the project management tool
that knows your
engineering team's history.
Falcon gives 10–30 person engineering teams structured project delivery with AI that understands every decision, every sprint, every contributor — not just your ticket titles. Swap your AI provider in one config change.
Jira grew up for enterprises.
Notion grew up for writers.
Neither grew up for your team.
Context dies in Slack
Three weeks ago your team decided not to use microservices for this feature. That decision lives in a Slack thread that no one can find. Your AI summarizes tickets. It doesn't know why.
AI lock-in is a business risk
You picked one AI provider because it was the best in January. It isn't in August. Every tool that hardcodes its AI model is a migration project waiting to happen.
Your PM tool doesn't scale with you
Notion at 5 people. Jira at 50. There's a cliff somewhere in the middle — around 15 to 30 people — where every tool either drowns you in ceremony or falls apart entirely.
Everything your team needs.
Nothing it doesn't.
Engineering-first project management
Projects, sprints, milestones, deliverables — structured the way engineering teams actually work. Not adapted from an issue tracker designed for ticketing systems. Not a Kanban bolt-on to a docs tool.
Realms for organizational structure. Sprints for delivery cadence. Milestones that mean something. All enforced with proper access control and multi-tenant isolation from day one.
AI that knows your whole story
Every sprint, every architectural decision, every contributor's history — organized into a knowledge graph your AI can reason from.
Ask it why a decision was made six months ago. Ask it which team member has the most context on the auth system. Ask it what's blocking this milestone and get an answer that makes sense.
Model-agnostic by design. Configure Claude, GPT-4, Gemini, or local models via Ollama. Switch providers in one config change, no migration required.
Your models. Your rules.
The AI landscape changes every quarter. The best model for code review today may not be the best model in six months. Your project management tool shouldn't make that decision for you.
Configure which model handles planning, which handles code review, which handles summarization. Swap providers at the config layer — not by migrating data or retraining workflows.
context_service:
provider: anthropic
model: claude-sonnet-4-5
planning_agent:
provider: openai
model: gpt-4o
code_review:
provider: ollama # local
model: llama3.2
summarization:
provider: google
model: gemini-2.0-flash
Production-grade infrastructure.
Not a prototype dressed as a product.
Multi-Tenant Isolation
PostgreSQL Row-Level Security + JWT tid claim enforced at every layer
Event-Driven Architecture
RabbitMQ topic exchange + canonical event envelopes — services communicate through contracts
Zero-Trust Internal Auth
Short-lived RS256 service tokens (30–60s) between every internal service call
3-Level Policy Cache
In-memory → Redis → HTTP fallback for entitlement enforcement at sub-millisecond latency
Compliance Audit Log
Append-only, immutable event store — every state change recorded, tenant-scoped, queryable
Full Observability
Prometheus metrics, distributed tracing via correlation IDs, structured JSON logs across all services
Five isolated databases. One docker-compose up."
Honest tiers. No surprises.
- 1 user
- 1 active project
- AI context service
- Full sprint tracking
- Unlimited projects
- Full AI context service
- Configure any AI provider
- Sprint analytics
- Priority support
- Audit log (90 days)
- Everything in Start Up
- SSO / SAML
- On-premise AI models (Ollama)
- Dedicated support
- SLA guarantee
- Unlimited audit retention
Shape what we build.
Lock in half-price. Forever.
We're working directly with 10 founding engineering teams. You get direct access to the founders, genuine influence over the roadmap, and Start Up pricing locked at $15/seat/mo for as long as you use it.
We read every application personally. We're looking for honest feedback, not testimonials.