AI-Powered Software Factory

A team of AI specialists
that builds software
the way real teams do.

Most AI tools help one person code faster. PINC is different: it organizes 13 specialized AI agents into a coordinated team. A product manager plans. Developers build. QA reviews. DevOps deploys. They coordinate through structured tickets and dependency graphs, not conversations.

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TEAM 13 SPECIALISTS
pinc / how it works
$pinc describe
Coordination: Stigmergic (shared DB state)
Execution: Concurrent dependency graph
Trust: 4-tier graduated autonomy
Safety: Write-before-act audit
Honesty: 37-signal sycophancy guard
$pinc fleet
PM Agent .......... planning, intake, backlog
Backend Dev ....... APIs, migrations, tests
iOS Dev ........... Swift, SwiftUI, Combine
Android Dev ....... Kotlin, Compose, Retrofit
DevOps ............ CI/CD, Fastlane, signing
QA Agent .......... gates, health, testing
+ 7 more specialists
75 skills verified. All tests passing.
$
STATUS LIVE ON RAILWAY
⚙
Specialization over generalism
13 agents, each expert in one domain. The same principle that makes human teams outperform solo developers, applied to AI.
⚖
Concurrent, not sequential
Independent tasks run in parallel across agents. Dependency graphs determine what waits and what starts immediately.
☑
Every action is auditable
No black box. Every decision, every artifact, every quality check is logged before execution and traceable after the fact.
The Idea

Why teams beat
solo performers.

Building software is a team sport. Even the best individual developer can't match a well-coordinated team of specialists. PINC applies the same logic to AI: instead of one generalist agent, specialized agents handle what they're expert at, coordinated by proven structures from Agile methodology.

The gap
AI coding tools help individuals, not teams
Current tools help one developer write code faster. They don't address how requirements, design, backend, frontend, testing, and deployment come together into a coherent product. The coordination problem remains unsolved.
The model
Borrowed from 25 years of Agile practice
Sprints for scope control. Backlogs for prioritization. Reviews for quality. Retrospectives for improvement (Schwaber & Sutherland, "The Scrum Guide"). PINC borrows the structures, not the rituals. Sprint planning becomes a dependency graph generator. Refinement becomes parallel database writes. No meetings. No agent dialogue.
The mechanism
Agents coordinate through shared state, not messages
When the Backend Dev finishes an API, it writes what it built to the database. The iOS Dev reads that context when its dependency resolves. QA reads completed artifacts and writes review verdicts. This is stigmergic coordination (Grassé, 1959): the same indirect pattern observed in ant colonies, applied to software agents.
The result
A factory, not a pipeline
Not step 1, step 2, step 3. A concurrent execution graph where multiple specialists work simultaneously on independent tasks, block on dependencies when needed, receive feedback from QA, and iterate. Like a factory floor: many things happen at once, coordinated by the work itself.
How It Works

From your idea
to working software.

You describe what you need. Each phase involves the right agents doing their part. Work flows through a dependency graph: independent tasks run in parallel, dependent tasks wait. No bottleneck, no idle agents.

01
Discovery
PM Agent runs conversational intake. Captures the core problem: who is struggling, what breaks, what would make it remarkable. No work starts without a validated problem statement.
02
Planning
PM Agent generates tickets with dependency graphs and acceptance criteria. Tasks are ordered by priority and linked by what depends on what. Capacity is sized from velocity of previous sprints.
03
Refinement
Relevant agents review every ticket in parallel by writing structured feedback to the database. No meetings. No discussion tokens. Objections trigger revision. No ticket enters the sprint without quality consensus.
04
Execution
Tasks are dispatched based on the dependency graph. Agents claim work atomically. QA reviews completed work as it lands. Defects create corrective tasks routed back to the responsible developer agent.
05
Delivery
DevOps Agent owns CI/CD, code signing, and deployment gates. QA gate must pass before deploy. Deploy events are tracked and surfaced to stakeholders in real time.
06
Learning
The system captures what shipped and what broke. Velocity tracking feeds next sprint capacity. Skill confidence updates drive trust promotions. Every sprint makes the next one better.
The Team

13 specialists.
One domain each.

Each agent owns one domain and never crosses into another. The PM doesn't write code. The QA doesn't plan sprints. DevOps doesn't design UI. Clear boundaries make behavior predictable and make it possible to trace every decision back to the agent that made it.

Central
Central
Dispatches tasks, gates external actions, enforces architecture laws
PM Agent
PM Agent
Conversational intake, sprint planning, backlog prioritization, stakeholder alignment
Designer
Designer
Design tokens, component specs, naming conventions, WCAG accessibility
Backend Dev
Backend Dev
OpenAPI spec updates, SQL migrations, API test generation, breaking change detection
iOS Dev
iOS Dev
Swift/SwiftUI patterns, unit tests, UI components, Combine pipelines
Android Dev
Android Dev
Kotlin/Compose patterns, unit tests, UI components, Retrofit clients
QA Agent
QA Agent
Sprint gate evaluation, health scoring, test generation from acceptance criteria
DevOps
DevOps
CI/CD pipelines, Fastlane (iOS + Android), code signing, deploy checklists, rollback
Scrum Master
Scrum Master
Velocity tracking, impediment detection, sprint health monitoring, process improvement
Observatory
Observatory
Quality evolution tracking, sprint health trends, fleet performance intelligence
Security
Security
SAST scanning, deploy security gate, vulnerability detection, audit compliance
Coherence
Coherence
Architecture decision compliance, historical decision recall, cross-component consistency
Data Agent
Data Agent
Health score composites, velocity prediction, risk signal aggregation
Under the Hood

Built on proven patterns,
not hype.

PINC doesn't invent new computer science. It applies validated patterns from distributed systems, robotics, and database engineering to AI agent coordination. Each decision below has a traceable rationale in published research.

Trust
Graduated Autonomy (4 Tiers)
Sheridan & Verplank (1978), Levels of Automation
Skills start fully supervised (SEED tier: human approves every output). After 25 successful executions, they earn more autonomy. Automatic demotion after consecutive failures. Rolling confidence window. The same framework robotics has used for 45 years to calibrate human-machine trust.
Scheduling
PostgreSQL as Job Queue
Gray & Reuter (1992), Transaction Processing
Task claiming uses FOR UPDATE SKIP LOCKED: a PostgreSQL-native atomic operation. Task claim, status update, and audit log happen in a single transaction. No Redis, no RabbitMQ. The database is both the source of truth and the coordination medium.
Safety
Write-Before-Act Audit
Inspired by database Write-Ahead Logging
Before any external action (create ticket, open PR, deploy), the intention is logged to the audit table. If the audit write fails, the action is blocked. Borrowed from financial systems (SOX compliance). In an AI context, this means no agent can act in the dark.
Honesty
Sycophancy Detection
Addressing LLM agreement bias
LLMs tend to report success even when they fail. PINC scans agent outputs for 37 sycophantic patterns: vague success language, softened failure reports, premature agreement. Flagged outputs are confidence-penalized. A system that lies to you is worse than no system at all.
Resilience
Fleet-Wide Circuit Breaker
Nygard (2007), "Release It!"
Standard circuit breaker pattern, but shared across all 13 agents. When one agent discovers the LLM API is down, the entire fleet stops calling it. Prevents cascading failures, protects in-flight work, and avoids burning rate limits on a failing endpoint.
Learning
Cross-Project Skill Flywheel
Privacy-preserving knowledge transfer
Skill confidence deltas flow between project instances without exposing client data. New projects inherit network intelligence. Sprint velocity feeds future capacity planning. Each project makes the next one start smarter.
Honest Status

What's built.
What's next.

Transparency matters. Here is an honest assessment of where PINC stands today and what remains to be proven.

Built, Tested, Deployed

Working today

The coordination layer, all 13 agents, the skill system, and the safety model are implemented, tested, and deployed on Railway with a live PostgreSQL database.

  • 13 agents with 75 verified skills across all roles
  • 4-tier graduated autonomy with data-driven promotion
  • Sycophancy detection (37 signal patterns)
  • Write-before-act audit on all external actions
  • PM conversational intake and sprint planning flow
  • Deployed on Railway with live PostgreSQL
Proving Next

The full loop

All pieces are built. The remaining milestone is demonstrating the complete cycle in production: from a user's idea to a deployed application, autonomously.

  • Full end-to-end autonomous delivery cycle
  • Sprint execution with concurrent agent workstreams
  • QA feedback loop: defect found, fix applied, re-reviewed
  • Cross-sprint learning and velocity improvement
  • Mobile delivery pipeline (Fastlane + Maestro)
  • Multi-project Skill Flywheel in production
Business Model

Two tracks.
One system.

PINC powers internal delivery at Najma.tech and will be available as a delivery operating system for other teams.

Track A

Najma.tech
delivers on PINC

An AI-augmented software agency for projects where quality and traceability are non-negotiable. Full audit trail on every action. Graduated trust on every skill. Structured sprint cadence from intake to delivery.

  • Web, iOS, and Android development
  • Sprint-based delivery with ticket-driven coordination
  • Client visibility through supervision dashboard
  • Complete audit trail and security posture
Track B

Your team
runs on PINC

License PINC as your delivery operating system. Keep your clients and your brand. The Skill Flywheel means every team on PINC benefits from aggregated learning across the network.

  • PINC license with white-label dashboard option
  • Cross-project learning (privacy-preserving)
  • Full skill suite and quality tracking included
  • Track B partnerships planned for late 2026

Software delivery,
reimagined.

PINC is an active research and engineering project. If you care about quality, traceability, and structured process in AI-powered delivery, let's talk.

Get in Touch See the Code