Fixed scope, no lock-in. We build the lakehouse and AI agents on the platform you already run: AWS, Snowflake, Databricks, Google Cloud or on-premises. Your first use case goes live in 6–8 weeks, and our engineers keep it running.
Fixed scope, no lock-in. We build the lakehouse and AI agents on the platform you already run: AWS, Snowflake, Databricks, Google Cloud or on-premises. Your first use case goes live in 6–8 weeks, and our engineers keep it running.
Fixed scope, no lock-in. We build the lakehouse and AI agents on the platform you already run: AWS, Snowflake, Databricks, Google Cloud or on-premises. Your first use case goes live in 6–8 weeks, and our engineers keep it running.
It runs on a cleaned-up sample. Connected to live data, access rules and real volumes, it stalls.
Platform, model and consulting spend keeps growing while the business still waits for something it can use.
Your engineers are busy keeping today’s systems alive, and the specialists you need are hard to hire.
We build on the AI service already in your cloud account: Amazon Bedrock, Snowflake Cortex, Databricks Agent Bricks or Vertex AI. There is no new platform to buy.
One use case, four two-week sprints, and a go-live date we agree on day one.
How the Sprint works →Intelligent DataOps finds the cause of a pipeline failure and proposes the fix as a pull request for an engineer to approve.
Intelligent DataOps →A founder leads every project, and senior engineers stay on it through go-live.
Pick the first use case, map the data it needs and agree what “live” means.
We build the part of the lakehouse the use case needs and the agent on top, on your platform.
Intelligent DataOps keeps it running: AI-assisted monitoring and fixes, with engineers reviewing every production change.
Each one is adjusted to your requirements.
Ready-made architectures for AWS, Snowflake, Databricks, Google Cloud and on-premises.
Pre-built integrations for common sources.
Watches for ETL failures, alerts, and proposes the fix as a Git pull request that an engineer reviews.
At 10xCRM, it now handles 40 hours a month of pipeline maintenance.
Answers business questions from governed KPI definitions and flags dashboards that drift from them.
Explore AI Analyst →Start with a Sprint, extend with a Build, keep it running with Managed.
“AgentixLake built our multi-tenant analytics platform on Google Cloud. New customers now go live in minutes instead of weeks, and their AI agent handles the pipeline maintenance that used to take 40 hours a month. Their solution has empowered us to provide robust, data-driven consulting with exceptional ongoing support.”
Muhammed BagriacikChief Executive Officer, 10xCRM
“AgentixLake built an AI pipeline that extracts and structures data from our complex user manuals. It has greatly increased our efficiency, reduced manual work and sped up project delivery.”
Maarten PisoDigital Product Manager, DINTEGRA GmbH
“AgentixLake partnered with us at Syniotec to design and deploy a robust data-and-AI pipeline that drives actionable, scalable insights. Their solution has transformed our operations, enabling faster decision-making, smarter automation, and sustained growth”
Ben KebdaniFounder & Software Advisor, SyniotecTechnical documentation, spare parts and compliance data, turned into agents that find, extract and draft the answers your teams need.
Governed data platforms and document intelligence for research, clinical and regulatory data, with access control and audit trails built in.
One data and AI foundation reused across portfolio companies, so each new company gets a working use case faster than the last.
Multi-tenant analytics, high-volume event data and self-healing pipelines, so new customers go live in minutes and pipeline failures are fixed in 30 minutes.
Your data stays where it is, and the agents use the AI service already in your account. If another platform suits a use case better, we tell you.
| PLATFORM | DATA | AGENTS |
|---|---|---|
| AWS → | S3, Apache Iceberg, Glue | Amazon Bedrock |
| Snowflake → | dbt, Dagster, zero-copy clones | Snowflake Cortex |
| Databricks → | Delta Lake, Lakeflow, Unity Catalog | Agent Bricks |
| Google Cloud → | BigQuery, dbt, Mage.ai | Vertex AI |
| On-premises → | Apache Iceberg, Spark, Kafka | Open-source models we host on your infrastructure |
Claude is available on AWS, Snowflake, Databricks and Google Cloud. Production AI with Claude →
