ROADMAP: Heinrich Project Development Plan

Prepared: December 26, 2025
Target Vision: Enterprise-grade TRIZ-AI solution
Timeline Horizon: 24 months

Phase Overview

Phase 1: MVP Foundation     [Q1-Q2 2026]  → "Proof of Concept"
Phase 2: Production Ready   [Q3-Q4 2026]  → "Stable Release"
Phase 3: Market Entry       [2027]        → "Commercial Launch"
Phase 4: Scale & Expand     [2027+]       → "Enterprise Platform"

PHASE 1: MVP FOUNDATION (3-4 months)

Goal: Complete, working TRIZ pipeline that demonstrates core value proposition

1.1 Core Pipeline Completion (6 weeks)

Sprint 1-2: Contradiction Identification Engine

Deliverable: Implement contradiction matrix lookup algorithm

# Pseudocode
def identify_contradiction(parsed_problem):
    # Map problem text → 39 parameters
    improving_param = map_to_parameter(parsed_problem.desired_improvement)
    worsening_param = map_to_parameter(parsed_problem.undesired_consequence)
    
    # Look up in contradiction matrix
    principles = contradiction_matrix[(improving_param, worsening_param)]
    
    # Rank by relevance to problem context
    return sorted(principles, key=relevance_score)

Tests: 50+ test cases covering all parameter combinations
Acceptance: Can identify contradictions in 20 reference TRIZ cases

Sprint 2-3: Principle Selector with Scoring

Deliverable: Intelligent principle selection and ranking

def select_principles(contradiction, problem_context):
    # Get recommended principles from matrix
    candidates = get_principles(contradiction)
    
    # Score by:
    # 1. Domain relevance (manufacturing, software, etc.)
    # 2. Principle complexity (conservative for safety-critical)
    # 3. Historical success rate (when available)
    scores = {p: score_principle(p, problem_context) for p in candidates}
    
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)

Tests: Validate against domain experts for 30 test problems
Acceptance: Top-3 principles match expert recommendations 80%+ of time

Sprint 3-4: Effects Lookup & Scientific Integration

Deliverable: Enrich effects database and enable lookup
- Expand effects_database.json with 100+ scientific effects
- Implement effect → principle cross-reference system
- Add effect domain tags (physics, chemistry, materials, etc.)
Tests: Validate effects database completeness
Acceptance: Can retrieve applicable effects for any principle

1.2 LLM Integration (4 weeks)

Sprint 4-5: Implement OpenAI Adapter

Deliverable: Full OpenAI/GPT-4 integration

class OpenAIAdapter(BaseLLMAdapter):
    def generate(self, prompt, system_prompt=None, max_tokens=2000):
        response = openai.ChatCompletion.create(
            model=self.config["model"],
            messages=[
                {"role": "system", "content": system_prompt or self.config["system_prompt"]},
                {"role": "user", "content": prompt}
            ],
            max_tokens=max_tokens,
            temperature=self.temperature
        )
        return LLMResponse(
            content=response.choices[0].message.content,
            model=response.model,
            usage=response.usage
        )

Features:
- Token counting for cost estimation
- Retry logic with exponential backoff
- Rate limiting handling
- Context window management
Tests: Mock API calls, validate request/response formats
Acceptance: Successfully calls OpenAI API and returns structured responses

Sprint 5-6: Implement Anthropic Adapter

Deliverable: Claude API integration
Identical approach to OpenAI for consistency
Acceptance: Can switch between providers via config

1.3 Concept Generator with LLM (4 weeks)

Sprint 6-7: LLM-Based Synthesis

Deliverable: Replace template-based generation with LLM prompting

def generate_concepts(self, problem, principles, effects):
    concepts = []
    for principle in principles:
        prompt = self._build_synthesis_prompt(problem, principle, effects)
        response = self.llm.generate(prompt, system_prompt=SYNTHESIS_SYSTEM_PROMPT)
        
        concept = parse_concept_response(response)
        concept.principle_justification = principle.description
        concept.applicable_effects = effects
        
        concepts.append(concept)
    
    return concepts

Prompts to create:
- synthesis_prompt.txt - Generate solution from principle
- advantages_prompt.txt - Identify advantages of approach
- challenges_prompt.txt - Identify implementation challenges
- adaptation_prompt.txt - Adapt to specific constraints
Tests: Validate prompt quality with sample outputs
Acceptance: Generated solutions are coherent and actionable

Sprint 7-8: Pipeline Orchestration

Deliverable: End-to-end pipeline that produces working solutions

class HeinrichPipeline:
    def solve(self, problem_text):
        # Step 1: Parse
        parsed = self.problem_parser.parse(problem_text)
        
        # Step 2: Identify contradictions
        contradiction = self.contradiction_identifier.identify(parsed)
        
        # Step 3: Select principles
        principles = self.principle_selector.select(contradiction, parsed.context)
        
        # Step 4: Lookup effects
        effects = self.effects_lookup.lookup(principles)
        
        # Step 5: Generate concepts
        concepts = self.concept_generator.generate(parsed, principles, effects)
        
        # Step 6: Adapt solutions
        adapted = self.adaptation_planner.adapt(concepts, parsed.constraints)
        
        # Step 7: Build report
        report = self.report_builder.build(parsed, contradiction, principles, effects, adapted)
        
        return report

Tests: Integration tests with end-to-end execution
Acceptance: Can run complete analysis on 10 diverse problems

1.4 Testing & Validation (Ongoing through Phase 1)

Test Suite Overhaul

Unit Tests: Expand from stubs to comprehensive coverage
- Contradiction identifier: 50+ test cases
- Principle selector: 30+ test cases
- Concept generator: 20+ test cases
- LLM adapters: 40+ test cases
Integration Tests: End-to-end pipeline validation
- 20 diverse TRIZ case studies from literature
- Ground truth: Expert-validated solutions
- Success metric: Generated principles match expert top-3 in 80% of cases
TRIZ Methodology Tests: Validate TRIZ fidelity
- Contradiction matrix lookup: 100% accuracy
- Principle selection: Matches classical TRIZ recommendations
- Parameter mapping: Correct identification of system elements

Golden Test Suite

Create: 10-15 canonical TRIZ problems with expert-validated solutions
- Example: Classical problems from TRIZ literature
- Documentation: Why each principle is appropriate
- Metrics: Scoring rubric for solution quality
Use: Regression testing for all future changes

1.5 Documentation (Concurrent)

API Documentation: Auto-generated from code (Sphinx)
Pipeline Tutorial: Walk-through with example problem
Architecture Deep-Dive: How each component works
Contributing Guide: Clear instructions for extending system
Deployment Guide: Local setup + Docker

1.6 Phase 1 Success Criteria

✅ Must achieve:

Complete TRIZ pipeline produces solutions end-to-end
Golden test suite passes with 80%+ principle selection accuracy
All core modules have unit tests (>70% coverage)
LLM integration working with both OpenAI and Anthropic
Can handle 100+ problems without errors
Documentation sufficient for developer onboarding

PHASE 2: PRODUCTION READY (3-4 months)

Goal: Robust, deployable, observable system ready for real users

2.1 Production Infrastructure

Error Handling & Resilience

Global error handler with structured logging
Circuit breaker for LLM API calls
Fallback strategies:
- If LLM unavailable: Use rule-based concept generation
- If contradiction not found: Return all 40 principles ranked by domain
Validation: Validate all inputs, return helpful errors

Logging & Observability

Structured logging with correlation IDs
Metrics collection:
- Pipeline execution time
- LLM token usage (cost tracking)
- Principle selection accuracy (vs. human evaluation)
- User satisfaction (if telemetry enabled)
Dashboards: Basic health monitoring

Configuration Management

Environment-based config:

# config.yaml
llm:
  provider: openai  # or: anthropic, ollama
  model: gpt-4-turbo
  temperature: 0.7
  max_tokens: 2000

database:
  type: sqlite  # or: postgresql
  path: ./heinrich.db

logging:
  level: INFO
  format: json

Secrets management: Support for .env files and environment variables
Feature flags: A/B testing for algorithm variants

Database/Persistence

Implement: SQLite (local) or PostgreSQL (production)
Schema:
- Problems analyzed (with metadata)
- Solutions generated (with feedback)
- User evaluations (rating solutions)
- Case history (for learning)
Purpose: Enable learning from user feedback

2.2 API & User Interface

REST API

Endpoints:

POST   /api/v1/solve          - Solve a problem
GET    /api/v1/problems/:id   - Retrieve past problem
POST   /api/v1/feedback       - Rate solution quality
GET    /api/v1/principles     - List all principles
GET    /api/v1/parameters     - List all parameters
GET    /api/v1/statistics     - System statistics

Features:
- OpenAPI/Swagger documentation
- Rate limiting (100 req/min per API key)
- Pagination for large result sets
- JSON response formatting

CLI Tool

Commands:

heinrich solve "problem description"
heinrich list-principles
heinrich validate-knowledge
heinrich serve  # Start API server

Web Dashboard (Optional for Phase 2)

Simple problem input form
Solution visualization
History browser
(Can defer to Phase 3)

2.3 Deployment & Containerization

Docker

Dockerfile:
- Multi-stage build (dev dependencies stripped)
- Security: Non-root user
- Health checks
docker-compose.yml:
- Heinrich API service
- Optional PostgreSQL
- Optional monitoring (Prometheus)

CI/CD Pipeline

GitHub Actions:
- Run tests on every PR
- Lint code (flake8, black)
- Type check (mypy)
- Security scan (bandit)
- Build Docker image
- Deploy to staging on push to main

Deployment Targets

Local: Docker Compose
Cloud: AWS Lambda, Google Cloud Run, Azure Container Instances
Enterprise: Kubernetes-ready

2.4 Code Quality

Type Hints

Complete: Add type hints to all functions
Tool: mypy for static type checking
CI: Fail build if type errors

Documentation

Docstrings: Google/NumPy style for all public functions
Code comments: Explain why, not what
Examples: Every module should have usage example

Testing

Target: 80%+ code coverage
Tools: pytest with coverage.py
Reports: HTML coverage reports in CI

Code Style

Formatter: Black (automatic code formatting)
Linter: flake8 + pylint
Pre-commit: Hooks to prevent non-compliant commits

2.5 Performance Optimization

Profiling

Identify bottlenecks:
- Is it problem parsing?
- Is it LLM API calls? (Usually yes)
- Is it data loading?

Caching

Cache layers:
- Knowledge base (in memory)
- Parameter/principle lookups
- LLM API responses (for identical requests)

Async Support

Make LLM calls async to handle concurrent requests
Benefits: Single server instance handles multiple users

2.6 Security & Compliance

Security Hardening

Input validation: Sanitize problem text
Output validation: Never return secrets in logs
Dependency scanning: Check for vulnerabilities
Secrets: No hardcoded API keys

Data Privacy

User data: Minimal collection (only what's needed)
Retention policy: Delete old problems after 90 days (configurable)
Logging: Don't log full problem text (only hashes)

Documentation

Security policy: SECURITY.md with responsible disclosure
Privacy policy: What data is collected and how

2.7 Phase 2 Success Criteria

✅ Must achieve:

Passes security audit checklist
API serves 100+ requests/minute stably
Deployment to 3+ cloud platforms successful
80%+ test coverage
Zero critical vulnerabilities in dependencies
Comprehensive error handling (no crashes)
Logging/monitoring operational
Can run offline (no required external dependencies)

PHASE 3: MARKET ENTRY (6 months)

Goal: Launch commercial product with competitive advantage

3.1 Market Research & Differentiation

Competitive Analysis

Goldfire Insight: Analyze pricing, feature set, positioning
Custom GPT + TRIZ: Understand DIY alternatives
Patent AI tools: Learning from adjacent market

Customer Discovery

Interviews: 20-30 with target customers
- R&D teams in manufacturing
- Innovation managers in tech
- Patent attorneys
- Consulting firms using TRIZ
Surveys: What problems do they have with existing solutions?

Product Positioning

Identify: Unique value proposition
- Example: "Explainable AI that actually follows TRIZ principles, not just mimics them"
- Example: "Faster than Goldfire, cheaper than consultants"
- Example: "Integrates with your existing tools (Jira, Confluence, etc.)"

3.2 Product Enhancement

Domain-Specific Models

Create: Specialized versions for key industries
- Manufacturing (mechanical systems)
- Software/Tech (feature design)
- Materials science (new materials)
- Business processes (non-technical)

Advanced Features

Multi-step problems: Solve cascading contradictions
Team collaboration: Multiple users on one problem
Solution comparison: Side-by-side evaluation
Patent analysis mode: Find solutions to patented problems
Integration APIs: Connect to Jira, Confluence, GitHub, Slack

Knowledge Base Expansion

Add: Industry-specific effects databases
Validate: Have domain experts review principle selections for their field
Create: Case study library with documented solutions

3.3 Validation & Case Studies

Golden Test Suite Expansion

Build: 50+ validated case studies
- 10 from manufacturing
- 10 from software/product
- 10 from materials science
- 10 from business process
- 10 from historical TRIZ problems

Benchmark Against Baseline

Metrics:
- Principle selection accuracy vs. human experts
- Execution time vs. classical TRIZ consultants
- Cost per problem solved
- User satisfaction scores

Publication & Credibility

Write: 3-5 technical papers
- "Hybrid TRIZ-AI for Systematic Innovation"
- "Empirical Evaluation of Principle Selection Accuracy"
- Case studies in target journals
Conference presentations: TRIZ conferences, innovation summits

3.4 Pricing & Business Model

Pricing Options

Option A: Freemium SaaS

Free tier: 5 problems/month
Pro: $99/month (unlimited, API access)
Enterprise: Custom (dedicated instance, training)

Option B: Per-Use API

$0.50 - $5.00 per problem solved
Depends on complexity (LLM tokens used)
Volume discounts for enterprise

Option C: Hybrid

Subscription for web app + pay-per-call for API
Best of both worlds

Customer Segments

Target 1: Innovation Teams (SME)

Pricing: $199/month
Problem: Need systematic innovation
Solution: DIY tool that guides the process

Target 2: Consulting Firms

Pricing: Custom enterprise contract
Problem: Scale TRIZ expertise beyond consultants
Solution: AI as accelerator, not replacement

Target 3: Patent/R&D Organizations

Pricing: $10-50k/year
Problem: Generate novel solutions at scale
Solution: Integrated into their workflow

3.5 Go-to-Market Strategy

Phase 3.1: Beta Launch (Months 1-2)

Closed beta: 50-100 power users (TRIZ practitioners)
Collect: Feedback and usage metrics
Document: Success stories and testimonials

Phase 3.2: Public Launch (Months 3-4)

Announcement: Blog post, Twitter, newsletter
Pricing announcement: Clear pricing tiers
Marketing: Technical blog about TRIZ + AI
Partnerships: Start conversations with TRIZ organizations

Phase 3.3: Growth (Months 5-6)

Community building: TRIZ + innovation community
Content marketing: Guides, tutorials, case studies
Sales process: Enterprise sales for larger deals
Partnerships: Integrations with complementary tools

3.6 Phase 3 Success Criteria

✅ Must achieve:

1,000+ paying users or $50k+ MRR
80%+ customer satisfaction score
Benchmarked against Goldfire (comparable or better accuracy)
3+ published case studies
50+ validated test cases in industry domains
10+ integration partnerships
Sustainable unit economics (CAC payback < 12 months)

PHASE 4: SCALE & EXPAND (Ongoing 2027+)

Goal: Enterprise-grade platform serving innovation teams globally

4.1 Platform Expansion

Vertical Integration

Add: Specialized tools for specific industries
R&D Suite: Problem + Solution tracking
Innovation Portfolio: Manage multiple projects
Impact Measurement: Track implemented solutions' ROI

Horizontal Integration

Integrations: Jira, Confluence, GitHub, Slack, MS Teams
APIs: Robust API for embedding in customer workflows
Webhooks: Real-time event notifications

International Expansion

Localization: Not just translation, cultural adaptation
Regulatory: Compliance with data laws (GDPR, CCPA, etc.)
Support: Multi-language customer success team

4.2 Advanced Capabilities (Research)

Hybrid Reasoning

Combine:
- TRIZ systematic approach (existing)
- Design Thinking (empathy)
- Lean methodology (rapid testing)
- Systems thinking (complex systems)

Multi-Agent Orchestration

Implement: Multiple AI agents working together
- Domain expert agent (understands industry)
- TRIZ specialist agent (applies methodology)
- Critic agent (evaluates solutions)
- Orchestrator (coordinates)

Knowledge Distillation

Learn: From successful solutions over time
Improve: Principle selection based on user feedback
Personalize: Adapt to company's innovation culture

4.3 Enterprise Features

SSO & Team Management

SAML/OAuth integration for enterprises
Role-based access control
Audit trails for compliance

Advanced Reporting

Analytics dashboard: Problem solve patterns
Custom reports: Board-ready metrics
Export: PDF, Excel, custom formats

SLA & Support

Premium support: 24/7 response times
Dedicated success manager for enterprise customers
Training programs for innovation teams

IMPLEMENTATION RECOMMENDATIONS

Technical Stack (Recommended)

# Backend
Framework:        FastAPI (async, auto-docs)
Database:         PostgreSQL (scalable)
Caching:          Redis (performance)
Task Queue:       Celery (async LLM calls)
Monitoring:       Prometheus + Grafana
Logging:          ELK Stack (Elasticsearch, Logstash, Kibana)

# Frontend
Framework:        React or Vue.js
API Client:       TanStack Query (React Query)
Visualization:    D3.js or Plotly (solution diagrams)

# DevOps
Containerization: Docker
Orchestration:    Kubernetes (Phase 3+)
CI/CD:            GitHub Actions (current) → ArgoCD (Phase 3)
Infrastructure:   Terraform (IaC)

# Testing
Unit/Integration: pytest + pytest-asyncio
API Testing:      Postman / REST Assured
Load Testing:     Locust or k6

Resource Planning

Phase 1 (MVP Foundation) - 3-4 months

Team:
- 1 Technical Lead (full-time)
- 2 Backend Engineers (full-time)
- 1 QA/Test Engineer (full-time)
- 0.5 DevOps Engineer (part-time)
- 0.5 Technical Writer (part-time)
Cost: ~$150-200k (salary + overhead)
Infrastructure: ~$2-3k/month (cloud, LLM APIs)

Phase 2 (Production) - 3-4 months

Add:
- 1 Product Manager
- 1 Security Engineer
- 1 Full-stack Engineer
Cost: ~$280-350k
Infrastructure: ~$5-8k/month

Phase 3 (Market Entry) - 6 months

Add:
- 1 VP Product / CEO
- 2 Sales / BD
- 1 Marketing / Content
- 1 Customer Success
- 1 Data Analyst
Cost: ~$600-750k + sales commissions
Infrastructure: ~$10-20k/month + LLM costs

Funding Recommendations

Phase	Timeline	Funding Needed	Suggested Route
1-2	6-8 months	$300-500k	Bootstrapped or Angel round
3-4	12-18 months	$1-2M	Seed round from deep-tech VCs
Scale	2+ years	$5-10M	Series A from innovation/enterprise VCs

RISK MITIGATION

Risks & Mitigations

Risk	Severity	Mitigation
LLM quality insufficient	High	Start with GPT-4, build rule-based fallback
Market doesn't value TRIZ	Medium	Validate with customer interviews early
Competitors move faster	Medium	Focus on niche (explainability) vs. general
LLM API costs too high	Medium	Implement caching, offer on-prem option
Talent acquisition	High	Build in tech hubs, offer equity
TRIZ experts skeptical	Medium	Include domain experts in development

SUCCESS METRICS SUMMARY

Phase 1 (MVP)

✅ Working end-to-end pipeline
✅ 80%+ principle selection accuracy
✅ 2,000+ lines of tested code

Phase 2 (Production)

✅ 1,000+ problems solved without errors
✅ <5 second average solve time
✅ 99% uptime SLA

Phase 3 (Market Entry)

✅ 1,000+ active users
✅ $50k+ monthly recurring revenue
✅ 80%+ Net Promoter Score

Phase 4 (Scale)

✅ 10,000+ active users
✅ $500k+ annual revenue
✅ 4+ enterprise customers

NEXT IMMEDIATE ACTIONS

Week 1-2: Finalize Phase 1 sprint backlog
Week 2-3: Assign development team
Week 3+: Start with Contradiction Identifier (highest impact)
Parallel: Begin customer discovery interviews

This roadmap is a living document. Update quarterly based on learnings, market feedback, and technical discoveries.

Version: 1.0
Last Updated: December 26, 2025
Next Review: March 2026

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ROADMAP: Heinrich Project Development Plan

Phase Overview

PHASE 1: MVP FOUNDATION (3-4 months)

1.1 Core Pipeline Completion (6 weeks)

Sprint 1-2: Contradiction Identification Engine

Sprint 2-3: Principle Selector with Scoring

Sprint 3-4: Effects Lookup & Scientific Integration

1.2 LLM Integration (4 weeks)

Sprint 4-5: Implement OpenAI Adapter

Sprint 5-6: Implement Anthropic Adapter

1.3 Concept Generator with LLM (4 weeks)

Sprint 6-7: LLM-Based Synthesis

Sprint 7-8: Pipeline Orchestration

1.4 Testing & Validation (Ongoing through Phase 1)

Test Suite Overhaul

Golden Test Suite

1.5 Documentation (Concurrent)

1.6 Phase 1 Success Criteria

PHASE 2: PRODUCTION READY (3-4 months)

2.1 Production Infrastructure

Error Handling & Resilience

Logging & Observability

Configuration Management

Database/Persistence

2.2 API & User Interface

REST API

CLI Tool

Web Dashboard (Optional for Phase 2)

2.3 Deployment & Containerization

Docker

CI/CD Pipeline

Deployment Targets

2.4 Code Quality

Type Hints

Documentation

Testing

Code Style

2.5 Performance Optimization

Profiling

Caching

Async Support

2.6 Security & Compliance

Security Hardening

Data Privacy

Documentation

2.7 Phase 2 Success Criteria

PHASE 3: MARKET ENTRY (6 months)

3.1 Market Research & Differentiation

Competitive Analysis

Customer Discovery

Product Positioning

3.2 Product Enhancement

Domain-Specific Models

Advanced Features

Knowledge Base Expansion

3.3 Validation & Case Studies

Golden Test Suite Expansion

Benchmark Against Baseline

Publication & Credibility

3.4 Pricing & Business Model

Pricing Options

Customer Segments

3.5 Go-to-Market Strategy

Phase 3.1: Beta Launch (Months 1-2)

Phase 3.2: Public Launch (Months 3-4)

Phase 3.3: Growth (Months 5-6)

3.6 Phase 3 Success Criteria

PHASE 4: SCALE & EXPAND (Ongoing 2027+)

4.1 Platform Expansion

Vertical Integration

Horizontal Integration

International Expansion

4.2 Advanced Capabilities (Research)

Hybrid Reasoning