Knowledge Engine for AI Leaders

Turn scattered AI experiments into governed production value.

Coordinate use cases, data contracts, evaluations, risk, operations, and cost so AI leaders can scale what works and stop what does not.

KEP

AI portfolio review

Review active AI use cases and show which ones are not ready to scale.

I connected objectives, owners, evaluations, data access, security and legal reviews, production telemetry, adoption, and cost. I’m preparing the portfolio brief now.

AI portfolio 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.

AI portfolios fail when experiments outrun operating discipline.

AI leaders coordinate business value, data, models, evaluations, security, privacy, delivery, adoption, and unit economics across many teams. Without one reviewable operating path, pilots multiply while ownership and production evidence remain unclear.

AI 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 AI use casesare ready to scale?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTEVAL→quality testguard→safety ruleintent: assess readinessCanonical modelENTITIES · RELATIONSHIPSModelUse caseReviewPOWERSREQUIRESSemantic bindingsCONCEPT → SOURCE DATAModel→AWSUse case→GitLabReview→Jiratables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHAWSGitLabJiraModelsUse casesReviewsJOINAI contextSupport agentUSE CASECore modelMODELEval 0.91EVALUATIONPolicy checkGUARDRAILVIRTUALIZEDquery 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 →
  • AWS
  • Azure Cloud
  • BigQuery
  • Snowflake
  • Databricks
  • GitLab

Workflows

Change the work, not just the interface.

01

Govern the use-case portfolio

Connect objectives, owners, business value, data requirements, risk reviews, dependencies, status, and realized outcomes across active AI work.

02

Evaluate release readiness

Organize test sets, quality and safety results, source evidence, failure analysis, human-review requirements, and approvals before deployment.

03

Operate reliability and cost

Bring together quality, latency, incidents, usage, adoption, model and infrastructure cost, and policy exceptions into a recurring operating review.

One end-to-end example

From disconnected pilots to a governed scale decision.

  1. 01

    Connect

    Gather the business objective, owner, data contract, system design, evaluations, risk reviews, costs, telemetry, adoption, and outcome evidence.

  2. 02

    Analyze

    Test readiness against approved quality, safety, security, privacy, reliability, ownership, and unit-economics requirements.

  3. 03

    Deliver

    Create the portfolio brief, readiness scorecard, risk register, owner actions, and reviewed scale, pause, or stop recommendation.

  4. 04

    Control

    Require the organization’s authorized approvals before expanding access, changing production behavior, or enabling consequential actions.

What the team gets

Outcomes people can inspect and use.

AI portfolio brief

Use cases, value hypotheses, owners, dependencies, risk, status, cost, and realized outcomes in one reviewable view.

Evaluation and readiness package

Test evidence, failures, limits, source trace, human-review boundaries, and approval status.

Reliability and cost review

Quality, latency, usage, incidents, adoption, and unit economics with recommended owner actions.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine provides the organizational context, orchestration, artifacts, source evidence, policy, and approval path required to move AI work from isolated pilots into governed operations.

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