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.
Proof you can open, not slideware
CV-Matcher
Our own production hybrid RAG on Qdrant. Public repo, live demo, full teardown.
Open the repo Stepler, 10M+ downloads
An AI coach inside the fitness app we built end to end: 10M+ downloads, millions of users across 12 markets.
Case study AI EdTech, speech-to-text
WhisperX speech-to-text with word-level timestamps and speaker diarization, pgvector retrieval, CTO-led.
Read the case study Guidable
AI-generated audio tours in production, for hundreds of thousands of users across Germany. Backed by Deutsche Telekom.
Read the case study STFH, AI interviewer
Our own AI hiring product, deployed: AI video interviews with speech-to-text, text-to-speech, and LLM-based scoring. Rebranding from DevMatch to STFH.
Open the app Private LLM, legal
A private-LLM platform and its full infrastructure for a legal-tech product, built to run inside the client's own perimeter.
AI sales platform
Buyer research and personalized outreach, shipped as a production GTM tool.
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 correctnessThe vibe-coded app that double-charged everyone. Ledgers, idempotency, reconciliation, and the invariants the AI skipped.
- Workflow migrationWhen the no-code agent hits its ceiling, we re-platform it onto typed, durable, testable code with explicit state.
- PersonalizationMake the AI-built MVP production-real: grounding, guardrails, and the correctness layer it shipped without.
How to hire
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.
Traditional
AI sits off to the side. Hand-written code, occasional autocomplete.
AI-powered
Uses the tools ad hoc. Faster typing, the same workflow underneath.
AI-native
Work restructured around AI: specs, evals, review, context engineering.
This is us
Vibe coder
Ships fast, cannot maintain it. The MVP that breaks in production.
Our build loop
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.
01
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.
02
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.
03
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.
04
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.
How our code review works 05
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.
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 buildOur 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 AIReal, 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. 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.
See us work
Walkthroughs, not promises
AI backendA Gemini AI microservice in NestJSWatch the walkthrough AI EdTechSpeech-to-text for EdTechWatch the walkthrough Real codeInside our GitHub: real production codeWatch the walkthrough Full-stackA Next.js build with AI featuresWatch the walkthrough React + NestJSA full-stack product walkthroughWatch the walkthrough
CapabilitiesWhat 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.
- Hybrid retrieval:Dense plus keyword search on Qdrant and pgvector, reranked for precision.
- Public teardown:Our CV-Matcher RAG is open: the repo, a live demo, and the full write-up.
- Grounded answers:Responses constrained to retrieved, cited context, not the model’s guess.
AI agents
Multi-agent systems that survive production, deployed as real services.
- Orchestration:LangGraph graphs with explicit state and tool use, not a fragile prompt chain.
- Public repos:Working agent code you can read before you hire us.
- Durable and guardrailed:Retries, timeouts, and human checkpoints where they matter.
Speech-to-text and voice
WhisperX transcription with word-level timestamps and speaker diarization, in production.
- WhisperX:Word-level timestamps and speaker diarization at scale.
- Voice agents:Real-time speech in, grounded responses out.
- EdTech-proven:Shipped inside an AI learning platform, CTO-led.
AI integration
Add AI to the product you already have, grounded and production-ready.
- LLM features:Drafting, search, summarization, and classification wired into your stack.
- Workflow automation:Agentic steps that replace manual back-office work.
- Provider-agnostic:Claude, GPT, and open models behind one swappable layer.
Production hardening
Make an AI-built app real before money, scale, or users hit it.
- Money correctness:Ledgers, idempotency, reconciliation, and the invariants the AI skipped.
- Evals and observability:Golden-set evaluations that catch regressions before your users do.
- Migration off no-code:Re-platform onto typed, durable, testable code with explicit state.
Tell us what you're building. We'll tell you what we'd ship.
Trusted by the best in their industries.
Normative
Adam Egesa 
CEO & CTO
2muchcoffee provides top-notch development work and expert advice that please end-users needs.
The team is transparent about progress, communicative, and committed to deadlines.
Stepler
Niklas Frisk
Co-founder & CEO
The app has received positive feedback from users. 2muchcoffee leverages their strong
work ethic and technical expertise to produce results that meet the needs and requirements of the client.
The team develops solutions that engage the client's audience.
Scholyr
Lindsay Scholtes 
Co-founder & CEO
Internal stakeholders are pleased with the UX/UI and functionality of the final product.
Excellent communication and consistent professionalism were hallmarks of this partnership.
Customers can expect a dedicated, innovative partner that will meet every requirement.
Station
Alexandre Lacgèze 
Co-founder & CTO
Users commented that the revamped app was richer in features and more user-friendly.
The solution would also be a lot easier to scale in the future thanks to the well-written code.
Collaborative and diligent, 2muchcoffee took the time to understand the core business goals,
which informed the work.
Inktank
Peter ten Klooster 
Co-founder
2muchcoffee filled the development partner role seamlessly and created an essential component
for the client. Their team was responsive and always available. They offered detailed feedback that showcased
their expertise in the field. Customers can expect a capable and flexible team of developers.
Digistore24
Lars Rieger 
Product Manager
Collaborating with an in-house design team, 2muchcoffee delivered dynamic,
user-friendly websites and pages within a narrow time frame. The team remained involved and diligent,
offering experienced guidance and recommendations to minimize shortfalls or errors.
A senior team, honest about AI
A senior team that ships production AI, runs on AI itself, and is honest about what AI can and cannot do.
Your data stays yours
It never trains a third-party model. We design for confidentiality and review your data handling before a line of code ships.
A real, named stack
Claude, GPT, and open models behind a provider-agnostic layer. LangGraph, Qdrant, pgvector. We name the stack because we run it.
Grounding and a real eval loop
Answers constrained to retrieved, cited context, with an evaluation harness that catches regressions before your users do.
US company, global senior team
A US company with a distributed senior team: US accountability and business-hours overlap, engineers across Europe and North America.
FAQ
Questions buyers actually ask
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. The estimator at 2muchcoffee.com/estimate gives you an honest range in about two minutes, no email required.
Do you build custom OpenAI development services?
Yes, and provider-agnostic ones. We build on OpenAI and Anthropic models behind a routing layer, so a custom GPT-based assistant, RAG system, or agent is scoped the same way: grounded on your data, evaluated against golden sets, and deployable where your compliance bar allows, including self-hosted.
CONTACT OUR TEAM
Do you have an idea for your next project? Not sure what tech stack or business model
to choose? Share your thoughts and our team will assist you in any inquiry.
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