Autonomous Resolution
Autonomous resolution is a graduated ladder, not a switch: Sainapse predicts in shadow mode, drafts in suggest mode, and acts alone in auto-apply mode only where calibrated confidence has held above your threshold. Each category is proven on your own tickets before it climbs, never switched on by a launch date.
Resolution finishes the ticket. Deflection just stops it.
Autonomous resolution means Sainapse completes the ticket end to end: it reads the ticket, decides the action, writes it into your systems of record, and closes the loop.
That is different from a bot that redirects the customer to a help article and calls the ticket closed.
Sainapse names this vocabulary on purpose. A deflection rate counts tickets a customer gave up on. A resolution rate counts tickets a customer never has to reopen, verified against what changed in your systems, not against a survey.
Each category climbs a visible ladder.
Shadow
Sainapse predicts the resolution and logs it, but nothing reaches the customer or the ticket yet. This rung exists to measure accuracy against real tickets before anything acts.
Suggest
Sainapse drafts the resolution and your agents send it. Every accepted draft and every correction becomes calibration data for the next rung.
Auto-apply
Sainapse acts alone, only in categories where calibrated confidence has held above your threshold, with an independent judge model reviewing every output before it ships.
Three capability classes, one difference
Most vendors sell one of two shapes: a deflection bot that redirects the customer, or a suggest-only copilot that drafts and waits. Sainapse is the third shape: a resolution engine that climbs from shadow to auto-apply, per category.
| Deflection bots | Suggest-only copilots | Sainapse | |
|---|---|---|---|
| What ships | A self-serve link or macro | A drafted reply for a human to send | A ladder that climbs from shadow to auto-apply per category |
| What counts as done | The customer stopped asking | A human clicked send | The system of record changed |
| Where autonomy starts | Day one, everywhere | Never; always a human in the loop | Only where calibrated confidence has held above threshold |
| How it's proven | Rarely published | Draft-acceptance rate | A per-category accuracy ladder, published |
What runs today, what stays supervised
Sainapse publishes the ramp instead of a day-one number.
No category starts at full autonomy.
On the Avery Dennison document-intelligence deployment, the zero-touch order rate climbed 26% → 71% over 13 months while monthly volume grew ~13×; the header field-edit rate fell 8.4% → 1.37% over the same period.
That is what a real ramp looks like: measured per category, published with the dates, and reversible the moment the numbers move the wrong way. It is not a fixed percentage promised before go-live.
Common questions
No, and any vendor claiming that is describing a demo, not production. Every category starts in shadow mode, moves to suggest once accuracy holds, and reaches auto-apply only where calibrated confidence has stayed above your threshold. The ramp is measured and published, not assumed.
Deflection means the customer gave up asking, and a redirect to a help article counts as success. Resolution means the ticket is closed: the system of record changed, and the customer doesn't have to reopen it. Sainapse measures the second definition.
Your admin does. The confidence threshold per category is a setting your team controls, not a promise Sainapse makes on your behalf. A category can also move back down the moment accuracy drifts; the ladder runs in both directions.
Only if you decide they should. Nothing forces a category up the ladder. Routine categories tend to climb first, and a category with real downside risk for your business can stay in suggest mode indefinitely, by your own choice.
An independent judge model reviews every auto-apply output before it reaches a customer, separate from the model that produced the resolution. If confidence dips or the judge model disagrees with the proposed action, the ticket routes to a person instead of going out, and the disagreement itself feeds the accuracy record that decides whether the category keeps its auto-apply rung.
Where this fits
- Independent judge model
The review layer that checks every auto-apply output before it reaches a customer.
- When a person stays in the loop
How exceptions and low-confidence cases route to a reviewer instead of auto-applying.
- Writing back to your systems
The write layer that turns a decided resolution into a real record change.
- Learning from every correction
How each resolved ticket becomes calibration and future context, not a one-off answer.
- Full ticket lifecycle automation
The use case this ladder was built to run: intake through resolution, one pipeline.
- Customer Support workflow
Where autonomous resolution sits inside the rest of the support workflow.