Agents learn your language
They learn your teams’ terms, shorthand, and ways of working, then create a shared understanding of what everything means.
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 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.
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.
01
Connect
Select the databases, files, documents, applications, and approved external sources the decision actually requires.
02
Prepare
Normalize fields, resolve inconsistencies, clean and join the data, preserve reusable tables, and configure refresh from the sources.
03
Contextualize
Relate records to documents, entities, decisions, and source passages so agents and people can inspect how the information fits together.
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.
Context that compounds
Context grows through the work itself.
Teams should not have to pause the work to document every lesson. The decisions they review, correct, approve, and reuse can make the next workflow better informed.
01
Data work leaves signals
Mappings, corrections, accepted matches, rejected joins, quality decisions, and published outputs reveal how records should relate.
02
The Knowledge Engine connects the trace
Relate those choices to source fields, documents, entities, transformations, provenance, and downstream use.
03
Context becomes reusable
Later pipelines, dashboards, and agents can begin with more of the organization’s validated data knowledge.
Source records remain the authority. The Knowledge Engine keeps extracted evidence and inferred relationships distinguishable so people can inspect what the organization recorded and what the system derived.
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.
Agent Lab and Custom Skills
Turn a good workflow into shared capability.
Build a specialized data-product agent
Run the pipeline and quality checks, then publish the dashboard or report from the same prepared data.
Save a transformation skill
Preserve mappings, joins, provenance requirements, and review rules for the next data product.
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