Knowledge Engine for Product

Turn customer signal into a roadmap your teams can trust.

Connect feedback, delivery work, dependencies, usage, and production health so product leaders can make and explain better tradeoffs.

KEP

Launch readiness review

Review the enterprise launch plan, identify the biggest delivery risks, and prepare the executive update.

I connected roadmap commitments, engineering work, dependencies, customer evidence, release health, and open approvals. I’m preparing the launch brief now.

Enterprise launch readiness 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 roadmap competes with evidence arriving from every direction.

Product leaders balance customer demand, revenue commitments, engineering capacity, dependencies, quality, security, and product outcomes. The tradeoffs become hard to defend when each source tells a different part of the story.

Product 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“What could delaythe enterprise launch?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTGA→public releaserisk→launch blockerintent: assess readinessCanonical modelENTITIES · RELATIONSHIPSWorkReleaseDesignSHAPESBLOCKSSemantic bindingsCONCEPT → SOURCE DATAWork→JiraRelease→GitLabDesign→Figmatables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHJiraGitLabFigmaWork itemsReleasesDesignsJOINLaunch contextEnterpriseRELEASESSO epicWORKQA signoffMILESTONEPriyaOWNERVIRTUALIZEDquery 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 →
  • Jira
  • GitLab
  • Figma
  • Pendo
  • Amplitude
  • Salesforce

Workflows

Change the work, not just the interface.

01

Synthesize customer signal

Connect feedback from support, sales, customer conversations, usage, and research; group recurring needs; and preserve the evidence behind each theme.

02

Review roadmap and dependencies

Bring commitments, capacity, technical dependencies, customer impact, security work, and decision history into one portfolio review.

03

Coordinate releases and outcomes

Prepare launch-readiness briefs, status updates, risk reviews, and post-launch adoption reporting from the same source-linked context.

One end-to-end example

From fragmented delivery updates to a reviewed launch decision.

  1. 01

    Connect

    Gather customer evidence, roadmap commitments, design and engineering work, dependencies, testing, security reviews, enablement, and product telemetry.

  2. 02

    Analyze

    Compare commitments with current work, surface blockers and downstream impact, and identify where evidence no longer supports the plan.

  3. 03

    Deliver

    Create the launch-readiness brief, risk register, executive update, owner list, and post-launch measurement plan.

  4. 04

    Control

    Keep roadmap changes, release decisions, customer commitments, and external communications behind the appropriate owner approval.

What the team gets

Outcomes people can inspect and use.

Customer-signal brief

Recurring needs, affected accounts, product evidence, source links, and open questions ready for product review.

Roadmap and dependency review

Commitments, capacity, blockers, risks, tradeoffs, owners, and decision history in one working artifact.

Launch and outcome report

Readiness, current status, customer impact, adoption, reliability, and follow-up work connected to the release.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine connects customer evidence, planning, delivery, and outcomes so product teams can make reviewable decisions without replacing their roadmap, development, or analytics systems.

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