--- title: "Sainapse: Autonomous Resolution" description: "Autonomous resolution is a graduated ladder (shadow, suggest, auto-apply), proven per category, with the accuracy ramp published, not promised." canonical_url: "https://www.sainapse.ai/features/autonomous-resolution" markdown_url: "https://www.sainapse.ai/features/autonomous-resolution.md" text_url: "https://www.sainapse.ai/features/autonomous-resolution.txt" archetype: "feature-leaf-deep" --- # Sainapse: Autonomous Resolution Customer Support ## 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. On this page: - [What it is](https://www.sainapse.ai/features/autonomous-resolution#features_autonomous_resolution_03) - [How it works](https://www.sainapse.ai/features/autonomous-resolution#features_autonomous_resolution_04) - [Capability classes](https://www.sainapse.ai/features/autonomous-resolution#features_autonomous_resolution_05) - [Thresholds](https://www.sainapse.ai/features/autonomous-resolution#features_autonomous_resolution_06) - [The ramp](https://www.sainapse.ai/features/autonomous-resolution#features_autonomous_resolution_07) - [Questions](https://www.sainapse.ai/features/autonomous-resolution#features_autonomous_resolution_08) Updated August 2026 What it is ## 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. How it works ## Each category climbs a visible ladder. ### Shadow Rung 1: 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 Rung 2: Sainapse drafts the resolution and your agents send it. Every accepted draft and every correction becomes calibration data for the next rung. ### Auto-apply Rung 3: 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 - **Sequence**: Routine categories climb first; high-stakes categories stay in suggest mode for as long as you choose - **Reversibility**: The ladder runs in both directions: a category drops back to suggest the moment accuracy drifts - **Threshold**: A confidence floor your admin sets per category, not a number in a deck - **Judge model**: An independent model reviews auto-apply outputs before they ship, separate from the model that produced them - **Ownership**: Your admin sees every category's current rung and can move it manually at any time The ramp ## 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 ### Is Sainapse autonomous on day one? 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. ### What's the difference between resolution and deflection? 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. ### Who decides when a category moves up the ladder? 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. ### Do high-stakes ticket categories ever reach auto-apply? 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. ### What stops an auto-apply mistake from reaching a customer? 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. Part of ## Where this fits ### How the ladder is enforced - [Independent judge model](https://www.sainapse.ai/features/quality-gates): The review layer that checks every auto-apply output before it reaches a customer. - [When a person stays in the loop](https://www.sainapse.ai/features/human-in-the-loop-exception-handling): How exceptions and low-confidence cases route to a reviewer instead of auto-applying. ### What resolution runs on - [Writing back to your systems](https://www.sainapse.ai/features/end-to-end-system-write): The write layer that turns a decided resolution into a real record change. - [Learning from every correction](https://www.sainapse.ai/features/knowledge-from-resolutions): How each resolved ticket becomes calibration and future context, not a one-off answer. ### Autonomous resolution in production - [Full ticket lifecycle automation](https://www.sainapse.ai/use-cases/end-to-end-ticket-automation): The use case this ladder was built to run: intake through resolution, one pipeline. - [Customer Support workflow](https://www.sainapse.ai/workflows/customer-support): Where autonomous resolution sits inside the rest of the support workflow. ## See the ladder run on your own tickets. - [Book a demo](https://www.sainapse.ai/book-a-demo) ## Sitemap See the full [Sainapse sitemap](https://www.sainapse.ai/sitemap.md).