Knowledge Engine for IT Operations

Resolve incidents with the full operational context.

Connect service history, system signals, documentation, and team knowledge so IT can triage issues, identify likely causes, coordinate response, and keep stakeholders informed.

KEP

Incident response workspace

Investigate the checkout API errors after today’s deployment and prepare a response plan.

I connected the alert, service dependencies, recent changes, telemetry, tickets, and runbooks. I’m preparing the incident brief now.

Checkout API incident briefReport

58%

lower token cost

when using Knowledge Engine

2x

more tasks

vs. frontier models

32%

more accurate with open source

vs frontier model

19 of 34

tacit-knowledge facts captured

vs. RAG’s 1

Source: “Knowledge Engine Platform for Long-Horizon Professional Agents” and “Context Graphs for Recovering Tacit Organizational Knowledge”, Accrete AI, August 2026.

Incidents begin before the alert.

The cause may be buried in a recent change, an old ticket, a service dependency, or the experience of someone who knows the environment. When that context stays scattered, responders repeat the same investigation under pressure.

IT Operations cognitive fabric

Connect the context behind the work.

Agents learn the language of the work, create the data flows that connect its systems, and build a living context graph for each request.

Agents learn your language

They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.

Agents connect your systems

They create the data flows that bring the right information together for each task, without manual setup for every source.

Agents build the context graph

They connect people, records, decisions, and outcomes while preserving a clear path back to the source.

From domain language and source data to a context graphThe context model resolves jargon, synonyms, abbreviations, and intent into shared entities and relationships. Source mappings connect those concepts to tables and keys while agentic data pipelines prepare durable data. The resulting context graph can be virtualized over its sources or materialized.NATURAL-LANGUAGE REQUEST“Why is checkout downafter today’s release?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTP1→critical incidentblast→affected servicesintent: find root causeCanonical modelENTITIES · RELATIONSHIPSIncidentServiceChangeAFFECTSFOLLOWSSemantic bindingsCONCEPT → SOURCE DATAIncident→ServiceNowService→DatadogChange→Jiratables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHServiceNowDatadogJiraIncidentsTelemetryChangesJOINIncident contextCheckoutSERVICEDeploy 8421CHANGEINC-3814INCIDENTM. RiveraOWNERVIRTUALIZEDquery sources in placeMATERIALIZEDpersist selected subgraphs
The context model interprets a domain’s vocabulary, synonyms, abbreviations, and intents, then resolves natural-language requests to shared entities and relationships. Source mappings connect those concepts to tables, columns, keys, dialects, and execution integrations. KEP can query the resulting context graph virtually over source data or materialize selected graph data.
  • ServiceNow
  • Jira
  • Datadog
  • Grafana
  • AWS
  • Azure Cloud

Workflows

Change the work, not just the interface.

01

Triage incidents with context

Bring together monitoring signals, service and asset context, recent changes, prior incidents, runbooks, and team knowledge to establish what is known and what needs investigation.

02

Assess change and service impact

Connect a proposed change to affected services, dependencies, owners, historical failures, and supporting documentation before the team decides how to proceed.

03

Coordinate response and reporting

Produce an incident brief, assign the next work, maintain a live status dashboard, draft stakeholder updates, and preserve what resolved the issue.

One end-to-end example

From alert to reviewed response plan.

  1. 01

    Connect

    Gather the alert, service map, recent changes, ticket history, telemetry, runbooks, and relevant conversations.

  2. 02

    Analyze

    Compare the current symptoms with earlier incidents and changes; rank likely causes and identify missing evidence.

  3. 03

    Deliver

    Create an incident brief, response plan, owner list, stakeholder update, and live operational dashboard.

  4. 04

    Control

    Show proposed updates or remediation actions before changing a connected system, then preserve the approved decision trail.

What the team gets

Outcomes people can inspect and use.

Incident brief

Known facts, likely causes, open questions, source evidence, owners, and next actions.

Live status dashboard

Current service health, incident milestones, response ownership, and stakeholder-ready updates.

Reusable resolution knowledge

The investigation path, decision rationale, and resolution available to the next responder.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine Platform coordinates cross-system work across change risk, incident investigation, and service dependencies.

Start with one workflow

Define the outcome before expanding the system.

Bring the workflow, its information sources, its approval requirements, and the result your team needs. We’ll map a focused first deployment and the path to reuse.

Discuss this workflow