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 Data Science
Move models from experiment to reliable business impact.
Connect data quality, experiments, deployments, model health, and business outcomes into a reviewable operating path.
KEP
Model portfolio review
Review production model health and identify which issues threaten customer or business outcomes.
I connected model telemetry, data-quality checks, experiment history, incidents, product usage, costs, and outcome metrics. I’m preparing the portfolio brief now.
Production model health 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.
A model is only as reliable as the system around it.
Data scientists need trustworthy inputs, reproducible experiments, clear lineage, controlled releases, production monitoring, and business-outcome evidence. Those records often split across notebooks, pipelines, registries, tickets, dashboards, and people.
Data Science 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
- Snowflake
- Databricks
- AWS
- Azure Cloud
- Datadog
Workflows
Change the work, not just the interface.
01
Assess data and experiment readiness
Connect datasets, definitions, lineage, quality checks, experiment history, assumptions, and approvals before a model moves forward.
02
Monitor models and coordinate incidents
Bring together model, feature, pipeline, latency, cost, and product signals; investigate changes; and prepare a reviewed response plan.
03
Report business impact
Relate model behavior and experiments to product and business outcomes, then produce portfolio reporting with assumptions and confidence boundaries visible.
One end-to-end example
From isolated health alerts to a reviewed model response.
01
Connect
Gather model and feature telemetry, pipeline status, data quality, lineage, experiment history, code changes, incidents, costs, and outcome metrics.
02
Analyze
Establish the timeline, compare current behavior with baselines, trace upstream dependencies, and identify the evidence behind likely explanations.
03
Deliver
Create the health brief, affected-outcome view, investigation plan, owner list, and recommended release or rollback decision.
04
Control
Require authorized review before retraining, promoting, rolling back, changing data, or altering a production decision path.
What the team gets
Outcomes people can inspect and use.
Readiness and experiment brief
Data, lineage, assumptions, evaluation, risks, approvals, and reproducible evidence for the proposed work.
Model health investigation
Timeline, affected systems, likely causes, confidence boundaries, owners, and recommended response.
Business-impact report
Model and experiment outcomes connected to product, operational, financial, or risk measures.
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
Model work leaves signals
Experiment choices, data fixes, release decisions, incident findings, and observed outcomes reveal what makes the system reliable.
02
The Knowledge Engine connects the trace
Relate those decisions to datasets, features, code, experiments, models, deployments, telemetry, and business measures.
03
Future work becomes reproducible
Teams can reuse validated definitions, investigation paths, release evidence, and operating knowledge across the model portfolio.
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 connects data, experiments, production behavior, and business outcomes into governed work while complementing existing data and ML platforms.
Agent Lab and Custom Skills
Turn a good workflow into shared capability.
Build a specialized model-review agent
Refresh portfolio context, investigate health changes, and prepare the recurring operating review.
Save a release-readiness skill
Preserve required checks, evidence, ownership, approval gates, and the team’s reporting format.
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