The problem
QA reasoning lives in tickets, chat threads, and whoever happens to remember the last time this broke.
The thesis
AI can absorb the repetitive reasoning and preserve what the organization already learned — so judgment stays human.
The promise
Here’s exactly what’s real. And exactly what’s not. Nothing in between.
Most QA tools store test results. QA OS is trying to build the layer that remembers why — and gets better at reasoning about your product every time a human corrects it.
QA OS is an architecture for making organizational quality-engineering reasoning persistent and retrievable, instead of re-derived from scratch by whoever's on call — built independently, with an explicit, honest map of what's real today and what a real QA organization's data and feedback would take it to next.
How it works
Five steps. Three of them are real today.
This is the whole loop, in plain language — no diagram required yet. If you want the deep architectural version, that's a separate page; this is the one anyone can read.
Requirement
Someone writes what needs to be true, in plain English — e.g. “Users must be able to log in via OAuth2, including token refresh and session expiry.”
Risk is scored
The system checks its memory for similar requirements it has seen before, then scores how risky this one is — and writes down why.
Tests are drafted
Candidate test cases are generated from the requirement and its risk score. They are marked “draft.” Nothing here is trusted yet.
A person decides
A QA lead approves or rejects each draft. No model makes this call — this is the one step with no AI in it at all.
The decision is remembered
Whatever the person decided is written back to memory, so the next similar requirement inherits it instead of starting from zero.
That's the entire real, working slice. Everything past step five — running the tests, triaging failures, deciding if a release is ready — is the vision the rest of this page is honest about not having built yet.
The same loop, with a real example
A requirement arrives: “Users must be able to log in via OAuth2, including token refresh and session expiry.” QA OS checks its memory for similar past requirements — finds two, one flagged high-risk and approved, one rejected as over-tested. It scores this one's risk (0.82, high — auth surface, external-facing) informed by that history, and generates candidate test cases. A QA lead reviews them in the pending-review queue, approves one, rejects the other with a note. That decision is now in memory for the next auth-related requirement that comes in.
The approved tests execute. One fails. QA OS investigates: is this a real regression, a flaky test, an environment issue, or a test defect? It checks whether this code path changed recently and whether a similar failure happened before. It helps triage a defect, tags it to the requirement and the historical pattern, and factors it into release readiness. Months later, a new engineer asks "what happened the last time we touched the session-refresh flow?" — and QA OS has an answer, because the reasoning was never allowed to evaporate.
What's real, what's next
3 of 10 planned modules are real and wired today. Here's exactly which — and exactly what's next.
The honesty here isn't modesty — it's the engineering signal. Anyone can claim a system works. Fewer people publish a line-by-line map of exactly which three parts do, and delete the parts that only pretended to. That map is below, and it's the same one this codebase's own README uses to audit itself.
Scores how risky a requirement is before a single test is written.
Turns a requirement and its risk assessment into candidate test cases.
The human checkpoint before a generated test counts as real.
Runs generated tests via Playwright, orchestrated through Temporal.
Classifies defects using knowledge-graph context.
Produces a go/no-go recommendation, never an autonomous release trigger.
Natural-language queries over the knowledge graph.
Feeds production telemetry back into risk scoring.
Serves the dashboard's backend surface.
Wires external tools — Jira, GitHub, Slack, Playwright — into the pipeline.
The lifecycle
Ten modules, one pipeline — marked by what's actually true.
Real, specific, unglamorous facts
Retrieval memory grounded in real human review decisions, not vector-store guessing.
Swappable persistence — in-memory, SQLite, or Neo4j — with zero call-site changes.
Every LLM failure raises a real error; nothing silently fakes success.
Real per-call token and cost accounting, logged to an auditable ledger.
Every module in the repository is labeled by what it actually does — real, partial, or stub — including the ones that are broken or empty. Nothing fake is dressed up as finished.
Co-Building the Target Architecture
The 3-module core proves reasoning continuity works. Bringing the full 10-module operating system to life requires serious co-builders.
The verified working slice proves that software requirements can be risk-scored, synthesized into candidate suites, and locked through human review over real HTTP. Turning this foundational proof into an industry-standard quality operating system requires distributed systems engineering, production incident data, and visionary design partners. Here is how we build the vision together.
Partnership Track
Founding Systems & Platform Engineers
You build resilient distributed systems, workflow orchestrators, or storage infrastructure capable of handling mission-critical enterprise workloads.
“The target architecture calls for a persistent, multi-tenant knowledge graph and a resilient distributed execution engine. Module 4 is ready to be transitioned from a prototype interface to a production-grade Temporal/Playwright execution loop. Let's co-architect and build the core engine that powers this reasoning spine.”
Partnership Track
Applied AI & LLM Systems Architects
You specialize in deterministic validation, stateful agentic memory, semantic retrieval, and eliminating model hallucinations in high-stakes engineering domains.
“The working slice uses strict, deterministic extraction to keep model reasoning bounded. Bringing the full vision to life means evolving this into a hybrid semantic-and-relational memory layer where every prompt is mutation-verified and every retrieval traces back to auditable organizational history. Let's design the cognitive architecture together.”
Partnership Track
Principal QA Architects & Quality Leaders
You have led quality transformations at scale and know firsthand that engineering teams constantly lose tribal QA knowledge across sprint boundaries.
“QA OS is designed to institutionalize quality intelligence—capturing risk models, human review decisions, and causal defect post-mortems so teams operate at TMMi Level 5 without manual drag. Help refine the domain ontology, audit the reasoning heuristics, and shape the benchmark for autonomous quality governance.”
Partnership Track
Design Partners & Production Pilots
Forward-thinking engineering organizations ready to replace fragmented Jira, GitHub, and test handoffs with an uninterrupted, compounding reasoning layer.
“Deploy the QA OS working slice alongside your active delivery pipeline. Feed real sprint requirements into the risk-assessment and test-synthesis loop, calibrate the human approval gates against your team's standards, and directly shape the platform's commercial roadmap as a founding design partner.”
What's next
The roadmap is ordered by real dependency, not a features wish-list — execution has to be real before feedback loops mean anything, and evaluation has to exist before specialization is worth trusting.
See the roadmap →