Matt Coburn

$2M+ revenue as AI founder | Production ML at Fortune 100 | Teams of 15+

I build AI products that generate revenue. $2M+ as technical co-founder. Production ML at 2 of top 3 global automakers and Fortune 100 financial institutions. I take products from zero to revenue as a player-coach (70/30 coding/management).

Key Results

  • $2M+ enterprise revenue as founder (AutoExtract.ai, SafeScan)
  • $200M+ annual ad spend optimized at Expedia
  • 240% ROI improvement on underperforming ad inventory
  • Fortune 100 clients: MasterCard, Fannie Mae, Freddie Mac, 2 of top 3 automakers

Tangible Intelligence — Founder & CEO

Dallas, TX | Jan 2020 – Apr 2023

Founded an AI document-intelligence company from scratch and grew it to ~$2M enterprise revenue in two years. Built the products, hired the team, sold to Fortune 100, and led SOC 2 certification.

What I built:

  • AutoExtract.ai — No-code HITL extraction platform with ontology-driven schemas, deterministic entity resolution, and click-to-source provenance. Sold to MasterCard, Fannie Mae, Freddie Mac.
  • SafeScan — Real-time sensitive-data and anomaly detection system combining ML, rules, and search for compliance workflows.

Leadership: Scaled and managed an 8-person engineering/data team. Led SOC 2–aligned deployments and enterprise sales cycles.

Aristotle — Founding Engineer & Tech Lead

Los Angeles, CA | June 2024 – Present

Own technical direction end-to-end for a zero-to-one enterprise document intelligence platform. Built core backend, set engineering standards, hired/led 4 engineers. Shipped to production for 2 of the top 3 global automakers.

Key contributions:

  • Ontology & entity system: Deterministic core entities (People, Orgs, Events, Relationships) in Pydantic + Postgres with strict identity semantics
  • Document → fact → provenance pipeline: Structured extraction with click-to-source spans/pages; ambiguity forced into explicit review flows
  • Grounded LLM UX: Streaming answers with verified vs pending states, citations for assertions/quotes, and HITL review tools
  • Agent runtime: Tool-using agents as explicit state machines with boundary validation and retry/escalation semantics
  • Engineering leadership: Established CI/CD, code review standards, release gates, observability, and production-readiness rituals

WorkFusion — VP of Data Science

New York, NY | 2023 (recruited to ship applied AI; departed when company pivoted strategy)

Joined as hands-on VP to lead applied AI in regulated financial environments. Led ~15 data scientists while staying hands-on in system design.

  • Shipped KYC/AML systems (transaction monitoring, risk scoring, case prioritization)
  • Built LLM-based document extraction & auto-labeling for high-volume IDP workflows with strict governance
  • Designed security-first MLOps, evaluation, and audit practices for regulated production

M Science (Jefferies subsidiary) — Principal Data Scientist, Founding Lead

New York, NY | May 2018 – Dec 2019

Conceived, architected, and shipped an ontology-driven knowledge platform as an internal startup. Hired and led an independent team, shipped to production. Clients: BlackRock, Two Sigma, Citadel.

  • Built Ontology-as-a-Service layer modeling companies, securities, events, and relationships across public + proprietary data
  • Designed entity resolution rules and relationship semantics prioritizing deterministic identity over heuristic joins
  • Built application layer for clients to apply shared ontology semantics to private datasets with isolation and access control

Expedia Group (Hotels.com) — Data Scientist

Dallas, TX | Oct 2016 – May 2018

Worked on large-scale search/ads systems with models and pipelines influencing $200M+ annual spend.

  • Built models estimating expected value of clicks across millions of keywords, enabling daily automated bid optimization
  • Identified systemic underperformance across publisher inventory; reallocated spend, yielding ~240% ROI improvement
  • Built revenue-critical production pipelines with monitoring and safeguards

Technology Stack

Core: Python, SQL, TypeScript, C, Zig LLMs & AI: LLM agents, knowledge graphs, RAG architectures, PyTorch, HuggingFace, OpenAI/Anthropic APIs Backend: FastAPI, Pydantic, PostgreSQL, Redis, Docker, Kubernetes, AWS Data & Infra: Pandas, NumPy, Spark, ETL/streaming pipelines, vector DBs Specialties: Document intelligence, structured extraction, ontology design, AIUX, evaluation frameworks, production ML governance

Education

University of Texas at Dallas

  • B.S. - Electrical Engineering, 2013
  • Masters Coursework - Computer Science, 2016

Let's Talk

Seeking VP/Head of AI roles at mission-driven companies where I can take products from zero to revenue.

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