Generative AI applications grounded in your own knowledge.
We build assistants, copilots, and document intelligence on top of your content and systems, with retrieval, evaluation, and guardrails that make the output safe to rely on.
A general purpose chatbot rarely helps an enterprise. What helps is a system that answers from your policies, contracts, tickets, product data, and history, and that admits when it does not know.
We design retrieval augmented generation pipelines, evaluate them against a real question set from your business, and keep the model layer swappable so you are never locked to one provider or price point.
Every build ships with cost controls, prompt and response logging, red team testing, and a human review path for the decisions that need one.
What we deliver
Generative AI and LLM Applications capabilities.
Enterprise assistants and copilots
Assistants embedded in the tools your teams already use, answering from approved internal sources.
Retrieval augmented generation
Chunking, embedding, vector search, and reranking tuned to your content so answers cite real sources.
Document intelligence
Extraction, classification, and summarisation for contracts, invoices, claims, and technical documentation.
Evaluation and guardrails
Golden question sets, automated scoring, hallucination checks, and policy filters before anything reaches users.
Model selection and cost control
Right sized models per task, caching, and routing so quality goes up while token spend stays predictable.
Conversation and voice interfaces
Chat, search, and voice front ends designed for real workflows rather than demos.
How we work
A short path from idea to production value.
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01
Frame the task
Define the questions to answer, the sources of truth, and what a good answer looks like.
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02
Build the pipeline
Ingestion, retrieval, prompting, and integration into your application or workspace.
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03
Evaluate
Score accuracy, grounding, latency, and cost against a question set drawn from your business.
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04
Harden and ship
Guardrails, monitoring, feedback capture, and rollout with a human in the loop where it matters.
Business outcomes
What changes for your business.
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Answers with sources
Every response can be traced back to the document or record it came from.
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Hours returned to teams
Search, summarisation, and drafting work that used to take an afternoon now takes minutes.
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Predictable running cost
Model routing and caching keep spend flat as usage grows.
FAQ
Questions we are asked most.
Will our data be used to train public models?
No. We deploy on enterprise endpoints with training disabled, and can run inside your own cloud tenancy or on open models you host.
How do you stop hallucinations?
Grounding on retrieved sources, strict prompting, citation display, automated evaluation on every release, and refusal behaviour when confidence is low.
Can it work with our internal systems?
Yes. We integrate with SharePoint, Salesforce, ERP, ticketing, and databases through their APIs, and respect existing user permissions.
What does a first version cost to run?
Pilots typically run at a modest monthly inference cost. We model it before build so there are no surprises when usage scales.
How the work runs
A delivery rhythm you can plan around.
Discover
Workshops to map processes, systems and the outcome you are measured on.
Design
Solution blueprint, data model and delivery plan agreed before we build.
Build
Two week sprints with working software and a demo at the end of each one.
Launch
Migration, UAT, enablement and a cutover plan your teams can rehearse.
Support
Managed support, enhancements and quarterly roadmap reviews.
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ExploreReady to put AI to work on a real business problem?
Bring us one process, one dataset, or one question. We will tell you honestly whether AI is the right answer and what it would take to build.