Assess the value of an investment, or value your startup the way VCs will.
We build valuations grounded in both market reality and technical rigor. Whether you are raising, selling, or investing, we surface the drivers that actually move enterprise value in AI and software—moat, team, cost-to-serve, and path to defensible growth.
Why rigorous valuation matters
- AI and infra-heavy companies can look margin-rich but hide expensive inference, training, and data costs; valuations swing on real unit economics.
- A few technical details—latency, data rights, scalability, reliability—decide whether forecasts are believable. Financials alone miss them.
- Comparable sets and multiples move quickly; tying them to actual capability and market position keeps you from overpaying or underpricing.
- Boards and investment committees want clarity on risk and upside. Structured scenarios help align on price, structure, and post-close plans.
- Credible valuations shorten negotiations and keep optionality; sloppy ones erode trust and invite heavy protections from the other side.
How we add value
We combine practitioner diligence with investor modeling. We translate technical and product realities into scenarios, metrics, and deal structures you can defend.
What we deliver
- Valuation ranges and rationale: Scenario-based view (base, upside, downside) tied to technical moat, team strength, and cost-to-serve.
- Comparable analysis: Relevant comps and benchmarks with adjustments for AI/infra intensity, go-to-market, and growth profile.
- Unit economics and margin view: Realistic cost-to-serve (inference/training), cloud efficiency opportunities, and path to target margins.
- Risk and mitigation map: Technical, product, and team risks with recommended structures (earnouts, holdbacks, covenants) to protect value.
- Investment memo inputs: IC-ready summary with a red/yellow/green view across tech, market position, and operating readiness.
How we run the process
1) Thesis and purpose (Day 0–2)
Align on why you need the valuation (raise, sale, investment) and what levers matter most to your thesis.
2) Technical and product scan (Day 2–7)
Assess moat drivers: data advantage, latency/reliability, integration surface, roadmap credibility, and leadership strength.
3) Unit economics build (Day 5–9)
Model cost-to-serve, efficiency potential, and margin trajectory under realistic adoption and usage patterns.
4) Market and comps (Day 7–10)
Select and adjust comps; benchmark metrics and growth profile against relevant peers.
5) Scenarios and structures (Day 9–12)
Develop valuation ranges with deal structure options that balance risk and upside.
6) Readout and IC support
Deliver the valuation pack and stand with you in IC or board discussions as needed.
Who this is for
- Founders preparing to raise or sell and wanting a defensible valuation story.
- Investors and corp dev teams pricing AI, data, or software targets where technical depth drives value.
- Boards seeking an independent view before approving a transaction.
Engagement models
- Valuation sprint (1–2 weeks): Focused range, comps, and margin view for a specific transaction.
- Full valuation plus diligence (2–3 weeks): Combined technical/product assessment with valuation and structure recommendations.
- On-call support: IC and negotiation support to defend assumptions and structures.
