Agents are scaling faster than guardrails
Most support desks buying agentic AI right now cannot say which decisions their agents are allowed to make alone, what those agents touched, or how they would stop one. That is the number this business is built against.
How it works
The same three moves your engineers already make by hand — run in seconds, on every ticket, as an explicit state graph rather than one long prompt.
Watch it diagnose
Pick an incident, hit run, and follow the agent's reasoning step by step — the same trace your engineers would see in production, and the same trace an auditor would read back.
.log .txt .json .yaml .py .sh .js .conf — or pick both a log and the script that produced it.Ingested files are read in your browser for this walkthrough — in a pilot the same drop zone streams to the RCA service, which parses, redacts and embeds server-side. Try the PII sample to watch redaction fire before anything is stored.
- Parsing incident
- Redacting PII
- Classifying issue
- Searching ticket history
- Diagnosing root cause
- Generating recommendation
Responsible AI, enforced in code
Not a policy PDF. Eight controls that live in the agent graph itself — each one shipped before the pilot goes near a real ticket.
The public demo above has no connection to any live system, by design and without exception. Real logs, real tickets and a signed DPA only enter the picture inside a paid pilot — where inference moves to a provider with contractual data-handling terms.
Proven at scale
Not a prototype pattern. It was built, deployed and measured on a Fortune-500 support desk before it became a product.
Pricing, on request
Scope drives the number, so the number is quoted after a fifteen-minute conversation — not printed on a page. What holds regardless: fixed fee, no hourly billing, and rates set deliberately at the reasonable end for a direct engineer engagement.
Send the shape of the problem — ticket volume, stack, one workflow you'd start with — and you'll get a scoped fixed-fee number back, plus what it would cost to keep running.
Questions
Does the demo touch our real ticket data?+
No. It runs entirely on synthetic incidents and a synthetic ticket history — nothing you paste is stored, and it connects to no live system. Real logs and tickets only come into play during a paid pilot, under a signed agreement.
Can the agent change anything in our environment?+
Not unless you explicitly scope it that way, and the default answer is no. It reads and recommends; the fix is applied by an engineer. Read-only tool permissions are the starting position of every pilot, and widening them is a decision you make in writing.
How do we audit a diagnosis after the fact?+
Each node's input and output is logged, so any recommendation can be replayed: what was parsed, what was retrieved, which SOP was cited, what confidence was assigned. Retainer clients get a dashboard over that trace data rather than having to ask for it.
Which ticketing systems does it connect to?+
Jira, ServiceNow and Zendesk out of the box. Anything else can be scoped during the pilot — the connector layer is deliberately thin.
What if our incidents look nothing like the samples?+
Likely they don't — the demo covers a few common categories to show how the reasoning works. A pilot is built around your ticket taxonomy using your own history. That's the entire reason to run one rather than judge from the demo.
Who actually builds it?+
One engineer, directly. No account managers, no handoff between the person who scoped it and the person who writes it. That's a constraint as much as a pitch: capacity is finite, so engagements are taken a few at a time.
Bring us the workflow. We'll shape the solution around it.
Incident triage is where we go deepest, but it isn't the boundary. The team has shipped agentic and retrieval systems across managed services, telecom, SaaS platforms and document-heavy back-office operations — enough different stacks to know which parts of your problem are genuinely novel and which are already solved.