Content AI assistants, automated processing, MCP servers, custom workflows. Claude Code, Anthropic SDK, Model Context Protocol - production AI integrations that actually move the needle.
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Chat-based editing for your CMS, internal knowledge bases, or customer-facing help. Built properly, not glued on with a prompt.
Expose your systems to Claude, Cursor, or any MCP-capable client. Your tools become a natural-language interface for your team.
Retrieval-augmented generation across your documents, sites, and databases. Answers grounded in your content, not invented.
Multi-step agents that do the work, not just describe it. Content audits, data analysis, support triage - running autonomously with audit trails.
Prompts that perform. Eval suites that catch regressions. AI engineering, not prompt-poking - built the way we ship our own AI.
Bring your own API key or run open-weight models on your own infrastructure. No data leaves your stack if that's your requirement.
Half of what people ask for is not the right job for AI. We'll tell you honestly if your idea is a fit, a half-fit, or a waste of money.
Working prototype in under a week. Real data, real prompts, real evals. You see if it actually works before anyone writes a production spec.
Proper AI engineering - caching, evals, observability, guardrails. Production AI behaves very differently from demo AI, and we know the difference.
Most AI projects fail because they skip step one.
Integrate AI into your project →Eddi is our AI content assistant for Umbraco, with 240 tests passing and real editors using it daily. EditMyPDF fills forms from plain-English profiles. This website uses Claude-driven scaffolding for its content and pages.
We are not advising on AI from the sidelines - we are shipping it.
Tell us the use case. We'll prototype it in a week and tell you if it's worth building properly.
Start an AI project →
The ones we get asked most. If yours isn't here, send us an email.
AI integration means building AI into your existing applications and workflows: content AI assistants for your CMS or knowledge base, MCP servers that expose your systems to natural-language tools, RAG systems that answer questions grounded in your own documents, and multi-step agent workflows that do the work rather than just describe it - all backed by proper prompts and eval suites.
Ask us - half of what people ask for is not the right job for AI, and we'll tell you honestly if your idea is a fit, a half-fit, or a waste of money. Most AI projects fail because they skip that step. If it is a fit, we prove it with a working prototype before anyone writes a production spec.
Under a week. The prototype runs on real data, real prompts and real evals, so you see whether the idea actually works before committing to a production build. From there, shipping to production adds proper AI engineering: caching, evals, observability and guardrails - because production AI behaves very differently from demo AI.
An MCP (Model Context Protocol) server exposes your systems to Claude, Cursor, or any MCP-capable client, so your tools become a natural-language interface for your team. We build MCP servers as part of our AI integration work - this website itself was scaffolded using Claude-driven tooling.
Yes. You can bring your own API key, or run open-weight models on your own infrastructure - no data leaves your stack if that's your requirement. The same constraint can apply across everything we build, from content assistants to RAG systems and agent workflows, so compliance requirements shape the architecture from the start.
Yes. Eddi is our AI content assistant for Umbraco, with 240 tests passing and real editors using it daily. EditMyPDF fills forms from plain-English profiles. This website uses Claude-driven scaffolding for its content and pages. We are not advising on AI from the sidelines - we are shipping it.