who is the target user ICP for ecorouter - provide relevant user story examples and help me to estimate the market oppor

Happy to help you think through this. But first, a caveat: I don't have verified data about EcoRouter's actual customer base, positioning, or any market research the team may have done. What follows is a reasoning framework and illustrative examples — not facts about EcoRouter's real users or a validated market estimate. You'll need to ground these against your own data before using them.

Who the ICP likely is

Given the product framing (routing tasks to the smallest sufficient amount of AI), the natural candidates are:

1. Cost-conscious engineering/product teams running many LLM calls at scale, feeling the pain of a rising inference bill.
2. Sustainability-minded organisations that want to reduce the compute/energy footprint of their AI usage.
3. Platform/infra teams who want a routing layer so they don't hand-roll model selection logic.

The strongest ICP is usually the intersection: a team with high call volume, budget accountability, and someone who owns cost or efficiency as a metric. Sustainability alone rarely drives purchase unless tied to cost.

Illustrative user stories

• "As a platform lead at a mid-size SaaS company, I want simple tasks routed to cheaper/smaller models so my monthly inference bill drops without hurting output quality."
• "As a startup CTO, I want a drop-in routing layer so I don't have to maintain my own model-selection heuristics as new models ship."
• "As a head of AI at an enterprise, I want to report reduced compute footprint to meet ESG commitments while keeping latency acceptable."

Note these are hypotheses to test, not confirmed use cases.

Estimating market opportunity at scale

I can't give you a real number, but here's a defensible way to build one:

Bottom-up (recommended):

SAM = (# of orgs running production LLM workloads in your reachable segment)
      × (avg annual AI spend per org)
      × (% of that spend addressable by routing/savings capture)

The last term is key: your revenue is typically a slice of savings, so the addressable pool is a fraction of total inference spend, not the whole thing.

To fill this in, you'd need:

• Count of target orgs (by size band / vertical) — from firmographic data.
• Avg inference spend per org — hardest input; needs surveys or usage benchmarks.
• Realistic savings capture % and your pricing model (subscription vs. % of savings).

Top-down (total AI inference spend × your reachable %) is fast but easy to inflate — use it only as a sanity check.

To make this concrete rather than generic, I'd need from you:

1. Which segment/geography you're targeting?
2. Your pricing model (flat fee, seat, % of savings)?
3. Any real usage or customer data you already have?

Want me to build out a sample bottom-up model with placeholder numbers you can replace?