Generative AI, grounded in your data.
Foundation models are powerful and confidently wrong. The engineering that matters is grounding them in your data, constraining them with your rules and evaluating them like any other system. We build generative applications — assistants, copilots and search — that are accurate, safe and measurable.
Fluent is not the same as correct.
A language model predicts plausible text — not truth. Left ungrounded, it will answer your customers confidently and wrongly. The engineering that makes generative AI safe for the enterprise is grounding: retrieving the right context, citing sources, and constraining what the model is allowed to say.
Retrieval-Augmented Generation, end to end.
RAG is the backbone of enterprise generative AI. Each step is an engineering decision that affects accuracy, cost and latency.
Ingest
Pull content from your documents, systems and databases.
Chunk
Split content into retrievable units — the quality of chunks decides the quality of answers.
Embed & index
Turn chunks into vectors and store them in a vector database.
Retrieve & rerank
Find the most relevant context for the question, then reorder for precision.
Generate & cite
The model answers using only the retrieved context, with citations.
Evaluate
Score groundedness and relevance, offline and in production.
Prompting, RAG or fine-tuning?
These solve different problems. Confusing them is the most common — and most expensive — mistake in generative AI.
Prompt engineering
Best for: Behaviour & format
- Controls tone, structure, steps
- No new knowledge added
- Fastest and cheapest to change
- The first lever, always
RAG
Best for: Knowledge & freshness
- Injects your proprietary, current data
- Answers with citations
- Update knowledge without retraining
- The default for enterprise Q&A
Fine-tuning
Best for: Style & domain adaptation
- Bakes in tone, format, patterns
- Not a way to add facts
- Higher cost and lifecycle overhead
- The exception, used deliberately
Generative applications that hold up.
Assistants & copilots
Grounded helpers embedded in your product and workflows.
Enterprise search
Semantic search across your content, systems and knowledge.
Summarisation
Condense documents, threads and records reliably.
Structured extraction
Turn unstructured text into clean, typed data.
Content generation
On-brand drafts with humans firmly in the loop.
Classification & routing
Understand and route requests at scale.
Evaluation and safety, not vibes.
If you can't measure it, you can't ship it. We treat generative quality like any other engineering metric.
Groundedness & faithfulness
Is the answer actually supported by the sources?
Relevance
Did retrieval find the right context in the first place?
Hallucination detection
Catch and block unsupported claims.
Guardrails
Input and output filtering for safety and policy.
PII protection
Redaction and data-boundary controls.
Eval harness
Automated evals offline and live, gating releases.
A model strategy that ages well.
Models change monthly. We architect so the model is a swappable component — chosen on evidence, not hype.
Proprietary models
Best for: Peak capability, speed to value
- Top-tier reasoning
- Managed, hosted, fast to adopt
- Per-token cost, less control
Open models
Best for: Control, privacy, cost at scale
- Self-host inside your boundary
- Full control and portability
- You own the ops and tuning
Hybrid routing
Best for: Most enterprises
- Right model for each task
- Small models for easy work
- Cost and latency optimised
Questions a CTO asks.
Which model should we use?+
It depends on the task, your privacy needs and cost/latency budget — and it changes fast. We stay model-agnostic, benchmark options on your use-case, and design so you can switch without a rewrite.
Is our data safe?+
Yes. We use private deployments and provider settings that don't train on your data, redact PII, and keep retrieval inside your boundary. Data governance is part of the design, not an afterthought.
Should we fine-tune or use RAG?+
RAG for knowledge (facts, documents, freshness); fine-tuning for behaviour (style, format, domain tone). Most enterprise use-cases are a RAG problem — fine-tuning is the exception, not the default.
How do you know it's actually accurate?+
We measure it: groundedness and faithfulness (is the answer supported by the sources?), relevance, and hallucination rate — with an eval harness that runs offline and in production.
How do you control cost?+
Retrieval keeps prompts small, we route easy queries to smaller models, cache aggressively, and set token budgets — so quality goes up while cost goes down.