Generative AI

Generative AI and agents that do real work, safely.

We build assistants and multi-step AI agents that search your knowledge, draft documents and carry out tasks across your tools — with guardrails, audit trails and human checkpoints where the stakes are high.

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Answers cite their sources
Human checkpoints
Private deployment options

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Built withGuardrails & human review

The challenge

A demo is easy. A dependable system is not.

Generative AI can draft, summarise and reason across information in seconds. The difficulty is making it accurate on your data, safe with sensitive information and reliable enough to put in front of staff or customers.

We combine retrieval over your own content, carefully designed prompts and tools, and an evaluation harness that measures quality on every change. Agents get only the permissions they need, and anything consequential goes to a person for approval.

What you walk away with

A generative AI assistant or agent in production
Retrieval over your documents and data (RAG)
Guardrails, permissions and audit logging
An evaluation suite that gates every release
Cost and usage monitoring

Discovery: 2 weeks
Models: Hosted or private
Controls: Guardrails + audit trail
Releases: Fortnightly

Capabilities

What we deliver

Knowledge assistants (RAG)

Ask questions of policies, contracts and manuals and get answers with links to the source.

AI agents

Multi-step agents that use tools — search, CRM, email, spreadsheets — to complete tasks.

Content generation

Proposals, reports, product copy and summaries drafted in your voice for people to review.

Workflow automation

Generative steps embedded in existing processes, from intake triage to report preparation.

Model customisation

Prompt engineering, fine-tuning and model selection matched to your accuracy and cost targets.

Safety & evaluation

Red-teaming, guardrails and automated evaluation so quality is measured, not assumed.

Use cases

Where it makes a difference

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Knowledge work

Your organisation’s knowledge, one question away

Staff ask in plain language and get an answer drawn from your own documents, with citations they can check — instead of searching shared drives for an hour.

Search across documents, wikis and tickets
Respects existing access permissions
Every answer links to its source

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Operations

Agents that take work off your team

Agents prepare first drafts, reconcile information across systems and move routine cases forward, leaving people to review, decide and handle the exceptions.

Tool access limited to what the task needs
Approval steps for consequential actions
Full audit trail of every step taken

How we deliver

Four stages, no black box

12 weeks
Discovery
Pick the task, gather real examples and define what a good answer or outcome looks like.
22–4 weeks
Prototype
A working prototype on your data, measured against the evaluation set from day one.
3Fortnightly releases
Harden
Guardrails, permissions, integrations and monitoring added before wider rollout.
4Ongoing
Scale
Roll out to more users and tasks, with quality and cost tracked on every release.

Technology

Built on proven, mainstream tools

We choose technology for reliability and for how easily your team can run it after handover — not for novelty. Everything integrates with the systems you already use.

Check your stack with an engineer

Frontier LLMs

Open-weight models

Vector databases

Agent frameworks

AWS / Azure / GCP

LLM observability

Questions

Generative AI, answered

Something else on your mind? Ask our team.

Is our data used to train public models?

No. We use enterprise model agreements that exclude your data from training, or deploy open-weight models privately in your own cloud when required.

How do you stop the AI from hallucinating?

Answers are grounded in retrieved source material and cite it, the system is told to decline when sources do not support an answer, and an evaluation suite measures accuracy on every release.

Which model do you use?

Whichever meets your accuracy, latency, cost and data-residency needs. We are model-agnostic and design systems so the model can be swapped as better options appear.

Can agents take actions on their own?

Only within the permissions you approve. Low-risk steps can run automatically; anything consequential — sending, paying, deleting — can require a person to approve first.

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