THE PATH TO AUTONOMOUS RESOLUTION
Any team can demo an AI agent that answers a ticket. Getting one to solve real technical tickets, the same way every time, took six layers we built first.
THE PATH TO ASR
WHAT A DIY BUILD SEES
An agent that demos well and breaks on real tickets.
WHAT IT TAKES TO MAKE IT WORK
Knowledge Gap Analysis finds the questions your docs don't answer. DeDupe Detection finds articles that repeat or contradict each other. New articles are drafted from resolved tickets.
Why ASR needs it: An agent is only as good as what it can cite. Missing or conflicting articles become wrong answers.
Each correction is logged as a resolution trajectory: what the AI did, what the engineer changed and how the ticket ended. Those records tune the models.
Why ASR needs it: This is data a DIY build cannot buy. It only exists after engineers have corrected AI on real tickets at scale.
Support engineers use AI answers, drafts and ticket insights every day, and accept, edit or reject each one. Runbook Controls let each team decide where AI runs.
Why ASR needs it: Every accepted, edited or rejected answer is a labeled example of what good looks like on your tickets.
Proprietary embeddings, a chunking strategy built for support content, sentiment, 50+ languages and routing across models by task.
Why ASR needs it: General models treat a stack trace as plain text. Ours are tuned to find what matters in it.
Tickets, bugs, articles and chat threads mapped to one model, tagged by product and version, with related incidents clustered.
Why ASR needs it: The same bug shows up as a ticket, a Jira issue and a Slack thread. Without one model, the agent sees three unrelated problems.
Salesforce, ServiceNow, Zendesk, Jira, Confluence, Slack, Teams, GitHub and SharePoint, connected natively. Live in about two weeks.
Why ASR needs it: An agent can only solve what it can see. A ticket answered from half the evidence is a guess.
Built from the bottom up. Each layer exists because the one above it needed it.
Token spend is the small part of building this. Evals, people, infrastructure and operations tooling are the rest.
Each layer started as a product of its own. Together they are why the agent on top holds up.
Salesforce, ServiceNow, Zendesk, Jira, Confluence, Slack, Teams, GitHub and SharePoint, connected natively. Live in about two weeks.
Why ASR needs it: An agent can only solve what it can see. A ticket answered from half the evidence is a guess.
Tickets, bugs, articles and chat threads mapped to one model, tagged by product and version, with related incidents clustered.
Why ASR needs it: The same bug shows up as a ticket, a Jira issue and a Slack thread. Without one model, the agent sees three unrelated problems.
Proprietary embeddings, a chunking strategy built for support content, sentiment, 50+ languages and routing across models by task.
Why ASR needs it: General models treat a stack trace as plain text. Ours are tuned to find what matters in it.
Support engineers use AI answers, drafts and ticket insights every day, and accept, edit or reject each one. Runbook Controls let each team decide where AI runs.
Why ASR needs it: Every accepted, edited or rejected answer is a labeled example of what good looks like on your tickets.
Each correction is logged as a resolution trajectory: what the AI did, what the engineer changed and how the ticket ended. Those records tune the models.
Why ASR needs it: This is data a DIY build cannot buy. It only exists after engineers have corrected AI on real tickets at scale.
Knowledge Gap Analysis finds the questions your docs don't answer. DeDupe Detection finds articles that repeat or contradict each other. New articles are drafted from resolved tickets.
Why ASR needs it: An agent is only as good as what it can cite. Missing or conflicting articles become wrong answers.
ASR investigates each ticket across your systems, follows your runbooks and resolves it end to end. It checks what actually happened by calling tools like Datadog and Sentry over MCP.
You set eligibility. You choose which ticket categories ASR may touch.
Internal note first. ASR drafts its answer as an internal note, and your engineers accept or correct it.
The acceptance-rate gate. A category goes autonomous only after its acceptance rate clears the gate.
The Autonomy Ledger. Every decision is recorded, so you can audit what the agent did and why.
We'll connect your systems, show you the gaps in your knowledge base and show you which ticket categories are ready for ASR.