Consumer agents at volume, without the runaway bill
Evigauge
Direct to consumer · Use case
Consumer AI lives or dies on unit economics. Evigauge prices every interaction and tests the agent before customers do.
Direct to consumer
Consumer AI is a unit-economics problem with a security problem attached. Both are measurable before they are expensive.
The situation
Support agents, shopping assistants, returns, personalisation and outbound messaging — high volume, low margin, directly in front of customers.
At that volume, cost per interaction is a board number. So is what happens when someone talks the agent into something it should not do.
Where the value leaks
Three costs land before anyone files a ticket. None of them shows up as a failure, which is exactly why they persist.
| Cost | Where it goes |
|---|---|
| TIME | Cost questions take a week to answer Finance asks what a conversation costs. Engineering builds it from raw logs, and the answer is stale on arrival. |
| COST | Context sent repeatedly, in full The same history is re-sent every turn. Prices fall, volume rises faster, and the bill grows either way. |
| RISK | Hostile inputs reach the agent A public agent is probed constantly. Untested before release, the first report comes from a customer or a journalist. |
One answer, beat by beat
A single run, with the record Evigauge writes at each step and what that record is worth once it exists.
- 01
Customer message· Journey opened
Cost is attached to a customer journey from the first turn.
- 02
Context assembled· Context compressed
A materially smaller token bill on context-heavy calls.
- 03
Model call· Cost per turn
Finance reads a live figure instead of a quarterly reconstruction.
- 04
Safety check· Injection flags
Hostile inputs are found in review, not by a journalist.
- 05
Reply sent· Fleet-wide view
Peak-season spend is visible while it happens.
What we bring to it
Each capability, what it does to the run, and the result the business can hold someone to.
| Offering | What it does | What you get |
|---|---|---|
| Cost & speed | Prices every trace to the token and attributes spend per journey, stage and model. | Cost per conversation is a live number finance can read, not a quarterly reconstruction. |
| pxcore compression | Compresses dense context before it reaches the model, with a recalibration layer per provider. | A materially smaller token bill on context-heavy calls, with the answer preserved. |
| Prompt-injection testing | Tests the agent and tool surface against prompt-injection at pull-request time and flags over-broad permissions. | The hostile-input problem is found in review rather than in public. |
| Fleet monitoring | Shows token and cache consumption across the whole consumer fleet as traffic moves. | Peak-season spend is visible while it happens, not after it lands. |
What actually changes
Cost per conversation is a quarterly estimate
A live figure, attributed per journey
Full context re-sent on every turn
Compressed before it reaches the model
Injection found by a customer
Found at pull request, before release
Around the table
The question each officer is asked, and the answer this puts in their hands.
CFO
“What does each AI conversation cost us?”
A live number per journey, with the drivers named.
Chief Digital Officer
“Can we put this in front of every customer?”
The hostile-input surface is tested before release, not after a report.
VP Customer Experience
“Will quality hold at peak?”
Fleet-wide consumption and failure rates are visible while traffic is running.
Consumer AI is a unit-economics problem with a security problem attached. Both are measurable before they are expensive.
Ready to see it on your own workload?
Bring one real run. We will show you what happened.