Generative AI

Generative AI That Does More Than Generate Text

The hard part of generative AI isn't calling a model. It's grounding it in your content, evaluating whether the answers are actually right, and keeping it reliable and affordable once real people depend on it.

What we build

Retrieval-augmented generation

Answers grounded in your documents, with citations back to the source.

Internal knowledge assistants

Search and Q&A across policies, tickets, wikis and contracts your team can't find.

Support copilots

Drafting and suggestion tools that keep an agent in the loop before anything is sent.

Document intelligence

Extraction, classification and summarisation of invoices, forms and correspondence.

Permission-aware retrieval

Answers that respect document-level access control, with access logged — the question enterprise procurement always asks.

Agentic workflows

Multi-step automation with tool access, guardrails and human checkpoints, with authorisation enforced where tools execute.

Content generation

Drafting inside a review workflow, never publishing straight to production.

The engineering under it

A demo takes a weekend. Production takes the rest of this list.

Evaluation harnesses so you can tell whether a change helped or hurtRetrieval quality — chunking, embeddings, reranking and index hygieneGuardrails, refusal behaviour and graceful degradationPrompt and model versioning, with the ability to roll backCost and latency monitoring per featureAccess control, audit logging and data residencyMCP servers to expose internal systems under governanceHuman-in-the-loop wherever being wrong is expensive

Why most pilots fail

MIT research in 2025 found roughly 95% of enterprise generative AI pilots produced no measurable impact on profit and loss, and Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027 on cost, unclear value or inadequate risk controls. The pattern in the failures is consistent: no evaluation baseline, no integration into an actual workflow, and retrieval built over content whose permissions were never modelled.

Agree an accuracy threshold before the pilot startsAgree a kill criterion at the same timeIntegrate into the tool people already use, not a separate chat appModel permissions before indexing a single document

An honest caveat

Generative AI is confidently wrong in ways traditional software is not, and fluent enough that people believe it. If a task needs the same answer every time, or a wrong answer is costly and hard to detect, conventional software is the better tool. We apply the same four questions to every use case: is the problem measurable, does AI beat conventional software here, can it be deployed safely, and can the result be measured afterwards.

Explore Generative AI

Tell us what you're trying to build. We'll help you figure out what comes next.

Start a Conversation All Services