Embedded in your team
We work within your rituals, tools and repositories, on site and remote.
Forward Deployed Engineering
Our senior engineers work inside your team, on site and remote, building AI skills, agents and context engineering on the Claude and Cursor stack. Backed by evals, until your AI runs in production.
Our senior engineers work where your work happens: on site and remote, with your data, systems and governance. From the first use case to production.
We work within your rituals, tools and repositories, on site and remote.
We build inside your systems and your governance. NDA on request, no model training on your data or code.
The person in your first call is the same one designing your architecture and building with your team.
We cut bounded contexts together with your domain experts, using Domain-Driven Design and Event Storming. The specs for the agents come after.
For companies without dedicated technical leadership: architecture decisions, technology assessments, vendor selection and building internal engineering structures, until you hire your own.
In the end your team carries on independently, or we operate and evolve the system long-term.
We build on the Claude and Cursor stack and connect AI with the systems where your work actually happens. Every capability is backed by evals and made for production.
Reusable capabilities for agents on the Claude and Cursor stack: skills, project rules and commands that let agents work reliably in your codebase and your processes.
Our Claude Code overviewSchemas, memory files and data pipelines that give agents exactly the context they need. That is the decisive lever for reliable results.
Tool-using agents connected to your systems via the Model Context Protocol, with defined boundaries and approvals. Our own MCP server is publicly reachable.
Our MCP serverLM judges, golden datasets and CI/CD gates check how your AI systems behave before a change goes live.
How we evaluateLLMs, embeddings and automations, cleanly embedded in your existing software stacks and system landscapes.
Turning existing prototypes or grown systems into production-ready, maintainable software.
This is the technology behind our AI work: the same stack that runs our Labs tools in production.
Embeddings, vector stores, and hybrid search over enterprise knowledge – with chunking and reranking strategies we choose per corpus.
Tool-using agents coordinated via the Model Context Protocol (MCP), with schemas, memory files, and defined boundaries. We operate our own public MCP server, so we know the practice first-hand.
Input and output validation, prompt-injection defenses, PII filtering, and human-approval gates for consequential actions, built into the architecture from the start.
The right model per task: small and fast where possible, frontier-class where it matters. We plan caching, batching, and cost budgets in as architecture decisions.
Our Labs projects run in production on exactly this stack. You can look at them, and some you can try directly.
AI assistants use it to query our services, assessments and insights and, with consent, to submit an enquiry.
Hourly semantic clustering of 500+ news sources, served to web, iOS and Android.
S&P index inclusion forecast across the entire US equity universe, recalculated fully automatically twice per trading day.
Our assessment runs as an agent in dialogue and turns the conversation into a prioritised report.
The difference lies in how much structure and verification surrounds your work with AI.
From a quick prototype to a production-ready system: the right mode depends on stakes of the code. A demo for the board can be built fast and loose. An API that processes payments calls for Agentic Engineering.
Fast, casual prompts, often called vibe coding. Ideal for demos, user tests and technical hypotheses. The code serves learning and does not go to production.
Formal specifications, architecture docs and memory files define the context. Automated test suites, CI/CD gates and LM judges verify every step.
Ask an AI-native team how mature they are, and they answer with exactly one thing: how they evaluate their LLM systems. We treat evals as infrastructure and build them in from the first agent.
Automated graders that score AI output against a rubric – correctness, safety, tone – before a human ever sees it.
Curated test cases that catch silent regressions the moment a prompt, model, or context pipeline changes.
No agentic change ships without passing its eval suite – the same discipline as unit tests, applied to LLM behavior.
For high-stakes decisions, a defined review point where a person signs off. Evals shrink that surface, and a person stays involved at those points.
AI-native teams ask: "How do you evaluate your LLM systems?" That's the real maturity signal.
We work with Claude Code and Cursor every day. We bring that practice into your team, so your developers work agentically and quality stays high.
CLAUDE.md, Cursor rules and architecture docs that explain your conventions, your domain and your boundaries to agents.
Recurring tasks as skills and commands the whole team shares, from reviews to releases.
Tests, CI/CD gates and evals that check agentic changes before they reach the main branch.
We work with your team on real tasks until the new habits stick.
Speed alone does not decide the return on a codebase. Total cost of ownership does, and in the AI era that cost is driven by token economics. Prototype code written with fast, casual prompts (vibe coding) is almost free to start, but the bill arrives later if it stays in use: token burn when the model fixes its own mistakes, a maintenance tax when someone reverse-engineers ad-hoc code months on, plus security cleanup.
Agentic Engineering inverts the curve: more effort upfront (schemas, tests, structured context) for markedly lower cost per feature afterwards. Beyond the crossover point, vibe-coded software can cost 3–10× more per feature. That is why we use rapid prototyping for learning and Agentic Engineering for everything that has to live.
Whether AI skills, an agent connected to your systems or a production-ready agentic system: we find the right approach together. First conversation free, no commitment.
Discuss your project nowThe questions we hear most from clients in startups, hidden champions and enterprises.