Selected Work

Strategy, evidence, and delivery systems for high-stakes science.

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What we do in practice

Representative work and deliverables. Some engagements are public; others are described at the level of method and outputs.

Most teams do not need another idea. They need:

a decision-ready view of reality (data, landscape, pipeline),
a fundable plan (narrative + milestones + budget logic),
a delivery system (roles, cadence, governance),
and, sometimes, a small platform that keeps the program alive after the report.

Below you will find a small portfolio of public and representative work. Some engagements are public; others are shared at the level of method and outputs.

Featured

Breakthrough Energy: climate-tech scouting and venture pipeline

We build evidence-backed scouting funnels with transparent scoring and decision-ready dossiers, so partners can move from “interesting” to “fundable.”

Summary

A structured funnel for TRL 1–5 innovations, from landscape framing to ranked pipeline.

Clients/partners: Breakthrough Energy Coalition

Landscape framing + evidence gates (TRL-based)
Defensible scoring rubric with transparent weights
Ranked pipeline with short decision dossiers

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Preview of featured case study deliverable

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Case studies

Work portfolio

Public and representative engagements. Proof objects are shared when appropriate.

Program observatory platform (internal + public views)

We build lightweight observatories that normalize data, expose decision dashboards, and publish curated public views when appropriate.

2026 PDF Platforms Data systems

Supercomputing ecosystem diagnostics (HPC)

We map stakeholders and constraints, identify opportunity clusters, and turn the findings into an operating roadmap that can actually be executed.

2025 PDF Ecosystem diagnostics Roadmapping

Digital women entrepreneurs in Mexico

We reconstructed the “Nenís” phenomenon using INEGI microdata and platform behavior signals to support policy design and program decisions.

2024 PDF Public sector Data science

PILARES graph big-data model (Mexico City)

We built a graph-based big-data model for the PILARES network to surface patterns, community structure, and operational signals that support better decisions.

2022 PDF Public sector Graph data science

Engagement

How we work

Typical deliverables

Landscape analyses and program diagnostics (evidence, scope, stakeholder reality)
Scoring rubrics and selection pipelines (for cohorts, venture building, or portfolios)
Roadmaps with owners, milestones, and measurable outputs (so plans ship)
Decision memos and narratives for funders and partners (so programs get funded)
Focused platforms: observatories and dashboards that keep the program alive

How projects run

Week 0–1: scope, constraints, decision questions, and evidence plan
Weeks 1–4: data assembly, analysis, and iteration with stakeholders
Weeks 4–8: deliverables, workshops, and handoff into an operating cadence

For platform work, we keep the product surface small and the deployment boring: maintainable systems that survive after launch.

Confidentiality and proof

Some engagements are under NDA. We do not publish sensitive client details. When helpful, we share proof objects privately with qualified partners.

How to engage

Request an intro call and we will confirm fit, urgency, and next steps.