Production AI we shipped,
not demos
RAG systems, AI agents, and custom integrations, grounded and evaluated. The model is the commodity. Senior engineering judgment is the product.
Book a 30-min reviewAI services
Four ways to work with us on AI, from a single integration to a dedicated team of engineers.
AI development
Production AI built from the ground up: retrieval, agents, evaluations.
AI agent development
Multi-agent systems with explicit state, real tools and guardrails.
AI integration services
AI features wired into the product you already run, provider-agnostic.
AI engineers
Senior AI engineers embedded in your team and managed by you.
Proof you can open, not slideware
- CV-MatcherOur own production hybrid RAG on Qdrant. Public repo, live demo, full teardown.
- Stepler, 10M+ downloadsAn AI coach inside the fitness app we built end to end: 10M+ downloads, millions of users across 12 markets.
- AI EdTech, speech-to-textWhisperX speech-to-text with word-level timestamps and speaker diarization, pgvector retrieval, CTO-led.
- NormativeAI-powered sustainability assessment for the emissions reporting platform.
- GuidableAI-generated audio tours in production, for hundreds of thousands of users across Germany. Backed by Deutsche Telekom.
- STFH, AI interviewerOur own AI hiring product, deployed: AI video interviews with speech-to-text, text-to-speech, and LLM-based scoring. Rebranding from DevMatch to STFH.
- Private LLM, legalA private-LLM platform and its full infrastructure for a legal-tech product, built to run inside the client’s own perimeter.
- AI sales platformBuyer research and personalized outreach, shipped as a production GTM tool.
Recent Projects
AI and software products we have shipped to production, not demos: an AI coach at 10M+ downloads, audio tours backed by Deutsche Telekom, and more.
The AI that doesn't hallucinate, hardened for production
Grounded answers, an evaluation harness that catches regressions, and observability in production. When an AI-built app has to be real before money or scale hits it, we run three rescue lanes.
Money correctness
The vibe-coded app that double-charged everyone. Ledgers, idempotency, reconciliation, and the invariants the AI skipped.
Workflow migration
When the no-code agent hits its ceiling, we re-platform it onto typed, durable, testable code with explicit state.
Personalization
Make the AI-built MVP production-real: grounding, guardrails, and the correctness layer it shipped without.
Founding engineers, not contractors
Our people have been the core technical team, not a body shop. We embed as founding engineers and fractional CTOs.
External CTO and core build
Our team has held External CTO roles at multiple startups, the CTO seat on an AI EdTech speech-to-text platform, and lead engineer on a stealth GPU startup rebuilt after a vibe-coded MVP.
Senior engineers behind the AI
Real, senior, and accountable, not a faceless bench. That EdTech speech-to-text platform alone took four, one each on the CTO seat, the WhisperX pipeline, the pgvector RAG, and the UX.
We build AI-native ourselves
Every engineer ships through Claude Code (how we build with Cursor and Claude Code). The proof is committed in this site's public repo: AGENTS.md, CLAUDE.md, and llms.txt are right there in git. We sell what we live.
AI-native vs AI-powered: tell them apart
The real question is not whether a developer uses AI, but how they structure the work around it.
AI explores the problem and helps shape the plan.
Developer plans the work manually.
AI helps break down the task.
Spec-driven, not vibe-coded
AI-native is a workflow, not a plugin. Most of what we ship is now written with AI in the loop, mostly Cursor and Anthropic’s models, and what changed is the shape of the work rather than only its speed. This is the loop that keeps what we ship reviewed and production-solid instead of fragile, and the same one that built the site you are reading.
Interrogate the idea, not the code
Every build opens with a relentless design interview. We pull the decisions out one at a time: what the thing is for, what it must not do, and what happens at the edges. The output is a written spec with the architecture calls recorded as decision records. Most projects fail in the gap between what someone asked for and what they meant, and this is where we close it.
The spec becomes tasks and tests
That spec is decomposed into discrete tasks, each carrying its own acceptance test, so what done means is settled before a line is written. Integration tests are declared up front and run against real infrastructure instead of mocks. This is the step that makes AI output reviewable: a model given a vague instruction returns plausible code, which is the expensive kind of wrong.
Agents build in small passes
Claude Code and Codex implement in parallel, each in its own branch, against those written tasks rather than a loose ticket. Code lands in increments, each one against its tests. Large one-shot generations are how teams end up with a codebase nobody understands, including the people who shipped it.
Reviewed twice, and verified past the spec
No agent-written diff reaches your product unreviewed. A senior engineer runs it through correctness and security review, alongside a model, before it merges. Then we drive the real product the way a user would, across light and dark, desktop and phone, because a green test suite is only ever as complete as the spec behind it.
A person owns the calls that matter
Architecture, security boundaries, data models, and anything touching money or patient data are decided and signed off by an engineer. The tooling drafts, refactors, writes migrations and fills in tests. It does not get the final say, and that line does not move.
What we build with AI
Production systems, not demos. Each of these is grounded, evaluated, and shipped, with public proof where we have it. The model is the commodity; the engineering around it is the product.
RAG and knowledge systems
Hybrid retrieval that grounds answers in your data, with the teardown published.
Dense plus keyword search on Qdrant and pgvector, reranked for precision.Our CV-Matcher RAG is open: the repo, a live demo, and the full write-up.Responses constrained to retrieved, cited context, not the model’s guess.AI agents
Multi-agent systems that survive production, deployed as real services.
LangGraph graphs with explicit state and tool use, not a fragile prompt chain.Working agent code you can read before you hire us.Retries, timeouts, and human checkpoints where they matter.Speech-to-text and voice
WhisperX transcription with word-level timestamps and speaker diarization, in production.
Word-level timestamps and speaker diarization at scale.Real-time speech in, grounded responses out.Shipped inside an AI learning platform, CTO-led.AI integration
Add AI to the product you already have, grounded and production-ready.
Drafting, search, summarization, and classification wired into your stack.Agentic steps that replace manual back-office work.Claude, GPT, and open models behind one swappable layer.Production hardening
Make an AI-built app real before money, scale, or users hit it.
Ledgers, idempotency, reconciliation, and the invariants the AI skipped.Golden-set evaluations that catch regressions before your users do.Re-platform onto typed, durable, testable code with explicit state.
Engagement models
Three ways to bring us in. Pick the one that fits your stage, you can change it as your roadmap changes.
Dedicated developer
One senior AI engineer, full-time on your product and managed by you, in your standups, your board, and your repository.
- Full-time on one product
- Works in your tools and process
- Design, QA and code review behind them
- Monthly rate, scale up or down
Best fit for an ongoing roadmap with a steady flow of work.
Dedicated team
A cross-functional pod that owns the AI feature end to end: engineering, evals, and QA, without you growing headcount.
- Engineers, design and QA in one pod
- Owns delivery end to end
- Ramps up without new hires
- Grows and shrinks with the roadmap
Best fit for a product that needs more than one pair of hands.
Project-based
A scoped RAG system, agent, or integration, delivered against a fixed plan, price, and timeline agreed before work starts.
- Scope agreed up front
- Fixed plan, price and timeline
- Delivered end to end
- Handover with the repo and docs
Best fit for a defined AI feature with a clear finish line.
Questions
What AI has 2muchcoffee actually shipped?
Production retrieval and agent systems: a public hybrid-RAG teardown on Qdrant, public multi-agent repos, the AI coach inside the consumer-scale Stepler app, and a production WhisperX speech-to-text platform for EdTech.
AI-native or AI-powered, what’s the difference?
AI-powered teams use the tools ad hoc. AI-native teams restructure the work around AI, with specs, evals, and review. The difference shows up in production, not in a demo.
What does spec-driven AI development actually mean?
Spec-driven AI development means the specification comes first and the code answers to it. We run a design interview, write a spec where every user story carries its acceptance test, and record the architecture decisions, then Claude Code and Codex build against that spec while a senior engineer reviews every merge. The agents do the typing; the spec and the review hold the quality line.
Can you rescue an AI-built MVP before it breaks?
Yes. We harden AI-built apps for production: money correctness, migration off no-code, and the grounding and guardrails the prototype skipped.
How do you stop the model from hallucinating?
Grounding and evaluation. We constrain answers to retrieved, cited context and run an evaluation harness with golden sets, so a change that increases hallucination shows up before your users find it.
Do you build AI from scratch or add it to an existing product?
Both. We build AI products from the ground up, and we add AI to an existing product through our integration services.
What does an AI MVP cost?
A focused AI-native MVP starts from around $7k. What moves the number is scope, the integrations, and your compliance bar, so every build is scoped before we quote rather than sold at a fixed price. Our estimator gives you an honest range in about two minutes.
Contact our team
Our team contacts you within 24 business hours
We collect all the key requirements from you
The team of developers prepares estimation
We can sign NDA since we respect the confidentiality of our clients