answers from your data, not its memory.
Retrieval, permissions and freshness decided before a model sees a question — so answers cite something real and stay inside what the asker is allowed to see.
Large language models · integrated, not bolted on
LLM integration services for the systems you already run.
Enterprise LLM integration means more than an API key. We ground models in your own data, constrain what they can return, measure whether they are getting better, and keep the cost predictable — inside the applications you already depend on.
TRUSTED BY GROWING BUSINESSES
The practical layer
RAG, made operational.
No hand-wavy chatbot experiments. Every component exists to make an answer more useful, safer, or easier to improve.
02 / CONSTRAIN
Output you can act on without reading every word.
Schema-validated responses, refusal paths when the model is unsure, and failure that is loud rather than plausible.
03 / MEASURE
You can tell whether last week's change helped.
An evaluation set built from your real inputs, run on every prompt and model change, so quality is a number rather than an impression.
A disciplined route to value
Make the right thing
before scaling it.
A four-part engagement designed to replace uncertainty with proof, then turn proof into an operating system.
01↗
Find the task
Identify work where language is the bottleneck and the output can be checked.
02↗
Build the eval first
Assemble real inputs with known good outputs before choosing a model.
03↗
Integrate narrowly
Ship one task end to end inside your existing systems rather than a broad pilot.
04↗
Harden and hand over
Budgets, fallbacks, monitoring and a team that can operate it without us.
What we build
Modular services.
One accountable system.
Bring us in for a focused need or keep us close from the first prototype to the millionth query.
RAG and knowledge retrieval
Grounded answers over documents you already own, with permissions respected and every claim traceable to a source.
Extraction and classification
Turn unstructured documents, emails and forms into structured records your existing systems can consume.
Tool use and function calling
Let a model call your APIs safely — with explicit permissions, argument validation and a record of every call.
Evaluation and guardrails
The harness that tells you whether a change helped, plus the boundaries that keep a wrong answer from becoming a wrong action.
Model selection and migration
Choose against your tasks rather than a leaderboard, and keep the provider behind an interface so switching stays a config change.
Cost and latency engineering
Token budgets, caching, precomputation and routing to the cheapest model that clears the quality bar.
Model independence
Pick models for the job,
not for the pitch.
We keep an objective view of the stack: quality, latency, cost and control are all part of the conversation.
How we make choices
Accuracy on your actual tasksEVALUATE
Latency inside your budgetMEASURE
Cost at real input sizesMODEL
Where your data is allowed to goVERIFY
Open weights vs hosted APICOMPARE
Switching cost if you change laterPLAN
Where this work shows up
Document-heavy workflows,
where language is the bottleneck.
The clearest wins are in workflows where people re-key information between systems that will not talk to each other.
Surplus lines insurance is a good example. A submission arrives as an ACORD form — sometimes a clean XML payload, often a scanned PDF or a fax — and has to reach dozens of carriers that each interpret the same nominal standard differently. The work is extraction, normalisation and validation before anything is dispatched, and getting it wrong is worse than being slow.
We built that pipeline for a comparative rating platform serving around 500 MGA offices, integrating 40+ carriers whose systems ranged from modern REST to legacy SOAP to flat-file uploads. Read the case study ↗
The same shape recurs elsewhere: claims intake, supplier onboarding, contract review, support triage. Anywhere a person currently reads something and types it somewhere else, an LLM integration has a measurable job to do.
Language models are one part of a wider picture. If the question is broader than text — models, pipelines, real-time inference, legacy systems — start with our AI integration services ↗ and we will point you at the right piece. For workflows that need to act rather than answer, see AI agent development ↗.
Where it works
Built around the
work, not the trend.
We design for environments where context, accountability and speed all matter at the same time.
Insurance
Submission intake, document extraction and cross-carrier data normalisation.
Financial services
Grounded internal knowledge with an auditable trail on every answer.
Healthcare
Summarise and retrieve approved information without losing provenance.
B2B software
Embed language features into your product without rebuilding it.
Retail and commerce
Product data enrichment, support triage and catalogue classification.
Legal and compliance
Research and review that shows its sources rather than asserting.
Manufacturing
Make manuals, incident history and field notes searchable in context.
Professional services
Turn collective expertise into something a new joiner can query.
Common questions
Clarity before the kickoff.
Short answers to the questions we are usually asked before an engagement begins.
A practical first step
Bring us the question that has real stakes.
We'll help you frame the opportunity, identify the constraints and decide what a useful first proof should look like.
READY WHEN YOU ARE
Build with more certainty.
Tell us what your team needs to know, decide or deliver faster.