Inception Program
Allium
01
Can agents find you?
Your API or MCP must surface for the customer’s intended task, not only for branded searches.
Simulate how AI agents
choose & use your {API}{MCP}
Know where AI customers drop off from discovery to task completion and which fixes increase conversion & revenue.
Used by 10+ leading API & MCP companies
CoinGecko
Nansen
Quicknode
Helius
Laso Finance
CoinGecko
Nansen
Quicknode
Helius
Laso Finance
Your AI customer journey is leaking conversion at every step.
02
Will they choose you?
Agents compare alternatives and select the API or MCP that appears best suited to the customer’s intent.
03
Can they finish the job?
Documentation, interfaces, parameters, and recovery paths must work before the customer receives the result.
See where agents drop off. Fix what blocks conversion.
Flovia measures and improves every step from discovery to task completion
in one repeatable loop.
38%
24%
27%
34%
19%
36%
14%
41%
22%
39%
See where you win and lose by intent and persona
Benchmark how agents discover and choose your API or MCP across customer intents and personas, including which alternatives lead in each scenario.
Messari updated its endpoint descriptionSelection rate 18% → 31% · Recommended: Test
−6.0 pp
Birdeye listed a new API + MCP serverNew agent surface detected · Recommended: Adopt
−4.2 pp
QuickNode repriced its Pro tierNo shift in your scenarios · Recommended: Ignore
0.0 pp
DexScreener rewrote its landing page for agentsAgent-facing copy changed · Recommended: Avoid
−0.8 pp
Benchmark competitor moves
Track changes to competitor docs, positioning, pricing, APIs, MCPs, integrations, and distribution, then measure how they shift agent discovery and selection.
DiscoveryAPI or MCP surfaced for the target intent
92%
Selectionchosen over relevant alternatives
88%
Executioncorrect interface found and request attempted
61%
Task completionrequired status confirmation was not returned
18%
Reproduce the full agent journey
See how agents discover, choose, and use your API or MCP. Evaluate how fully they complete each task and how well the results meet each persona’s expectations.
Explain how to retrieve final status
Replaces abstract guidance with agent-executable behavior in the payment quickstart.
40
POST /payments41
Returns payment_id42−
Check the payment status until complete.43−
Handle any errors appropriately.40
POST /payments41
Returns payment_id42+
Read payment_id from the submit response.43+
Poll GET /payments/{payment_id} every 2s.44+
Stop when status is confirmed or failed.45+
Return status, transaction_id, and failure_reason.
Apply fix
Generate an executable fix
Turn the failure into a fix for the surface that broke: an API or MCP description, an aggregated endpoint, a task-level MCP tool, or a consolidated execution flow. Your team reviews and implements it.
DiscoveryRun 01 92% → Run 02 92%
0 pp
SelectionRun 01 88% → Run 02 88%
0 pp
ExecutionRun 01 61% → Run 02 64%
+3 pp
Task completionRun 01 18% → Run 02 76%
+58 pp
Retest under equivalent conditions
Run the same evaluation again, report what improved in the tested phase, and identify the next blocker in the journey.
Get your first evaluation free
Start with a 30-minute call. Get findings, supporting evidence, and recommended changes in your private dashboard.
Questions, answered
What does Flovia evaluate?
Flovia simulates the full journey to evaluate whether an AI agent can discover your API or MCP for a target task, select it over relevant alternatives, execute the required steps, and complete the task. It also evaluates whether that completed work converts into paid usage and what it contributes to revenue, not just whether the task succeeds. Each phase is captured and scored separately so the next action matches the actual failure, and each improvement targets the step where the funnel leaks.
How is an evaluation kept comparable?
Every evaluation runs as a simulation across fixed intents, personas, models, and harnesses, sampled with data science techniques so runs stay representative. After a change, Flovia reruns the same task under equivalent conditions and reports the observed phase-level difference.
What can Flovia improve?
Flovia improves the agent-facing surfaces you control: documentation, llms.txt, API and MCP descriptions, and the product path from selection to successful use. The fix matches where the journey breaks, so it can be an aggregated endpoint instead of chained calls, a task-level MCP tool such as get_price, a quote-to-confirm flow consolidated into one call, structured errors with recovery actions, or a free tier shaped for agent experimentation. Flovia cannot change a model’s training data.
Who implements the recommended change?
Flovia provides a concrete remediation prompt and your team reviews and implements the change in its own environment. Enterprise customers can also use GitHub-connected, semi-automated implementation, with their team retaining review and deployment control.
Who is Flovia for?
Flovia is for API and MCP companies that see discovery, selection, and successful use by AI agents as essential to growth. Typical examples are crypto and financial API providers: data, search, execution, swap, and bridge APIs. The primary owner is typically able to set API or MCP direction and mobilize the required changes. Who that is varies by provider, but it is most often a growth lead, product lead, or AI lead.