Know if an agent pays off before you build it
Most agent projects die in production, not on paper. We model the technical feasibility, unit economics, and failure modes of a candidate workflow first — so you fund the ones that work and kill the ones that don't.
- Per-step automation scoring
- Grounded unit-economics model
- Accuracy & latency spikes
- Go / pilot / no-go call
Five dimensions of a real go/no-go
A feasibility model is only useful if it can say no. We pressure-test the things that quietly sink agent projects after the demo.
Technical feasibility
Can a model actually do this step at the accuracy your process tolerates? We score each step from fully automatable to human-only.
Unit economics
Cost per task at real token usage and tool calls, compared against the fully loaded cost of doing it the way you do today.
Data & retrieval
Whether the context an agent needs is reachable, clean, and fresh — or trapped in PDFs, tribal knowledge, and stale systems.
Integration surface
The real cost of wiring into your ERP, CRM, and APIs — rate limits, write access, and the systems that won't let an agent act.
Risk & oversight
Blast radius of a wrong action, where approval gates belong, and what regulators or auditors will demand of an autonomous step.
Failure modes
How the agent breaks under edge cases, ambiguous inputs, and drift — and whether those failures are recoverable or catastrophic.
Two weeks to a decision
A short, evidence-driven sprint that ends in a number and a recommendation, not a maybe.
Frame
We pin down the candidate workflow, its current cost, and the accuracy and latency it has to clear to be worth automating.
Spike
We build a throwaway prototype against your real data and tools to measure live accuracy, token cost, and integration friction.
Model
We turn those measurements into base, upside, and downside scenarios with confidence bands on every assumption.
Recommend
You get a go / pilot / no-go call, the reasoning behind it, and the build path if it's a go.
Numbers from your stack, not a vendor deck
Anyone can quote a model's benchmark accuracy. It tells you almost nothing about whether an agent will clear your bar on your data, with your edge cases and your tolerance for being wrong.
So we measure. A small spike runs the real task against real inputs and logs accuracy, cost, and latency. The feasibility model is built on those observations — every projection traces back to a number we actually saw, with the assumptions written down where you can argue with them.
- Live accuracy on your own data
- Measured token & tool-call cost per task
- Every assumption stated and challengeable
Gut-feel pilot vs. modeled bet
Why a two-week model is cheaper than the build it might prevent.
| Building on a hunch | Building on a model | |
|---|---|---|
| Trigger to build | A compelling demo | Measured ROI and confidence bands |
| Cost view | Estimated from vendor pricing | Cost per task from a real spike |
| Failure handling | Discovered in production | Stress-tested up front |
| Worst case | Six-figure build that stalls | A clean no-go you funded for weeks, not months |
Frequently asked questions
How is feasibility modeling different from a readiness assessment?
Readiness looks across your org for where agents could help. Feasibility modeling goes deep on a specific candidate workflow — proving (or disproving) that one agent can hit accuracy, cost, and latency targets before you commit a build budget to it.
What does the model actually output?
A decision-grade brief: a per-step automation score, a unit-economics model (cost per task vs. current cost), a confidence band on accuracy, the integration and data risks, and a clear go / pilot / no-go recommendation with the assumptions it rests on.
Do you run a real prototype, or is this on paper?
Both. We build a thin, throwaway spike against your real data and tool calls to measure actual model accuracy and token cost — not vendor benchmarks. The economics in the model are grounded in numbers from your environment, not a slide.
What if the answer is that we shouldn't build it?
Then we just saved you a six-figure build. A no-go with a clear reason is a successful engagement. We'll also point to the adjacent workflow that does pencil out, so you leave with a better bet, not just a dead one.
Bring a workflow. Leave with a number.
A two-week feasibility model that tells you whether to build, pilot, or walk away — grounded in your own data.