AI Agents as a Service

Deploy AI agentsgrounded in your own data

Most agents hallucinate because they are guessing at your business. Ours read from the warehouse you already run — governed, auditable, and answering with the lineage to prove it.

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SnowflakeDatabricksAWS RedshiftGoogle BigQueryAzure SynapseLakehouse
How It Works

Agents that adapt to your data, not the other way round

No new platform to buy and no second copy of your data. We build on the warehouse, semantic models and pipelines your team already operates.

Connect Your Platform

Agents read from the warehouse you already run — Snowflake, Databricks, BigQuery, Redshift, Synapse or an open Lakehouse — using your existing roles and access policies.

  • Native warehouse connectors
  • Existing RBAC respected
  • No data duplication

Ground Them in Your Data

Retrieval is built on governed tables and semantic models rather than scraped documents, so answers trace back to a column, a row and a query you can audit.

  • Semantic layer aware
  • Row-level lineage
  • Citations on every answer

Orchestrate Real Workflows

Multi-step agents that query, transform, validate and write back — running inside the same dbt, Airflow and Spark pipelines your data team already operates.

  • dbt & Airflow native
  • Deterministic fallbacks
  • Retry and replay built in
6
Platforms supported end to end
50+
Certified data engineers
200+
Pipelines delivered in production
99.9%
Pipeline uptime SLA
Built for Enterprise

Enterprise-grade agents, without the enterprise timeline

The controls a security review will ask about, built in from the first sprint rather than retrofitted before launch.

Runs in Your Cloud

Deployed inside your own VPC and cloud account. Your data never leaves your perimeter, and model routing is yours to configure.

Governed & Auditable

Every agent action is logged with the query it ran and the identity it ran as. Aligned with the ISO 27001, SOC 2, HIPAA and GDPR controls we already work under.

Evaluated Before Release

Agents ship with regression suites over your own questions and expected answers, so accuracy is measured rather than assumed.

Human in the Loop

Write operations, spend thresholds and destructive actions route to an approver. Autonomy is a dial you set per workflow, not a default.

Use Cases

Where data teams deploy agents first

Start with one workflow that has a measurable owner and a clear definition of correct, then expand.

Self-Service Analytics

Business users ask questions in plain language and get governed SQL, a result set and the lineage behind it.

Pipeline Triage

An agent watches failures, reads logs and lineage, proposes the fix and opens the pull request for a human to approve.

Data Quality Monitoring

Continuous checks on freshness, volume and distribution, with root-cause context attached to every alert raised.

Migration Acceleration

Agents translate legacy Oracle, Teradata and SQL Server logic into target-platform SQL, with automated validation of every conversion.

Cost Governance

Continuous review of warehouse spend, flagging runaway queries and idle compute with concrete right-sizing recommendations.

Documentation & Catalog

Table and column descriptions generated from real query patterns, then kept current as the underlying models change.

Frequently asked questions

Common questions about deploying agents on an existing data platform.

We are a data consultancy, not a platform vendor. Agents are built on the warehouse you already run, using your semantic models, access policies and pipelines. There is no new platform to buy and no copy of your data to maintain.
Inside your own cloud account and VPC. We deploy into your infrastructure, so data residency, network policy and model routing stay under your control. We can also run a managed environment if you prefer.
Snowflake, Databricks, AWS Redshift, Google BigQuery, Azure Synapse, and open Lakehouse setups on Apache Iceberg, Hudi or Delta Lake. Orchestration integrates with dbt, Airflow, Kafka and Spark.
Retrieval is restricted to governed tables and semantic models, every answer carries the query and lineage behind it, and each agent ships with a regression suite built from your own questions and expected results.
A two to four week assessment to identify the highest-value workflows and confirm data readiness, then delivery in agile sprints. Most first agents reach production in six to ten weeks, after which we can operate them as a managed service.
Yes, with approval controls. Write paths, spend thresholds and destructive operations route to a named approver by default. You decide per workflow how much autonomy each agent has.

Have a use case in mind?

Bring us the workflow. We will tell you honestly whether an agent is the right answer, what your data needs first, and what it takes to reach production.

Talk to a SpecialistEngagement Models