Platform Capabilities

What ProductIntel does
and why it matters

Nine core capabilities that replace an enterprise tool stack with a single AI-native platform. Each one solves a specific problem that mid-market teams face today.

21

Modules

across 6 capability groups

21

AI Agents

across 7 specialized teams

85+

API Routes

core, workforce, admin, consumer

85+

Database Tables

with RLS on every table

55+

Pages

desktop + mobile + demo

70+

Service Files

data access and business logic

8

Security Scanners

running daily with auto-fix

9

Tour Stops

guided demo in ~5 minutes

01

AI-First Work Management

Replaces: Jira, Linear, Shortcut

The Problem

Teams open Jira to a wall of tickets. No prioritization, no context, no recommendations. Every decision requires manual effort.

How We Solve It

Open ProductIntel and AI tells you what needs attention, why it matters, and what to do about it. Cost estimates, risk assessments, and recommended actions — before you ask.

›AI Triage generates a narrative briefing from backlog state, agent capacity, and budget
›Every story shows estimated token cost, risk level, and recommended action
›Smart Assign matches stories to the right agent team based on content analysis
›Spec Pipeline enriches vague stories into agent-ready specifications with file paths, acceptance criteria, and constraints
›Budget tracking shows monthly spend, burn rate, and stories remaining
02

AI Agent Workforce Management

Novel capability — no equivalent exists

The Problem

AI agents are configured in code, monitored through logs, and managed with tribal knowledge. There is no standard way to compose, benchmark, or optimize agent teams.

How We Solve It

ProductIntel treats AI agents like employees. Define them, compose teams, set strategy, track performance, and A/B test configurations — all through a management UI.

›21 specialized agents across 7 teams with profiles, tiers, specialties, and earned badges
›Team Wizard: describe what you need in plain English and AI recommends composition, strategy weights, and guardrails
›Supervisor configurations: 4 management styles (Delegator, Coordinator, Director, Facilitator)
›Training Arena: A/B test team configurations against benchmark stories with LLM-as-judge scoring
›Export team configs to Claude Code, Cursor, GitHub Copilot, Aider, Continue.dev, and JSON
03

Autonomous Agent Execution

Replaces: Custom CI/CD agent scripts

The Problem

Running AI agents requires custom infrastructure — worktree isolation, dependency management, test harnesses, PR creation, and failure recovery. Most teams build this ad-hoc.

How We Solve It

Click "Execute" on a story. An agent creates a git worktree, installs dependencies, implements the feature, runs tests, commits, pushes, and opens a PR — with full observability.

›Git worktree isolation — each job gets its own branch, no conflicts
›3-level testing: build verification, agent-run test suite, post-merge webhook
›Activity Monitor shows real-time execution with step-by-step logs
›Feedback loop: agents can ask questions, humans respond, agents resume
›Results written back: branch, PR URL, diff stats, commit log, test results
›Defects auto-created when builds or tests fail — closes the quality loop
04

Context Engine (RAG Foundation)

Differentiator: measurable AI effectiveness tied to context quality

The Problem

AI tools make generic recommendations because they lack product context. Agents that don't know your architecture, conventions, or business rules produce code that needs heavy review.

How We Solve It

The Context Engine is a structured RAG pipeline that feeds every AI decision. Upload your product knowledge and AI effectiveness jumps from ~60% to ~95%.

›Tiered onboarding: Quick Start (5 min, ~70% effectiveness) to Full Documentation (~30 min, ~95%)
›Quality scoring tracks coverage across 7 categories with specific gap identification
›Embedding pipeline (pgvector) for semantic search across all documentation
›Every agent call automatically pulls relevant context via getRelevantContext()
›Documents organized by product with architecture, conventions, API patterns, and business rules
05

Full AI Traceability

Replaces: Langfuse, Helicone, custom logging

The Problem

When AI makes a recommendation, stakeholders ask "why?" Most tools can't answer. Traceability is limited to developer-facing spans, not business-readable explanations.

How We Solve It

Every AI response in ProductIntel is fully traceable — what was retrieved, what the model received, and a plain-English explanation of why it responded the way it did.

›Inference Inspector: filter by source, model, cost, and latency
›Narrative explanations for non-technical stakeholders, not just spans for engineers
›Every LLM call logs tokens, cost, latency, retrieval sources, and prompt to pb_agent_runs
›Model Calibration: golden test cases with side-by-side comparison across providers
›Multi-provider support: Anthropic Claude + Google Gemini with per-feature model selection
06

Connected Knowledge Graph

Replaces: Disconnected Jira + Confluence + ServiceNow

The Problem

In typical tool stacks, a support case lives in ServiceNow, the related story lives in Jira, and the documentation lives in Confluence. Nothing is connected.

How We Solve It

Every artifact in ProductIntel — stories, documents, cases, research findings, feature requests — lives in a unified model connected through typed graph edges.

›Unified artifact model: STORY, DOC, CASE, API, HEURISTIC, REQUEST all in one table
›Knowledge graph with typed directional edges and confidence scoring
›A support case automatically informs a product story which triggers agent execution
›Intelligence Hub visualizes the full graph with force-directed layout
›Hybrid search: full-text + vector similarity across all artifact types
07

Competitive Intelligence Automation

Replaces: Gong, Klue, manual competitive analysis

The Problem

Small teams can't afford competitive analysts. By the time you manually research competitors, the landscape has shifted.

How We Solve It

AI-powered research pipelines that monitor competitors, analyze market trends, and surface strategic findings — automatically and continuously.

›Strategy research topics with AI-powered web research and finding accumulation
›Industry intelligence feeds monitoring relevant sectors
›Behavioral learning: Observer Agent watches platform usage patterns and extracts heuristics
›Research findings automatically connect to the knowledge graph
›Discovery Agent identifies emerging patterns across your entire artifact graph
08

Anti-Platform Architecture

Architecture: fork, don't rent

The Problem

SaaS platforms lock you in with multi-tenancy, opaque data models, and no customization. You rent features — you never own them.

How We Solve It

ProductIntel uses an anti-platform architecture. Each company forks the repo and owns their instance. Toggle modules, customize agents, swap models — all data-driven.

›Module manifest at repo root — enable or disable any of the 21 modules
›Schema split: upstream-owned core + fork-customizable module schemas
›Agent customization is data-driven: prompts, models, and tools live in database rows
›Model selection is config-driven via pb_model_config — never hardcode model IDs
›Theme customization: CSS variables, no design system lock-in
09

Security & Compliance

Built-in, not bolted on

The Problem

AI platforms often treat security as an afterthought. Vulnerabilities accumulate, audit trails are missing, and there's no systematic approach to AI safety.

How We Solve It

An 8-scanner security pipeline that runs daily, auto-creates findings, assigns them to agents for remediation, and auto-resolves when fixes are verified.

›8 scanners: npm audit, outdated packages, env exposure, auth guards, SQL injection, XSS, RLS check, sensitive columns
›Auto-response: critical findings auto-assign to Platform Security team with notifications
›Auto-resolution: re-scan verifies fixes and marks findings as done
›Row Level Security on all 85+ tables — including demo user write-blocking
›Supabase Auth with Google OAuth, email verification, and user approval gate

Technology

Built on production-grade infrastructure

Next.js 16

App Router, React Server Components, Turbopack

React 19

Latest concurrent features, server components by default

TypeScript

Strict mode, end-to-end type safety

PostgreSQL 17

85+ tables, pgvector embeddings, full-text search

Drizzle ORM

Typed queries, zero-overhead, schema-first

Supabase

Auth, Realtime subscriptions, Row Level Security

Anthropic Claude

Primary LLM — Haiku, Sonnet, Opus per feature

Google Gemini

Secondary provider — Flash and Pro tiers

Vercel AI SDK

Streaming responses, multi-provider abstraction

Tailwind CSS v4

Velocity-inspired design system, dark mode

See it in action

The guided demo walks through 9 key areas in about 5 minutes. No signup required.

Start the Demo TourBack to Home