Foible AI — My Mental Model

TL;DR · three lines

My understanding of your thesis. AI generates intent, and has the potential to efficiently connect it to human expertise. But existing incentives aren't aligned — the large labs are optimizing for token-maxing instead of outcome-maxing.
My extension. The durable product is the neutral context-custody layer between AI and human — how AI calls a qualified human with context preserved, trust earned, and outcomes accountable. This is a new category creation play.
My GTM posture. Enter through consumer trust-handoff marketplaces (the break is obvious, buyers are hot). The same primitive scales into autonomous agent commerce and vertical B2B specialist work as AI traffic compounds.

1 · The one-line

My one-line: Foible AI is how AI calls a human. Not an MTurk-type human — a qualified expert, briefed, trusted, and accountable for the outcome.

The secondary framing I keep returning to: the API for human judgment — the layer that turns AI-generated intent into qualified human outcomes.

My interpretation of what we do. The neutral context-custody layer for the agent↔human boundary. We hold the user's context, carry it across the handoff, and verify the outcome — wherever an AI agent needs a qualified human to finish the job.

The wedge, as I see it. Sell the business upside to marketplaces as a way to start building end-user relationships and trust — which we can then scale across different verticals and use-cases.

My unit of sale. Completed outcomes. Not tokens, not leads, not seats.

Where I think we start vs. where we go. V1 lands in consumer trust-handoff marketplaces — hot buyers, no standards play crowding the slot. The same primitive scales into autonomous agent commerce and vertical B2B specialist work as AI traffic grows.

2 · Product architecture — the 2.5-layer model

How I'm drawing the stack: three layers, one neutral middle.

L1 — The AI. ChatGPT, Claude, Alexa+, or a platform's own agent. Captures user intent.
L2 — The human. The provider who actually delivers. Outcome quality depends on what they know on arrival.
L2.5 — Foible AI. The context holder in between. Neutral by necessity — agents won't hold it (lock-in), platforms can't (one-surface view). My analogy here: same slot as Plaid.

Inside L2.5, my decomposition:

Context graph — per-user, portable, compounds every session. The moat.
Agent bridge — carries Foible identity into autonomous sessions where the user isn't in the loop.
Platform SDK — the revenue surface. Intercepts handoffs, enriches them, verifies outcomes.

My assumption on the business model: platforms pay on recovered completions. Users free. Agent vendors are a future third lane.

3 · Market map — the 2×2

How I'm segmenting the space:

	Low-skill unblocker	Qualified specialist
Collaborativeuser in loop	Cell 1 — 2FA prompts (too thin)	Cell 2 — agent escalates to a qualified human (Upwork, Wyzant)
Autonomousagent end-to-end	Cell 3 — headless CAPTCHA (Browserbase owns it)	Cell 4 — agent transacts where verified completion matters (ChatGPT→Resy, ChatGPT→Shopify)

We play Cells 2 and 4. Cells 1 and 3 are not our game.
My read: same handoff primitive across both — one product, two surfaces.
V1 wedge, as I see it. Consumer trust-handoff marketplaces (Cell 2). Cell 4 and vertical B2B specialist work — legal, clinical, creative — expand from the same base as AI traffic compounds.

4 · The four leak types

How I've decomposed where value leaks today:

Discovery leak — agent doesn't find the platform. Out of scope — my view is GEO / AI-SEO tools own this.
Context leak — user context dies at the handoff. Foible AI lives here.
Handoff leak — provider never sees the context, or sees it garbled. Foible AI lives here too.
Verification leak — nominal completion, failed outcome. Foible AI closes the loop here.