Size the market for an investment or a new product. Understand upside, competition, and the technical scope required to serve any market.
We size markets with both commercial rigor and technical realism—grounding TAM/SAM/SOM in how AI, data, and infrastructure actually get adopted. You get a clear view of upside, the competitive field, and what it will take to win.
Why market sizing with a technical lens matters
- AI and data markets evolve quickly; static TAM slides age fast. Linking size to adoption curves, latency/quality constraints, and compliance unlocks more credible plans.
- Cost-to-serve and technical feasibility shape how much of a market you can truly capture; ignoring them inflates forecasts and weakens investor trust.
- Competitive moats often come from data and workflow integration, not just features; sizing needs to reflect where you can be defensible.
- Enterprise buyers move in phases; understanding implementation paths and integration complexity changes how and when revenue shows up.
- Investors and acquirers scrutinize whether the product and team can actually reach the stated market—practitioner input keeps the story believable.
How we add value
We blend top-down, bottom-up, and competitor-informed views with a practitioner read on what is technically and operationally feasible. The result is a market model you can defend in the room.
What we deliver
- TAM/SAM/SOM with evidence: Sized markets tied to adoption assumptions, pricing models, and technical constraints.
- Segmentation and ICP clarity: Prioritized customer segments, workflows, and use cases where you have a moat.
- Competitive and substitution view: How incumbents, startups, and DIY alternatives shape the reachable market.
- Adoption and integration paths: Realistic timelines and friction points for enterprise and mid-market buyers.
- Sensitivity scenarios: Upside and downside cases based on pricing, usage, and cost-to-serve dynamics.
How we run the process
1) Thesis and scope (Day 0–2)
Align on the product, target segments, and what “win” looks like; set definitions for TAM/SAM/SOM.
2) Data collection (Day 2–6)
Gather market data, competitor signals, pricing benchmarks, and technical constraints that shape adoption.
3) Model build (Day 5–10)
Construct top-down and bottom-up models, layering in adoption curves, integration paths, and cost-to-serve realities.
4) Competitive and feasibility check (Day 8–12)
Validate against competitor positions, buyer behavior, and what the current team and product can credibly deliver.
5) Scenarios and readout (Day 12–14)
Deliver ranges with assumptions, sensitivities, and a concise summary you can use with boards, investors, or acquirers.
Who this is for
- Founders planning new product bets or preparing to raise with a sharper market story.
- Investors and corp dev teams evaluating AI/data/software targets and wanting defensible market views.
- Product and strategy teams prioritizing segments and needing a realistic path to capture share.
Engagement models
- Fast sizing (1–2 weeks): Rapid TAM/SAM/SOM with key assumptions for a decision or pitch.
- Full sizing and positioning (2–4 weeks): Deep model, competitive lens, adoption paths, and scenarios.
- Ongoing refresh: Periodic updates to keep your market story current as the landscape moves.
