Generative AI that earns its keep
Generative AI development that reads your business, not just the internet.
We design and ship production-grade LLM applications, including RAG copilots, AI agents and document intelligence, all grounded in your data, guarded by evaluation, and priced for scale.
For business leaders
Think of it as a sharp new colleague who has read every contract, SOP and ticket you have ever written, answers in seconds, and never leaks a secret. We find where AI saves real hours or real money, prove it in a pilot, and then scale what works.
- Answers grounded in your documents, with citations
- Measured accuracy before go-live, not after
- Predictable cost per conversation
- Data stays in your cloud or on-prem
For engineers & architects
Retrieval-augmented generation with hybrid search (BM25 + dense vectors), re-ranking, chunking tuned per document type, tool-calling agents with guardrails, offline and online evals (faithfulness, answer relevance, latency, cost per query), prompt and version management, PII redaction, and model-agnostic gateways across frontier and open-weight models.
- Claude
- GPT
- Llama
- Mistral
- LangGraph
- LlamaIndex
- pgvector
- Qdrant
- OpenSearch
- Python
- FastAPI
- Next.js
- Agentic AI
- Multi-agent orchestration
- RAG
- Vector search
- LLM evaluation
What we deliver
AI & LLM Engineering, end to end
- 01
RAG copilots & enterprise search
Chat with policies, manuals, contracts and knowledge bases, with source citations and access control.
- 02
AI agents & workflow automation
Agents that read, decide and act across your ERP, CRM, email and ticketing, with a human in the loop where it matters.
- 03
Document intelligence
Extract structured data from invoices, BOQs, purchase orders, medical and legal documents with confidence scoring.
- 04
LLM evaluation & LLMOps
Golden datasets, regression evals, tracing, prompt versioning and cost dashboards, so you can treat AI quality like software quality.
- 05
Fine-tuning & small models
Distil large-model behaviour into small, fast, cheap models for high-volume tasks and edge deployment.
- 06
AI strategy & readiness
Use-case discovery, ROI sizing, data readiness and a responsible-AI policy your board will sign off on.
Where it applies
Industries we bring this to
FAQ
Questions, answered
How do you stop an LLM from making things up?
We ground every answer in retrieved sources, require citations, score faithfulness with automated evals, and refuse to answer when confidence is low. Hallucination rate is a metric we report, not a surprise.
Will our data be used to train public models?
No. We use enterprise API tiers with zero data retention, or deploy open-weight models inside your own cloud or data centre when policy requires it.
How long does an AI pilot take?
A focused pilot on one high-value use case typically runs 4-6 weeks, from discovery to a measured, user-tested prototype.
Which LLM should we use?
It depends on accuracy, latency, cost and data-residency needs. We benchmark candidates on your own data and keep the architecture model-agnostic so you can switch later.
Have an idea, a process to fix, or a system to modernize?
Tell us where you are. We’ll reply within one business day with honest next steps, even if they don’t involve us.