Your AI can code.It can't read your mind.
Entalpa turns your idea into structured requirements — stakeholders, stories, acceptance criteria — that AI coding agents and dev teams implement with full traceability. The rules only you know get captured before the code exists, not after they break.
Free to start — no credit card, no requirements training needed.
See it in 20 seconds
Free, no account needed. A few runs per visitor per day.
The hidden requirement
The most expensive requirement is the one nobody wrote down.
AI agents are excellent at the happy path and blind to the exceptions. In our evaluations, agents reliably guessed the obvious flows — and reliably missed capacity rules, cross-role policies, and the carve-outs stakeholders actually care about.
Without Entalpa
Week 1 — the demo looks perfect
The agent ships the booking flow. Every screen works. Everyone is impressed.
Week 3 — the workaround appears
An urgent referral needs a same-day slot, but the daily cap blocks it. You knew urgent cases bypass the cap. The AI didn't. The front desk goes back to the paper calendar.
Week 4 — the expensive fix
The exemption touches every place capacity is checked. What would have been one line in a spec is now rework across the codebase — plus the trust you lost at the front desk.
With Entalpa
Day 1 — Entalpa asks first
“Can anyone bypass the daily booking cap?” You answer in one sentence. It becomes a requirement with acceptance criteria.
Same week — built right the first time
The agent implements the exemption together with the cap, links the code and tests to the requirement, and you watch it turn green.
Why it pays
Guessing is the expensive part.
We ran controlled evaluations: the same feature, the same AI coding agent — once guessing from a short brief, once implementing structured Entalpa requirements. The requirements arm reached a correct build at roughly half the cost, and the gap widens as more unstated rules stack up.
Cost to a correct build
−48%
Fix-it iterations
−57%
Time to done
−34%
Same feature, same agent — indexed to the guessing baseline (100)
Cost to a correct build
Iterations to a correct build
Identical tasks graded by the same test suite; costs measured from real agent runs, not estimates. The later a missed rule is found, the more places the fix touches — which is exactly where the money goes.
How it works
Three steps, no requirements training.
Describe your idea
Entalpa maps stakeholders, drafts stories, and writes testable requirements — asking you the questions you'd only remember in production.
Hand it to your builders
Connect an AI coding agent over MCP, or share with your dev team. Every requirement is precise enough to implement without guessing.
Watch it turn green
Implementation status, code references, and tests flow back onto each requirement. You see real progress — not vibes.
7 of 12 requirements verified
- Req-001Patients can view and book available slots online.verified
src/booking/slots.py · 6 tests
- Req-004Only front-desk-published availability is bookable.verified
src/booking/calendar.py · 4 tests
- Req-009Urgent referrals may bypass the daily booking cap.in progress
src/booking/capacity.py · 2 tests
- Req-007Monthly insurer export with treatment codes.planned
Your view while the agent works: every requirement shows its status, the code that implements it, and the tests that prove it.
Who it's for
Founders & entrepreneurs
Your first PM.
You know the business — the exceptions, the customers, the rules. Entalpa turns that knowledge into specs an AI can build from, long before you can afford a product team.
Product managers
AI-speed development, still manageable.
Agents ship faster than tickets can track. Keep acceptance criteria, traceability, and status reporting — without slowing the agents down.
Why not just…
You've tried writing it down before.
…a Notion doc?
Prose drifts, and agents can't reliably consume it. Entalpa requirements are structured, versioned, and served to your agent over MCP.
…a ChatGPT thread?
Great conversation, zero persistence. Nothing links the decision you made in chat to the code that implements it.
…a ticket tracker?
Jira tracks work you already defined. It never asks what you forgot — and what you forgot is where the budget goes.
Questions product people ask
What is Entalpa, in one paragraph?
A place where a product idea becomes requirements an AI coding agent can implement without guessing. You describe what you want; Entalpa maps the people involved, drafts the stories and requirements, and asks you the questions that decide how the thing actually behaves. Your engineers' agents then read those requirements directly over MCP, so what was agreed is what gets built.
Do I have to write the requirements myself?
Short answer: No — you answer questions, and the answers become the requirements.
Entalpa drafts first, from a description in your own words. Your work is reviewing and deciding: it asks what happens when a booking is cancelled late, who is allowed to approve a refund, which rule wins when two apply. Those are product decisions, so they are yours — but you are answering a specific question rather than facing an empty template.
Every answer is written down as a requirement with its acceptance criteria, so the decision survives the conversation it was made in.
What do my engineers actually get out of it?
Short answer: Requirements their agent can read, with the reasoning attached.
Instead of a prose document they have to interpret, they get structured requirements with acceptance criteria, served to their coding agent over MCP. Each one carries the story it came from and the decision behind it, so a question like "why is it done this way" has an answer that is not somebody's memory.
The practical difference is fewer rounds of build-it, demo-it, discover-what-was-meant.
Do I need to learn a syntax, a template or a methodology?
No. You write the way you would explain the product to a colleague, and answer questions in plain language. Entalpa does the structuring — ids, priorities, acceptance criteria, traceability — because that is the part software needs and people find tedious.
Is my product idea used to train AI models?
Short answer: No.
Your projects are not used to train models, ours or anyone else's. Data is encrypted in transit and at rest, and the model providers we call are used under agreements that exclude training on submitted content.
This holds for every account, and it is contractual for corporate ones.
Ship what you actually meant.
Describe your idea today. In ten minutes you'll have stakeholders, stories, and requirements your AI agent can build from tomorrow.
Free to start — no credit card.