Decision infrastructure
Structured pathways, assumptions, evidence order, and kill criteria.
When the decision is "which feedstock, route, or capex path?" spreadsheets hide assumptions and rank flips stay invisible. I build decision infrastructure: comparable scenarios, traceable economics, sensitivity on what matters, and a prioritized list of what evidence to buy next.
What you get
- ▸Pathway comparison with explicit assumptions (measured vs modeled vs literature bands)
- ▸Rank stability and sensitivity: what flips the answer when prices or yields move
- ▸Evidence-value ordering: which experiment or quote closes the most decision risk
- ▸Buyer and regulatory gates as first-class blockers, not footnotes
- ▸Deliverables boards and diligence teams can use (memos, registers, optional tooling)
Example work
- EcoMetrics
Climate intelligence: validate environmental claims for 75+ SKUs. dbt pipeline with 105+ data quality tests, DuckDB analytics, Streamlit dashboards — transaction-level logic, monthly aggregation models, multi-company architecture.
- RENW Platform
Logistics and operations at enterprise scale: unified shipping engine for 1K–100M units, piecewise volume discounts, multi-modal transport, geospatial port detection (Haversine), EPR integration, 75+ SKU catalogue — production BI with Prometheus/Grafana.
- KineticCRM
Agentic AI systems: CRM where autonomous agents manage leads and workflows — multi-strategy error recovery (~85%), graph-based workflow execution, conversation analysis, and 151+ automated tests.
Start a conversation
Selective conversations with founders, operators, and diligence teams. Mention decision infrastructure or pathway diagnostic in your note so we can route quickly.
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