Specs & Chat

From intent to implementation-ready spec

Codebase-grounded chat, custom reports, and JIRA/Linear enrichment. Every answer references source — every spec is anchored in how your system actually works.

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Custom Reports & Planning

Purpose-built analyses tailored to your specific initiative — migrations, feature planning, compliance mapping, and more.

Available Report Recipes

Codebase Migration Assessment
Custom Onboarding Document
New Feature Planning
Regulatory Compliance Mapping (GDPR, HIPAA, SOC 2)
Dependency Upgrade Impact
Module Extraction Analysis

“Migration from React 17 to 18 affects 142 components; 89% are compatible, 12 require Suspense boundary additions, and 3 rely on deprecated lifecycle methods.”

Custom Reports

Interactive Codebase Chat

Ask questions about your codebase and get instant, context-aware answers backed by deep code analysis. Every answer traces back to source — no hallucination.

What you can ask

“How does the authentication flow handle expired tokens?”
“Where is the logic that calculates shipping costs?”
“What components would be affected if I changed the Order schema?”
“Write a spec for a new endpoint following our existing patterns”

Codebase Chat

JIRA & Linear Integration

Enrich tickets directly in your backlog with context-aware specs, implementation plans, and AI-ready prompts.

What gets added to tickets

Context-aware PRD/spec with acceptance criteria
Technical implementation plan with affected modules
Impact & risk analysis with dependency mapping
Testing plan with suggested unit/integration/e2e tests
AI-ready prompts tailored for coding agents

Generate Spec

Click on any ticket to generate a structured PRD with technical plan and acceptance criteria.

Backlog Grooming

Auto-draft specs, effort estimates, and dependency analysis for upcoming sprint items.

Start Work

Get an AI-ready prompt pack with constraints, examples, and relevant files for coding agents.

Post-Merge

Automatically generate "what changed" notes and update architecture documentation.

Before

PLAT-1234.txt

// JIRA Ticket: PLAT-1234
// Status: Backlog

Title: "Improve checkout performance"

Description:
  "Checkout is slow. Please fix."

Acceptance Criteria:
  (none)

Estimate:
  (none)

After enrichment

PLAT-1234-enriched.json

// JIRA Ticket: PLAT-1234 (Enriched)
{
  "title": "Optimize checkout API latency",
  "impact_analysis": {
    "services": ["OrderService",
      "PaymentGateway", "InventoryCache"],
    "files_affected": 14,
    "risk": "medium"
  },
  "acceptance_criteria": [
    "P95 latency < 200ms (from 800ms)",
    "Zero increase in error rate",
    "Cart abandonment rate stable"
  ],
  "implementation_plan": [
    "Add Redis cache for inventory",
    "Parallelize payment + inventory",
    "Add connection pooling"
  ],
  "testing_plan": [
    "Load test: 1000 concurrent",
    "Integration: payment retry flow",
    "E2E: full checkout journey"
  ],
  "estimated_effort": "3-5 days",
  "ai_prompt_pack": "available"
}

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The MCP server lives at /mcp

Same context engine, exposed to Claude Code, Cursor, and Copilot via the Model Context Protocol. Fewer tokens per task, measured on SWE-Bench Pro.

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Built for the teams that translate intent into code

Developers

Using Cursor, VS Code agents, Claude Code

Platform teams

Standardizing AI-assisted development

Tech leads

Enforcing architectural and coding standards

Consultancies

Delivering across multiple client codebases

Plan with confidence. Ship with clarity.

Turn product intent into implementation-ready work — grounded in your actual codebase.

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