Generative AI solutions
A general-purpose model knows a great deal about the world and nothing at all about your business. Everything useful happens in the layer between the two.
We build retrieval-grounded assistants, copilots and automation on top of foundation models — with the retrieval, evaluation and guardrails that turn an impressive demonstration into something a regulated business can put in front of staff and customers.
Six shapes generative AI takes inside a business
Knowledge Assistants
Retrieval-augmented question answering across policies, contracts, manuals and tickets — answers with citations, so a reader can check them.
Workflow Copilots
Drafting, summarizing and triage embedded in the CRM, service desk or back-office screen your team already works in.
Agentic Automation
Multi-step processes where the model calls your own tools and APIs under an approval policy, with every action logged and reversible.
Document Generation
Proposals, responses, summaries and reports produced from structured data and templates, with a person approving before anything leaves the building.
Customer-Facing Chat
Support and self-service conversations with a strict scope, a defined escalation path and a tone that matches the rest of your brand.
Analytics Assistance
Internal tooling that lets a non-technical team query data, generate a report and reach an answer without a ticket in the analytics queue.
How a generative AI engagement runs
Scope
Pick a use case with a countable baseline — handling time, deflection rate, cost per document.
Ground
Ingest, chunk and index the source material, with permissions carried all the way through to retrieval.
Build
Prompt design, tool calling, orchestration and the interface the people doing the work will actually use.
Evaluate
A scored test set of real questions, re-run on every change — accuracy, refusals, latency and cost tracked together.
Ship and watch
A staged rollout with feedback capture, so the evaluation set grows out of live usage rather than guesswork.
What makes it safe to put in front of people
These are not optional extras further down the roadmap. They are part of the first release, or the first release does not happen.
- Answers grounded in retrieved sources, with citations shown
- Permission-aware retrieval — nobody sees what they could not already open
- PII detection and redaction on the way in and on the way out
- Prompt-injection and jailbreak testing before launch
- Refusal and escalation behaviour defined per use case
- Full prompt, response and tool-call logging for audit
- Cost and rate limits enforced per user and per tenant
- A kill switch that falls back to the existing process
The demonstration is the easy fifth of the work
A convincing generative AI prototype takes days. What takes the rest of the quarter is retrieval quality, permissions, evaluation, cost control, and the change management around a tool that is confidently wrong every so often.
We plan for that from the first week, which is why our engagements start with an evaluation set and a baseline number rather than with a model choice. The model is the part of this stack that is easiest to swap later.
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