Agentic Lakehouse

The lakehouse that maintains itself

Agents handle discovery, remediation, and analysis, with your approval. Let your platform team ship roadmaps, not maintenance.

Every change is proposed as a pull request and waits for your approval.

Data platforms rarely ‘break’. They quietly bleed resources.

Every schema change, new column, or uncatalogued dataset creates manual remediation. Sound familiar? With Automat-it’s Agentic Lakehouse platform, agents handle the upkeep of your data lake and you keep complete ownership.

Less engineering burden

Schema discovery, catalog updates, and drift fixes arrive drafted by agents and reviewed as a short pull request.

Risk mitigation by design

Agents propose and wait for approval. There is no autonomous write path to production, and every action is recorded in CloudTrail and Git.

Open format, no lock-in

Data stays in Apache Iceberg on your own S3, readable by Athena, Spark, EMR, Trino, or Redshift.

Predictable cost

Serverless-first on Glue, Athena, and Lambda means no idle clusters, and no seat-based pricing means more people can query without extra licences.

100% data ownership

Everything runs in your AWS account and your S3 buckets, so you control security and privacy at all times.

Three agents, one governed workflow

Built on Amazon Bedrock, each agent does one job well and stays inside explicit, default-deny tool policies.

Discovery Agent

Profiles newly landed data and proposes catalog updates before it becomes an undocumented liability.

Transformation Agent

Detects drift between source schemas and pipeline code, and proposes the specific change that absorbs it.

Talk-to-Your-Data Agent

Answers plain-language questions from your governed serving layer and cites the data behind each answer.

Every agent proposal arrives as a pull request with a diff, owner, and audit trail. Automation stays reviewable.

Production deployment in 3–4 weeks

For a single-domain scope, delivered inside your AWS environment.

Discover & Design

Automat-it reviews your data sources, current pipelines, governance needs, and priority use cases, then scopes the first domain and the approval workflow your team will use.

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Automated Implementation

We deploy the lakehouse in your account: Iceberg on S3 across Bronze, Silver, Golden, and Audit zones, orchestrated by Amazon MWAA, with transformations on AWS Glue and transient EMR on Spot. The three agents connect to your Git workflow.

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Operationalize & Scale

Go live with agents proposing changes as pull requests. Onboarding a new table becomes config and SQL, not a code change, so growth stays predictable as you add sources and domains.

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Do you tick any of these boxes?

Ready to run a lakehouse that maintains itself?

Book a discovery call and see how agents can take over discovery, remediation, and analysis while you keep total ownership of your data security and privacy.

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