Jeremy Achin
General Partner
Cortical Ventures
Jeremy Achin is a General Partner at Cortical Ventures and co-founder of DataRobot, with a background in building applied AI companies.
Cortical Ventures is an AI-first early-stage venture firm focused on production-grade AI systems, core infrastructure, and real-world deployments rather than hype-driven demos. The firm leads pre-seed and seed rounds for deeply technical teams building across physical AI, AI infrastructure, and mission-critical applications in areas like defense, health, safety, and enterprise workflows.
Evaluation weights
How much weight this firm places on each dimension. Totals 100%.
Team-led · 42%
Metrics
5%
Revenue, growth, and unit economics
Market
25%
Size, timing, and competitive landscape
Team
42%
Founder experience and execution ability
Product
28%
Differentiation and technical quality
- Strong bias toward technical founders over commercial-only teams
- Prefers production evidence over narrative ambition
- More receptive to AI infrastructure and physical AI than consumer AI hype
- Comfortable with frontier and regulated markets if execution quality is exceptional
Pitch difficulty
How hard it is to get a meeting and close funding from this firm.
Funded / yr
2
Deals closed in a typical year.
Led / yr
2
Rounds led in the last 12 months.
Pitches / yr
~156
Decks reviewed in a typical year.
Acceptance rate
1.3%
Share of pitches that get funded.
Estimated — public data is not fully disclosed.
Why it's hard
- Strong preference for deeply technical, operator-grade founding teams
- Rejects hype-driven or demo-only AI startups
- Concentrated focus on AI infrastructure, physical AI, and mission-critical applications
- Often seeks to lead rounds, which raises the conviction threshold
Cortical is aggressive at the earliest stages and willing to underwrite meaningful technical risk, but its bar is high on founder quality and production credibility. It is not broad-based or theme-agnostic; companies need to fit a specific AI-first, execution-heavy thesis to win conviction.
Green flags
What drives a yes for this firm.
- Deeply technical, operator-grade founders with strong AI credibility
- Clear path from prototype to production with tangible deployment evidence
- Potential to become foundational infrastructure or a category leader in the AI stack
- Mission-driven use case with real-world urgency, especially in defense, safety, or health
- Proof that the product works beyond a demo via contracts, OSS pull, pilots, or revenue
Red flags
What kills deals and gets a fast no.
- Pitching a flashy AI demo without a credible production roadmap
- Founding team lacks substantive AI or systems depth
- Generic LLM wrapper with weak differentiation or no defensibility
- No evidence of customer pull, deployment, or practical commercialization
- Hype-first storytelling that substitutes for execution details
How to win
Patterns that lead to successful pitches.
- Show technical founder credibility and why this team can ship production AI
- Lead with deployment proof: pilots, contracts, OSS adoption, or early revenue
- Position the company as infrastructure or a deeply embedded workflow/system, not a novelty app
- Demonstrate reliability, security, and real-world readiness in demanding environments
- Frame the opportunity around meaningful impact plus venture-scale market potential
Fund strategy & identity
Who they are and how they operate.
- Lead or co-lead Pre-Seed and Seed rounds in AI-native companies
- Back production-grade AI infrastructure and vertical systems with strong technical moats
- Underwrite technical and go-to-market risk for exceptional operator-founders
- Prefer companies with evidence of deployments, contracts, OSS adoption, or early revenue
- Make selective follow-on investments in later-stage category-defining AI platforms
Firm identity
AI-first specialist investorEarly-stage lead or co-leadOperator-led and execution-orientedProduction-over-hype convictionUS-centric with selective global reach
Investment focus
Industries, themes, and typical ARR expectations.
Industries
AI InfrastructureDeveloper ToolsData InfrastructureRobotics & AutomationDefense & AerospaceCybersecurityAI/MLEnterprise SoftwareHealthcare
Investment themes
Physical AI across robotics, autonomy, and defense hardwareThe AI production gap: tools and pipelines that move models into reliable productionCore AI infrastructure including compute, vector databases, agent sandboxes, and data pipelinesEnterprise AI applications embedded into real workflows rather than standalone demosAI for Good in defense, safety, health, and human connectionMission-critical systems operating in regulated or real-world environments
Typical check by stage
Pre Seed$500K-$1M
Seed$1M-$2M
Series A$1M-$3M
Typical ARR by stage
Pre Seedpre-revenue or pilots
Seed$0-$1M
Series A$1M-$5M
Investment thesis
Core beliefs and strategy behind their investing approach.
Decision patterns
How they evaluate and make investment decisions.
Notable investments
Key portfolio companies and why they fit the thesis.
Co-invested with
Other firms in this catalog who've backed the same companies.
Partners
Full firm roster — key partners, partners, and the wider team.
Key partners
Igor Taber
General Partner
Cortical Ventures
Igor Taber is a General Partner at Cortical Ventures, an AI-focused early-stage firm, with prior public background connected to DataRobot and Intel Capital.
Mike Obolonskyi
Principal
Cortical Ventures
Mike Obolonskyi is listed in public sources as a Principal at Cortical Ventures focused on AI and robotics. Cortical Ventures describes itself as an AI-focused venture firm interested in areas including AI pipelines, agentic infrastructure, vertical AI-first workflows, AI-driven human engagement, and defense tech.
Sheamus McGovern
Venture Partner
Cortical Ventures
Sheamus McGovern is a Venture Partner at Cortical Ventures and is publicly associated with AI, data science, and AI community-building through ODSC AI. Public sources describe him as founder of ODSC AI and Head of AI at Cortical Ventures.
Public voice
Notable statements and public positions.
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