Requirements. Code. Tests. Docs.
When did they last agree?

Your team has Jira, Kanban boards, refinement sessions. You discuss tasks, try to find edge cases, define test scenarios in advance. You add test coverage. You have release management — canary deploys, staging environments, the works. You're better than most of your peers.

And yet. You still have bugs. You still can't say exactly how everything works together — it's a collection of tickets, specs, and tribal knowledge. You can't confidently answer which exact test scenarios are covered and which aren't. Your documentation isn't correct and updated in all places at all times. The honest answer to all these questions is no.

And it only gets worse. You have Confluence, Jira, GitHub, Slack — but there is no single place where you can see how it all works together. The more components you add, the more micro-interactions multiply. The complexity doesn't scale linearly — it compounds. That's not a failure of your team. It's a failure of the tools.

Aerospace and automotive engineers have used these techniques for decades. Most software engineers have never heard of them. That's exactly the gap Proof fills — bringing these proven methods to ordinary teams.

1. Requirements engineering Writing down what your software must do before writing how. Not user stories — precise, verifiable behavioral specifications that can be traced to code and tests.
2. Formal verification Using mathematical solvers to prove properties about your spec. "Can these requirements all be satisfied simultaneously?" "Do any contradict each other?" "Are any conditions impossible to reach?" Answers in seconds, not weeks of review.
3. MC/DC coverage Modified Condition/Decision Coverage — proving that every boolean condition in your code independently affects the outcome. Goes far beyond line or branch coverage. Required by aviation (DO-178C) for the most critical software.
4. Bidirectional traceability Every requirement links to the code that implements it, the tests that verify it, and the docs that describe it. Change one — instantly see what else needs updating. The "blast radius" of every change, visible at all times.
5. Gap analysis Automatically finding what's missing from your specification. Unconstrained outputs, missing boundary checks, variables without assumptions. This is how Proof found a crash bug in the Tyk API Gateway that all tests missed.

Proof is not a requirements database. It is not a test runner. It is not a coverage tool. It is all of these fused into a single verification chain where every link is traceable, verifiable, and auditable.

1. Write requirements Full authoring tools for humans and AI. Multi-level: stakeholder use cases → system components → software specifications. Quality checks, gap detection, and conflict resolution built in.
2. Verify they hold Formal verification: are these requirements realizable? Consistent? Vacuous? Where are gaps? Proof uses Kind2 realizability, Z3 SMT proofs, and 7-category gap analysis to answer mathematically.
3. Define your data Property-based specifications: describe the shape of your data, merge strategies, boundary conditions. Proof generates test fixtures automatically and proves properties hold for ALL inputs, not samples.
4. Trace every change Bidirectional traceability from requirements → code → tests → documentation. See the blast radius of every change. Suspect links flag when artifacts drift. Nothing is missed.
5. Prove the code matches the spec MC/DC coverage at both specification and code level. Compare spec-level truth tables with code-level execution traces. Close the loop until they agree — or find issues in the spec itself.
6. Ship with evidence Generate NPR 7150, DO-178C, ISO 26262 compliant documents. Every claim backed by traceable evidence. 52 quality checks across 4 stages.

You don't start from scratch. Describe what your software should do in plain English — Proof's AI pipeline translates it into formal, verifiable requirements automatically. Our NL-to-FRETish translation outperforms published academic frameworks. You review and approve. The compiler validates in real-time — you can't produce an invalid specification.

No rewrite needed. Proof layers on top of your existing codebase. Pick your most critical component, make it bulletproof, then expand. In one case, applying Proof to a production API gateway's policy engine — without modifying a single line of existing code — found a crash bug that all tests had missed, plus seven undocumented design decisions. Two hours of spec work.

Tests prove what they test. They don't prove what they miss. Proof runs formal verification to catch what testing cannot: conflicting requirements, unrealizable specifications, vacuous conditions, and missing constraints.

Every requirement links to the code that implements it, the tests that verify it, and the documentation that describes it. Change a requirement — and Proof shows you exactly what downstream artifacts need updating. Change code — and Proof flags which requirements may now be suspect.

MC/DC (Modified Condition/Decision Coverage) proves that every boolean condition in your code independently affects the decision outcome. It has historically required $50K+/year tools (LDRA, VectorCAST) — and none of them support Go. Proof does.

The loop works both ways: export spec-level MC/DC truth tables, compare them with code-level execution traces, and iterate until they agree. When they don't — you've found either a bug in the code or an issue in the spec.

This is not an incremental improvement. It is a different paradigm in software development — one that merges the rigor of aerospace and automotive engineering with the agility of modern development workflows.