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Autonomous SDLC platform · idea to PR
From a single idea to production-ready software.
An autonomous AI engineering team (PM · Tech Lead · SWE · QA · SecEng · SRE · DevOps · Scrum Master) ships your project end-to-end — PRD, architecture, code, tests, security review, deployment plan.
- PM
- Tech Lead
- SWE
- QA
- SecEng
- DevOps
- SRE
- Scrum Master
8-agent chain. One canonical flow.
Each agent owns one phase of the SDLC, with a real artefact committed to your repo at each step. No black boxes — every handoff is captured and audit-logged.
- 01
PM
PRD
- 02
Tech Lead
Architecture
- 03
SWE
Code
- 04
QA
Test Results
- 05
SecEng
Security Report
- 06
DevOps
Deployment Manifests
- 07
SRE
Rollout Plan
- 08
Scrum Master
Sprint Plan
See what the team can build
Each output is a real PR in your repo — not a sandboxed demo. Browse a handful of workflow shapes the chain ships every day.
- Backend
REST API + tests
FastAPI route, Pydantic schemas, pytest coverage. One workflow, one PR.
View example - Integration
OAuth integration
3-legged OAuth, encrypted token storage, refresh logic, ContextVar plumbing.
View example - Infrastructure
Background-job queue
Redis-backed async queue with retry, dead-letter, and OTel spans.
View example - Scheduling
Cron-driven aggregator
Periodically-fired aggregator with fan-in shape and idempotent runs.
View example - Database
Migration + backfill
Alembic revision, online-safe migration, batched backfill job.
View example - Operations
Incident postmortem doc
Five-whys narrative, timeline, action items linked to ADRs.
View example
Everything you need from a real engineering team.
Not a code-completion sidekick. A full team — and a full audit trail.
Real execution
Agents emit code into a per-workflow git worktree; preflighted; pushed as real PRs.
Configurable gates
Approve at any phase (after_pm, after_tech_lead, before_push, before_deploy). Human-in-loop where decisions matter.
Multi-component delivery
One workflow can output multiple repos; per-component PRs with their own CI.
Refinement loop
Real ADO build pipelines iterate on PR feedback until green or escalate.
Multi-tenant SaaS
RLS-isolated by default; per-tenant feature gates; cost transparency per agent.
Memory + RAG
Neo4j knowledge graph + Qdrant vectors + Redis short-term. Lessons compound.
Observability
OTEL spans for every LLM call. Costs per workflow / per agent. Reasoning visible.
Cost transparency
Per-agent / per-model spend. Hard caps. Budget alerts before you spend $0.50.
Standards that compound.
Qynise reads your team's actual standards — Confluence pages, ADR docs, existing repos — and stamps them on every new project automatically.
ADR drift detection
Flags when running code diverges from a recorded decision. The team knows the moment a deploy contradicts an ADR.
Spec drift detection
Catches when implementation drifts from the original PRD. Every commit re-validated against acceptance criteria.
Lesson-effectiveness loop
Measures which standards actually move quality + cost numbers; demotes the ones that don't. Your playbook compounds.
Why an agent team beats a single agent or a manual SDLC
The same eight artefacts you'd ask of a real team — at the cadence of a coffee break.
PRD before code
- Qynise
- Yes
- Single-LLM
- No
- Manual
- Yes
Architecture review
- Qynise
- Yes
- Single-LLM
- No
- Manual
- yes (on review week)
Real code in repo
- Qynise
- Yes
- Single-LLM
- sandbox
- Manual
- Yes
Automated tests with no mocks
- Qynise
- Yes
- Single-LLM
- Partial
- Manual
- Yes
Security review
- Qynise
- Yes
- Single-LLM
- No
- Manual
- depends on team
Deployment plan
- Qynise
- Yes
- Single-LLM
- No
- Manual
- Yes
Cost / time predictable
- Qynise
- Yes
- Single-LLM
- per-prompt
- Manual
- multi-week
Idea → PR
- Qynise
- ~minutes
- Single-LLM
- sandbox only
- Manual
- weeks
One command. Eight artefacts.
Watch the chain run from the CLI. Everything is real — code, tests, manifests, PR.
qynise — bash
$ qynise workflow run "Add /healthz to the FastAPI gateway" [PM ] PRD ready[Tech Lead ] Architecture + 4-task plan[SWE ] Wrote 3 files (src/api/main.py, src/api/routes/health.py, tests/unit/test_health.py)[QA ] Tests pass (3/3 new, 0 regressions)[SecEng ] OWASP review — no findings[DevOps ] Dockerfile + k8s manifest emitted[SRE ] Runbook + on-call mapping[Scrum Master ] Sprint update posted PR opened: github.com/your-org/api/pull/247 ($0.42, 4m 18s)4m 18s
Average build time
$0.42
Cost per workflow
~minutes
Idea → PR
Built for production from day one
The numbers behind the chain — surface, cost, and where the human stays in the loop.
8
agents
in the chain
1,003
routes
API surface
4
gates
configurable at every phase
$/agent
costs
dollars-per-hour visible
From early-access teams
Pre-launch quotes from teams piloting Qynise on real production work. Full attributions go live alongside v0.1.0.
“The PRD-before-code discipline is what we kept saying we wanted. The chain just does it — and the SecEng pass actually catches things our own reviews miss.”
“Two engineers, one weekend, four shipped services. The refinement loop reads CI feedback and fixes itself. That's the value prop in one sentence.”
“Cost transparency per agent is the line item I was missing from every other tool. I can finally tell my board what AI engineering costs us per feature.”
“Configurable gates win. We approve after PRD and after Tech Lead, then let the rest run. Humans where decisions matter, agents where they don't.”
Try the local stack.
make run brings up the full 14-service stack on your laptop. No signup required.