Frontier accuracy. A fraction of the run cost.
Run the numbers on your workload in under a minute. No card, no code.
$150–600+ on the frontier reflex
$2.10–6.50, right-sized
90–97%
93–97%
near zero
accuracy falls to ~70–93%
after you ship
before you build
Modelled on real workloads, not marketing. The failure library (5M+ patterns) decides when a leaner model and architecture are sufficient; exhaustive pre-ship validation proves it, path by path, before the agent exists. Bands vary by task — every agent ships with its own cost justification.
Run the numbers on your workloadLower cost. Same accuracy. Proven first.
93–97% accuracy band
Inside the frontier's agentic band (90–97% on coding benchmarks) — for tasks the failure library shows are safe to right-size. Bands, not cherry-picked points.
5M+ failure patterns
Every class of agent failure mapped from real behaviour: retry spirals, silent success, cost runaway. A pattern mapped once is caught everywhere.
Every path tested pre-ship
One check per valid path — 17,568 on a recent build — before code exists. The expensive model is reserved for what actually needs it.
Stop building first and praying.
Build first, meet the failures in production.
See the failures on the goal, before a line of code.
Evals run after you have already built the wrong thing.
The check runs before the spend, while it is still cheap.
Every new agent relearns the same failures from scratch.
A failure class is mapped once and caught in every domain.
Send private goals and data to a frontier lab to find out.
Runs on your side, on your data, before you commit.
"Should we build this at all?" costs a burned sprint to answer.
Answered before sprint one, in the design phase.
Run the arithmetic on your workload.
Frontier reflex, today
$600K/mo
unvalidated paths, biggest model every call
With Pavamana
$4K–$13K/mo
right-sized architecture, every path tested first
You save
$587K–$596K/mo
at 93–97% accuracy — the band, not the cliff
Modelled, not measured. Based on the failure library deciding when a leaner model and architecture are sufficient, and exhaustive pre-ship validation proving it. Bands vary by task; your agent ships with its own cost justification. Figures are approximate; actual costs vary by workload, model, region, and provider terms.
See the full economics story — methodology, sources, and the production plan
Three steps. One stays free forever.
- 01Read CRM recordMapped safe
- 02Call refund APILikely break
- 03Update ticket recordStructural risk
- 04Hold multi-turn stateLikely break
- 05Pass context to modelStructural risk
Two teams are already mapping real agents.
Kezzler
BuSoft
Runs before the tools you already use
Every eval and observability tool starts after you build. Faultmap starts one step earlier.
The skeptical builder questions.
The pre-build AI agent doctor. We map where your agent will break before you build it, prescribe the fix, and forge the agent that passes.
A failure map of where your agent breaks, plus the first test suite it has to pass, generated from your goal and data before any code.
No. It is built for the pre-build moment, where a fix costs cents instead of thousands. The same failure-pattern library also diagnoses and fixes agents already breaking in production.
They start after you build. We start before. We map where the agent breaks before a line of code exists.
It runs on your side in the design phase. No code, prompts, or traces leave your environment.
The library is built from real agent behaviour across production builds and public failure corpora: retry spirals, silent successes, tool-permission blast radius, cost runaway, and every class in our failure catalogue. Each pattern is a concrete failure signature with its root cause, pre-build signal, and a test that catches it. We publish the taxonomy and the counting method in the Hard 70% series; the honest number is "five million and growing" — the moat is the classes, not the count.
Routing picks a cheaper model at runtime. It does not prove the task still succeeds. We prove it before the build: the failure map, the path enumeration, and the test suite exist before code does, and the cost quote is generated from the same map. Routing is one technique inside a right-sized architecture; the proof is the product.
For tasks where the failure library shows a leaner model and architecture are sufficient — which is most production agent steps — yes, and we show the band, not a cherry-picked point: 93–97% inside the frontier's 90–97% agentic band, at roughly 20–200×+ lower run cost. Tasks that genuinely need frontier reasoning get routed there, and the quote says so before you commit. Every agent ships with its own cost justification, so renewal is a report, not a pitch.
Because the alternative is six figures: $55–110K of build labour, $150–600+ per 1K requests on unvalidated frontier paths, and $20–100K a year fixing what breaks in production — for one agent. $10K buys three production agents built and kept cost-efficient, with savings baselined from day one.