Generative AI & LLMs
Generative AI that knows your business.
Assistants, copilots and agents grounded in your documents, systems and rules — designed for accuracy, privacy and real adoption.
Generative AI & LLM Applications
Large language models can draft, summarise, reason and act. Used naively they also hallucinate, leak data and produce inconsistent results.
We engineer generative AI applications with retrieval, guardrails, evaluation and access control built in, so they can be trusted with real work and real data.
Business challenges
What usually stands in the way.
Hallucinated answers
Generic chatbots invent facts when they lack context about your organisation.
Data privacy
Sensitive information must not leak to third parties or to the wrong users.
Inconsistent quality
Without evaluation, prompt changes silently break behaviour.
Cost and latency
Unoptimised LLM usage becomes slow and expensive at scale.
Our solution
How we solve it.
We combine retrieval-augmented generation, structured tool use and rigorous evaluation to build LLM systems that are accurate, auditable and cost-aware.
- Retrieval pipelines over documents, databases and APIs
- Agents that take actions through well-defined tools
- Guardrails, role-based access and audit logging
- Automated evaluation suites for quality regression
- Model routing and caching to control cost and latency
Technical capabilities
What we deliver.
Knowledge assistants
Chat and search across policies, manuals and internal knowledge.
AI agents
Multi-step agents that read, decide and act inside your systems.
Document intelligence
Extraction, summarisation and comparison of complex documents.
Content generation
On-brand drafting workflows with human review built in.
Conversational interfaces
Web, WhatsApp and voice assistants for customers and staff.
Private deployment
Self-hosted open models when data must stay on your infrastructure.
Development approach
Four stages. Clear outcomes.
- Step 1
Identify
Pinpoint high-value workflows where language is the bottleneck.
- Step 2
Ground
Connect the model to the right knowledge with permissions intact.
- Step 3
Evaluate
Build test sets that measure accuracy before anything ships.
- Step 4
Scale
Harden, monitor and optimise for cost as usage grows.
Start a conversation
Ready to put LLMs to real work?
Share the workflow you want to accelerate and we will outline a grounded, secure approach.