Decision Architecture for Deep Tech & Green Tech
Designing the systems through which complex decisions get made
Not just patterns for scaling—reusable decision infrastructure. Formalizing how technical risk gets evaluated, how strategic options get compared, and how organizations navigate uncertainty when capital, regulation, and scale intersect.
Get in touch →Deciding Under Uncertainty
The real bottleneck isn't building—it's deciding.
Formulas that work at 1K units break at 100K, so you can't confidently choose.
Quick validations fail audits, so you don't know what's defensible.
Automation locks you in before you know if it works, so the decision architecture itself is wrong.
Industries we serve
Decision environments that break
These aren't unique problems. Every company at this stage hits the same walls. The difference is having decision infrastructure that scales.
How I Help
Decision architecture, data systems, operations frameworks, and integration patterns—reusable infrastructure so your decisions scale.
Context
You need to calculate shipping costs for 1,000 units, but your formulas fail at 100,000. Volume discounts, port selection, regulatory compliance, and multi-modal transport need to work at enterprise scale. But you're building MVP.
Solution
Portable logistics calculation engine using piecewise linear interpolation for pricing tiers, Haversine distance calculations for geospatial optimization, and automated compliance checks. Handles 1K to 100M units with mathematical precision.
Benefits
- ✓Scales from MVP (1K units) to enterprise (100M units)
- ✓Mathematical precision for complex pricing tiers
- ✓Cloud-agnostic (works on AWS, GCP, Azure)
- ✓Geospatial optimization for multi-facility operations
- ✓Automated regulatory compliance calculations
Patterns
Context
You have 75+ SKUs with supplier claims, but manual validation takes weeks. You need confidence scores, data quality gates, and compliance evidence. But you're drowning in spreadsheets.
Solution
Automated data quality pipeline with 105+ validation tests, expert domain normalization, confidence scoring, and evidence bundles. Validates claims in minutes, not weeks, with 99.7% accuracy.
Benefits
- ✓105+ automated data quality tests
- ✓99.7% data accuracy with confidence scoring
- ✓Validates 75+ SKUs in minutes vs. weeks
- ✓EPR compliance evidence bundles
- ✓Reduces greenwashing risk
Patterns
Context
You built workflows in Google Apps Script or Zapier, but now you're locked in. The business logic is scattered, untestable, and can't move to another cloud provider. You need portability without rebuilding.
Solution
Three-layer architecture: simple triggers call portable Docker services. Business logic lives in version-controlled, testable containers. Works on Google Cloud, AWS, Azure, or DigitalOcean. Same code, different trigger.
Benefits
- ✓Zero vendor lock-in (works on any cloud)
- ✓Testable business logic (unit & integration tests)
- ✓Version-controlled changes (Git-tracked)
- ✓Scales to zero (Cloud Run, Lambda, Functions)
- ✓Simple triggers (Apps Script, Cron, Webhooks)
Patterns
Context
Your team works in spreadsheets (human-friendly), but your services need structured data (machine-friendly). Manual sync creates errors, version conflicts, and delays. You need bidirectional sync with conflict resolution.
Solution
Bidirectional sync pattern: Excel → Datastore (for fast reads), Datastore → Excel (for updates). Conflict resolution with last-write-wins and manual review. Excel stays human-editable, services get programmatic access.
Benefits
- ✓Excel remains master (human-editable)
- ✓Services get fast reads (from datastore)
- ✓Services can update assumptions programmatically
- ✓Automatic conflict resolution
- ✓Version tracking and audit trail
Patterns
Context
You're growing fast, but processes are tribal knowledge. New hires don't know procedures. You need SOP frameworks for Grants, Partnerships, Site Selection, Financial Modeling, Market Intelligence, Technical Sales, Product Validation, Sustainability, Regulatory Intelligence. But where do you start?
Solution
Comprehensive SOP framework organized into 10 functional areas with purpose, scope, procedures, outputs, quality standards, and scaling considerations. Plus executive capability mapping for clear ownership and delegation.
Benefits
- ✓10 functional areas with complete SOP templates
- ✓Clear ownership and delegation guidance
- ✓Quality standards and guardrails
- ✓Versioning and review process
- ✓Scales from 5 to 50+ team members
Patterns
Context
Your team relies on workspace platforms (Google Workspace, Microsoft 365, Notion, etc.) for daily operations. You need automation, but platform-specific scripts become unmaintainable. You want proper services, but don't want to abandon your team's workflow. You need integration patterns that scale.
Solution
Workspace-agnostic integration architecture: Platform triggers (Apps Script, Power Automate, webhooks) call containerized services. Direct API access via service accounts. Document and data management through platform APIs. Credentials managed securely. All business logic stays in version-controlled services.
Benefits
- ✓Works with any workspace platform (Google Workspace, Microsoft 365, Notion, etc.)
- ✓Platform triggers call powerful containerized services
- ✓Direct API access via service accounts
- ✓Document management and automation across platforms
- ✓Credentials managed securely, business logic version-controlled
Patterns
Context
You built MVP with SQLite (zero setup), but now you need concurrent access, better performance, and cloud deployment. Traditional migration is painful. You'd rewrite queries, change connection logic, and risk data loss.
Solution
Portable database architecture with abstract interface. Same code works with SQLite (MVP) and PostgreSQL (production). Simple export/import migration. No code changes, just connection string swap. Works on any cloud provider.
Benefits
- ✓Start with SQLite (zero setup, single file)
- ✓Migrate to PostgreSQL when needed (simple export/import)
- ✓Same code works with both databases
- ✓No vendor lock-in (works on AWS, GCP, Azure)
- ✓Abstract interface (zero code changes on migration)
Patterns
Context
You have source files, tracking sheets, quick reference docs, and detailed frameworks. But nobody can find anything. Information hierarchy is unclear. You need a system that scales from 10 to 1,000+ documents.
Solution
Information hierarchy framework: Level 1 (Quick Reference), Level 2 (Verified Facts), Level 3 (Detailed Frameworks), Level 4 (Source Files). Clear information flow, maintains single source of truth, supports both human and AI consumption.
Benefits
- ✓Clear information hierarchy (4 levels)
- ✓Easy to find what you need
- ✓Maintains single source of truth
- ✓Supports both human and AI consumption
- ✓Scales from 10 to 1,000+ documents
Patterns
Solution categories
Architecture
Portable systems that scale from pilot to production.
See services →Data
Validation, sync, and single source of truth.
See services →Operations
SOPs, information hierarchy, scaling playbooks.
See services →Integration
Workspace + services without vendor lock-in.
See services →This work is delivered through my services: Development, Consulting, and Decision Engines.
Let's discuss your context →Ready to Design Your Decision Systems?
These frameworks formalize tacit judgment into repeatable systems. Production-tested and portable— designed to scale your decision environment, not just your operations.
What You Get:
- ✓ Decision architecture—not just technical patterns
- ✓ Evaluation frameworks that formalize judgment
- ✓ Cloud-agnostic, portable designs
- ✓ Information architecture for better decisions
- ✓ SOP frameworks that codify process
- ✓ Integration patterns for your stack
How We Work:
- ✓ Design review—does your decision architecture scale?
- ✓ Customization for your uncertainty profile
- ✓ Integration with existing decision workflows
- ✓ Knowledge transfer—reusable, not dependent
- ✓ Iteration as constraints evolve