Knowledge Engine for Customer Support

Resolve faster with the complete customer story.

Bring tickets, account history, product context, incidents, and prior resolutions into one governed support workflow.

KEP

Support escalation review

Review the accounts with breached or at-risk SLAs and prepare the next actions.

I connected ticket history, customer tier, product context, incidents, owners, and prior resolutions. I’m preparing the escalation brief now.

SLA escalation 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.

The ticket is only one part of the customer problem.

Support teams must connect the current request to account priority, product behavior, earlier conversations, known issues, engineering work, and prior resolutions. When that context stays fragmented, queues grow and escalations lose clear ownership.

Customer Support 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“Which SLA casesneed action now?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTSLA→service promiseat risk→breach likelyintent: prioritize casesCanonical modelENTITIES · RELATIONSHIPSCaseAccountIssueHASBLOCKSSemantic bindingsCONCEPT → SOURCE DATACase→ZendeskAccount→SalesforceIssue→Jiratables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHZendeskSalesforceJiraTicketsAccountsIssuesJOINSupport contextNorthstarACCOUNTCASE-184CASEBUG-772ISSUEL. DiazOWNERVIRTUALIZEDquery 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.

Connected systems

Explore 1,000+ integrations →
  • Salesforce
  • Zendesk
  • Intercom
  • ServiceNow
  • Jira
  • Slack

Workflows

Change the work, not just the interface.

01

Triage and route with context

Classify incoming work using the issue, customer tier, product area, known incidents, ownership, urgency, and the team’s SLA policy.

02

Draft a grounded response

Retrieve the relevant customer history, documentation, similar resolutions, and product evidence, then prepare a response for agent review.

03

Coordinate escalations and trends

Connect related tickets, incidents, accounts, and product work; assign the next actions; and produce recurring voice-of-customer reporting.

One end-to-end example

From at-risk queue to reviewed escalation plan.

  1. 01

    Connect

    Gather tickets, customer and contract context, documentation, product telemetry, incidents, engineering work, conversations, and prior resolutions.

  2. 02

    Analyze

    Group related issues, test against known problems, identify SLA and account risk, and surface missing ownership or evidence.

  3. 03

    Deliver

    Create the escalation brief, proposed responses, owner list, customer updates, and voice-of-customer summary.

  4. 04

    Control

    Require support review before sending a response, changing a ticket, escalating an account, or updating a customer-facing system.

What the team gets

Outcomes people can inspect and use.

Prioritized support queue

Cases organized by urgency, customer context, known issue, SLA exposure, and required owner.

Response and escalation brief

Grounded draft responses, related evidence, open questions, owners, and next actions.

Voice-of-customer report

Recurring themes connected to affected accounts, product areas, incidents, and resolved work.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine brings customer, product, service, and operational context into the support workflow while keeping external responses and source-system changes under human control.

Review governance →Open the Trust Center →

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