Knowledge Engine for Enterprise Data Management

Turn enterprise data into decision-ready context.

Connect structured data, documents, business systems, and institutional knowledge; preserve where information came from; and make that context usable by agents, analysts, workflows, and live dashboards.

KEP

Customer data management workspace

Reconcile customer records across Salesforce, Snowflake, and NetSuite and publish a governed quality dashboard.

I connected the source records, mappings, deduplication history, and business rules. I’m preparing the quality report now.

Customer data quality reportReport

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.

Having the data is not the same as understanding the work.

Warehouses, catalogs, document stores, and business applications each preserve part of the record. Decisions also depend on how the sources relate, why earlier choices were made, and which evidence supports the conclusion.

Enterprise Data Management 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 customer recordsshould be merged?”CONTEXT MODELDomain languageJARGON · SYNONYMS · INTENTMDM→master datadupe→same customerintent: resolve identityCanonical modelENTITIES · RELATIONSHIPSCustomerProfileAccountMATCHESBILLSSemantic bindingsCONCEPT → SOURCE DATACustomer→SalesforceProfile→SnowflakeAccount→NetSuitetables · columns · keysAGENTIC DATA PIPELINESCONTEXT GRAPHSalesforceSnowflakeNetSuiteCustomersProfilesAccountsJOINCustomer contextNorthstarCUSTOMERACCT-104CRM RECORDC-778PROFILENS-445ACCOUNTVIRTUALIZEDquery 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 →
  • BigQuery
  • Azure SQL
  • Snowflake
  • Databricks
  • AWS
  • Salesforce

Workflows

Change the work, not just the interface.

01

Build reusable data pipelines

Ingest data from files, APIs, databases, and enterprise applications; then filter, clean, join, aggregate, persist, and refresh the prepared result.

02

Create source-linked context

Connect structured records with documents, entities, relationships, prior decisions, and institutional knowledge while preserving provenance.

03

Deliver governed data products

Turn prepared data and organizational context into analysis, reports, live dashboards, and agent workflows without replacing existing systems of record.

One end-to-end example

From raw inputs to a governed decision product.

  1. 01

    Connect

    Select the databases, files, documents, applications, and approved external sources the decision actually requires.

  2. 02

    Prepare

    Normalize fields, resolve inconsistencies, clean and join the data, preserve reusable tables, and configure refresh from the sources.

  3. 03

    Contextualize

    Relate records to documents, entities, decisions, and source passages so agents and people can inspect how the information fits together.

  4. 04

    Deliver

    Publish a dashboard, analysis, decision brief, or reusable workflow with permissions, provenance, and review boundaries intact.

What the team gets

Outcomes people can inspect and use.

Prepared and refreshing data

Reusable tables and ETL pipelines that keep analysis connected to changing source systems.

Source-linked organizational context

Structured and unstructured information connected through relationships, decision history, and provenance.

Governed data products

Interactive dashboards, reports, briefs, and agent workflows built from the same approved context.

The Accrete product

Context, work, and control in one path.

The Knowledge Engine acts as a context and action layer above the systems a company already uses. It complements warehouses, catalogs, master-data platforms, BI tools, and systems of record rather than asking customers to replace them.

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