AI built on Claude.
We build assistants, agents and document tools on Claude. The model is the easy part. Our work is making sure it only sees what each person is allowed to see, and that every change is tested before it goes live.
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Claude solutions what you can buy from us.
What we deliver on Claude.
Claude is the model family most of our AI work runs on, chosen for long-context reasoning, reliable tool use and structured output that holds its shape under load. But the model is rarely the interesting part of a build.
The systems that hold up are the ones where search respects your existing permissions, where an agent gets no more access than any other service account, and where somebody can explain what it did last Tuesday and why. That is the work we focus on.
- Internal knowledge assistants A question-and-answer tool over your own documents that respects your existing permissions. It can never show someone a file they could not already open themselves. RAG Permission-aware Claude API
- Agentic workflow automation Agents with scoped tools over Model Context Protocol: read-only where reads suffice, explicit confirmation on writes, and every tool call logged and replayable. MCP Tool use Audit logging
- Document processing and extraction Structured extraction with a confidence threshold, so clean documents post straight through and ambiguous ones route to a person with the uncertainty surfaced. Structured outputs Vision Human-in-the-loop
- Customer support automation Drafting and triage grounded in your own help content and ticket history, with a human approving anything that reaches a customer until the accuracy earns more autonomy. Grounded drafting Triage Escalation
- Evaluation and quality harnesses A graded evaluation set built from your real cases and wired into CI, so a prompt change is measurable and a regression blocks the deploy instead of reaching production. Evals CI gates Regression testing
- AI readiness and architecture review A short engagement to work out whether a language model is the right answer at all, what it would cost at your volume, and what has to be true before it ships. Discovery Architecture Cost modelling
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How we work on Claude.
Pick the part you care about.
Answers grounded in your own content
Retrieval over your documents with citations back to the source, and authorisation enforced in the retrieval step rather than asked for in a prompt.
- Every chunk carries its source document's permissions
- Retrieval filters on the asking user's identity
- Citations back to the source for every claim
- Hybrid search and re-ranking for recall that holds up
Tools scoped like any service account
Agents that gather context across systems in one pass, with writes gated and everything auditable.
- Model Context Protocol for tool connectivity
- Read-only tools where reads are sufficient
- Explicit confirmation before anything consequential
- Every tool call logged and replayable
Document work that stops being data entry
Extraction with a confidence threshold and a human queue designed in rather than bolted on.
- Structured outputs the application can validate
- Confidence routing to a human review queue
- Vision for scanned and photographed documents
- Deterministic checks on anything that must be exact
Prompt changes you can measure
Without an evaluation set, "the new prompt seems better" is the entire quality process.
- A graded evaluation set from your real cases
- Wired into CI, with regressions blocking the deploy
- Tracing so a bad answer can be reconstructed
- Cost and latency tracked per task, not per project
Why us for Claude.
What you get from working with us.
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Evals before features
A graded evaluation set built from your real cases, wired into CI, with a regression blocking the deploy. It is the difference between engineering and guessing.
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Permissions are part of retrieval
Authorisation belongs in the retrieval step, not in a system prompt asking the model to be careful. A document you cannot open cannot leak through an answer.
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Cost and latency are requirements
Prompt caching, batching where the work is not interactive, and model selection per task rather than reaching for the largest one every time.
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We will tell you when not to
A lot of what gets scoped as an AI project turns out to be a data-quality or process problem in disguise. A short discovery engagement tells you which one you have.
Claude FAQs
Not under the standard commercial API terms, which state that inputs and outputs are not used to train models. We will point you at the current terms rather than paraphrase them. For sensitive workloads we design around your data residency and retention rules explicitly, including running through a cloud provider you already have an agreement with.
Yes. Claude is available through Amazon Bedrock and Google Cloud Vertex AI, so the processing runs inside an account and region you already control, under agreements you already hold. For regulated work this is the right call, and we will suggest it before you ask.
Mostly by constraining the problem rather than asking the model nicely. Retrieval grounded in your own sources with citations, structured outputs the application validates, a confidence threshold that routes uncertain cases to a person, and an evaluation set that catches regressions before release. Anything that must be exactly right gets a deterministic check, not a prompt.
Yes, and we do it regularly. Putting a language model over a data-quality or process problem hides the mess rather than fixing it. A short discovery engagement will tell you which problem you actually have.
A scoped assistant or extraction pipeline is typically six to ten weeks to production, with an evaluation harness from week one. Agentic workflows take longer because the tool surface and the approval model need designing before anything is wired up.
Tell us what you are trying to build.
Whether the platform is already decided or still an open question, a senior engineer will reply within one working day. No SDR, no slideware.
Already on a platform
Inherited a setup, or hitting its limits? We start with an audit of what runs, what it costs, what is exposed and whether backups restore.
Still deciding
We take no licence margin, so the recommendation follows your constraints. The honest answer is often the platform you already pay for.
Want to partner with us
Referral, joint delivery, or a product company needing an implementation team. Say which and a senior person will pick it up.
Tell us what you are trying to build.
Whether the platform is already decided or still an open question, a senior engineer will reply within one working day. No SDR, no slideware.