services
the AI software agency that proves your idea before you build it.
Startup validation & MVP validation, delivered as real code
codebefore is an AI software agency built around one job: startup validation through real, runnable code. Most agencies bill you to build — we get paid to help you decide what's worth building. Every engagement ends with software that proves your idea works, or shows you why it doesn't.
New here? Start with prototype vs MVP, startup validation with code, or what an AI SDLC actually is.
why codebefore
why choose codebefore for startup validation?
We're not a full-service agency chasing a long billing relationship. We're a lean, code-first validation specialist. Here's what that means in practice.
Paid to help you decide, not to bill hours
Success for us is you reaching clarity — build, pivot, or stop. We win when you avoid building the wrong thing.
Real code, not wireframes
We validate through runnable software that puts your riskiest assumptions under real pressure. Mockups can't do that.
Senior engineers, not a faceless shop
Work is led by engineers who've architected systems handling millions of transactions in fintech and gaming — including roles at Trading 212, Nexo, and PokerStars.
Lean by design
Fixed scope, fixed price, and a bias toward the smallest thing that proves the point. No gold-plating, no scope creep.
Every engagement ends in a decision
You leave with a written go / no-go / iterate recommendation and code you own — not an open-ended invoice.
prototype sprint
A focused, time-boxed sprint that delivers a working prototype to prove or disprove a core business idea using real code.
what's included
- Runnable prototype (owned by the client)
- Minimal but correct architecture
- Key assumptions validated
- Demo + code walkthrough
- Written decision summary (go / no-go / iterate)
duration
2–3 weeks
outcome
Clear evidence to decide whether to build, pivot, or stop.
build-ready blueprint
A technical blueprint supported by a small proof-of-concept codebase, designed to enable confident full product development.
what's included
- Architecture & system design
- Minimal proof-of-concept code
- Technology stack recommendations
- Delivery & scaling considerations
- Handover-ready documentation
duration
1–2 weeks
outcome
A clear technical path forward, ready for internal teams or vendors.
technical validation add-ons
Targeted technical spikes to validate specific risks or assumptions.
examples
- API feasibility
- AI / ML proof-of-concept
- Performance or scalability testing
- Third-party integration validation
the codebefore validation process
Validation-through-code, in a fixed sequence. Each step has a timeline and a concrete deliverable, so you always know where the money is going and what you'll walk away with.
- 1Week 1
Idea deep-dive & risk mapping
We interview you and any stakeholders, then map the riskiest assumptions — the ones that would sink the idea if they're wrong. We define what "validated" actually means for your case, in measurable terms.
Deliverable: A prioritised list of assumptions and the success criteria for each.
- 2Week 1–2
Minimal architecture design
We ask one question of every design choice: what does this system need to do in the next 12 months, not the next 5 years? We pick the smallest feature set and the stack that proves the idea fastest.
Deliverable: A lean architecture and the shortlist of technologies we'll build with.
- 3Week 2–3
Rapid prototype build
Real, runnable code — never mockups or wireframes. The build focuses entirely on the assumptions that matter, with tight feedback loops so we course-correct as reality pushes back on the plan.
Deliverable: A working prototype you own, focused on your core hypothesis.
- 4Week 3
Live testing & feedback
We put the prototype in front of real users or real data and watch what actually happens. Behaviour is the truth — decks and opinions are not. We iterate on the core hypothesis based on what we observe.
Deliverable: Behavioural evidence: what worked, what broke, and where.
- 5Wrap-up
Decision summary & handover
You get a written go / no-go / iterate recommendation, a demo, and a code walkthrough. The code and documentation are handed over in full — your foundation for building, pivoting, or moving on.
Deliverable: A decision summary, plus full code and docs, owned by you.
built for fast validation
Every technology choice is about speed and proof, not gold-plating. These are the tools we reach for — and they're the same ones behind the case studies below.
Backend & languages
Data & messaging
AI / ML
Cloud & DevOps
Frontend
validation by domain
The domains where we've done the deepest work. Each links to a real case study, not a stock illustration.
FinTech & trading systems
Prove a strategy or a low-latency system works before you commit real capital or a full build.
See the delta-neutral basis trading bot and microsecond-latency Java case studies.
LegalTech & AI assistants
Validate whether an LLM + RAG system can answer domain questions accurately, with citations, in your users' language.
See the Bulgarian legal chatbot built on FastAPI, LangChain, and pgvector.
Data & analytics platforms
Test whether a lean stack can deliver a queryable data mart without a heavyweight ETL pipeline.
See how a single Go binary and ClickHouse replaced Kafka, Spark, and Airflow.
Infrastructure & cost optimisation
Confirm a simpler, cheaper architecture can carry your workload before you migrate to it.
See seven WordPress sites consolidated onto a single $5 VPS with CloudPanel.
case studies
what validation looks like in practice.
A sample of the systems we've designed, shipped, and post-mortemed for clients and internal projects — with the question each one set out to answer.
trading systems
Delta-neutral basis trading bot
Validation: Can a market-neutral funding-capture strategy run safely on real money?
Result: Nine months of design, 520 tests, and eight stages shipped — with the failure modes (partial fills, precision errors, mid-trade reboots) engineered out before mainnet.
legaltech / AI
Legal chatbot for a Bulgarian law firm
Validation: Can an LLM + RAG system answer company-registration law questions in plain Bulgarian, with citations?
Result: A FastAPI + LangChain + pgvector system replaced a five-service RAG design — sized for ~100 queries/day at launch and maintainable by a small internal team.
infrastructure
7 WordPress sites on one $5 VPS
Validation: Can a sprawling Docker-compose setup be replaced by one boring, correct box?
Result: Seven sites consolidated onto a single Contabo VPS running CloudPanel — CPU around 2%, with headroom to spare.
performance
Microsecond latencies with Java
Validation: Can the JVM process 10 GB/s of market data without the GC wrecking latency?
Result: Hardware-aware Java techniques kept the critical path at sub-microsecond latency under heavy load — proven, not projected.
risks we mitigate during validation
Most startup software fails in predictable ways. Validation-first work is designed to catch these before they get expensive.
Building too fast
Problem: Founders overengineer before they've found product-market fit.
Our approach: We focus on the single riskiest assumption and prove or disprove it in real code before anyone scales.
Assumptions vs. reality
Problem: Decks and wireframes look great. Real usage is messier.
Our approach: Runnable code shows what actually breaks. User behaviour is the evidence, not a hunch.
Tech stack lock-in
Problem: The wrong technology chosen early becomes an expensive regret.
Our approach: Validation code is small enough to rewrite. Every stack choice is explicit and testable.
Investor & stakeholder skepticism
Problem: "Nice idea — but will anyone actually use it?"
Our approach: A live, working prototype in users' hands. Proof beats a pitch when you're raising or aligning a board.
Open-ended time & money
Problem: Validation drags on with no clear finish line.
Our approach: Fixed scope, fixed price, 2–3 week sprints, and a clear go / no-go decision at the end.
who validates your idea
real engineers behind the code.
Founder & Lead Engineer
Engineering Manager at Tradu and a Software Architect with over 10 years building high-performance, scalable systems in fintech and gaming. He has held architecture and leadership roles at Trading 212, Nexo, and PokerStars — designing distributed systems that handle millions of transactions.
faq
frequently asked questions
How much does MVP validation cost?
Prototype sprints range from €6,000–€9,000 depending on scope. Build-ready blueprints are €3,000–€4,000, and technical validation add-ons start at €1,500. Every engagement is fixed-price, so you know the cost before we start.
How long does a prototype sprint take?
2–3 weeks. You get a working prototype, a demo and code walkthrough, and a written go / no-go / iterate decision summary.
What's the difference between a prototype sprint and a build-ready blueprint?
A prototype sprint proves or disproves an idea with real, runnable code. A blueprint provides architecture, technology choices, and a small proof-of-concept to enable confident full development.
Can I use the prototype code for my product?
Yes. You own the code outright and can use it as the foundation for your product or as a reference for your engineering team. No licensing strings attached.
Do you validate technical feasibility?
Yes. Our technical validation add-ons target specific risks — API feasibility, AI/ML proof-of-concept, performance and scalability testing, and third-party integration validation.
What makes codebefore different from a general dev agency?
We don't bill by the hour to build whatever you ask for. We're paid to help you decide what's worth building. Success for us is you reaching clarity — build, pivot, or stop — not a feature count. We specialise in validation, not open-ended product development.
What if the validation says "don't build"?
That's still a win. We document what you learned, which assumptions broke, and what to test next. Failing fast on a €6K sprint is far cheaper than failing slowly on a six-figure build.
Do you work with non-technical founders?
Yes. We translate technical risk into business terms. You'll understand every architectural decision and what it proves — no jargon required to follow along.
Can my own engineering team work alongside codebefore?
Absolutely. We can pair with your in-house team, or hand off fully documented code and architecture for them to build on. The blueprint engagement is designed exactly for that handover.
What happens after the validation sprint?
One of three paths: you build the full product using our code and blueprint as a starting point, you pivot based on what the validation revealed, or you stop and save your runway for a stronger idea. We support all three.
Who owns the code after the sprint?
You do — 100%. Use it as your product foundation, as a reference for your team, or as a starting point for another agency. It's yours to keep.
How do you handle confidentiality?
We're happy to sign your NDA before any discovery work begins. Your idea, your data, and your code stay yours.
ready to validate?
Tell us what you want to prove. We'll help you figure out the fastest path to clarity.
start a conversation