Learn how pETL works
Source system to lakehouse: the AI harnesses that extract, model, validate, and deploy, with a human approving every step.
Agentic Intelligence for Data & AI
From raw data to autonomous action, faster.
We help enterprises turn scattered data into decisions they can trust and action they can take. New DAIS builds the foundation, delivers the insight, and deploys the agents, faster than traditional consulting and provably correct at every step.
What we've been building
The state of enterprise data
$12.9M
Average yearly cost of poor data quality to an organization.
Gartner
56%
Name siloed data as their top obstacle to preparing data for AI.
Cloudera / Harvard Business Review, 2026
7%
Say their data is completely ready for AI adoption.
Cloudera / Harvard Business Review, 2026
Most enterprises aren't AI-ready because their data isn't. New DAIS fixes that first.
What we help you achieve
Build the foundation. Deliver insight. Take action.
Three connected outcomes on one AI-native engineering practice, from raw source data to decisions your business acts on every day.
Build the Foundation
A trusted data platform, in weeks
The pETL Accelerator
Our AI harnesses turn scattered source systems into a governed, documented data platform, built on data-product best practices and provably correct against source.
See how pETL worksDeliver Insight
Answers your business can act on
AI-Assisted Analytics
We turn your platform into analytics anyone can query in plain English, tuned to the way your business actually thinks about its data, on our Chatalytics app or the tools you already own.
Take Action
Agents that do the work
Enterprise AI Agents
Custom decision-support and automation agents that reason across your data and take authorized action in your core systems.
Find your use cases
0:00 / 0:00
Foundation · pETL
Meet pETL. Your lakehouse, built by AI.
A suite of AI harnesses that carry a business from its stated requirements to a working, documented data platform, on the platform of your choice. A human approves every step.
01
Step 01
pETL Ingest
Connects to source systems and lands transactional data in the Bronze tier, validates it, and sets up change-data-capture and scheduled loads. For sensitive data, it can generate synthetic, anonymized copies so real data is never exposed to the AI.
02
Step 02
pETL Modeler
Works from your requirements, KPIs, reports, and existing queries to design the data products your business needs, using Data Product and Data Mesh patterns on a Medallion foundation. An adversarial hardening loop and expert review vet each candidate against the requirements and against real sample data until the design is provably right, producing Gold-layer data products and the Silver foundations that support reuse.
03
Step 03
pETL Pipeline
Renders the approved design into platform-native pipelines and code for your target, Databricks, Fabric, or a classic SQL warehouse, with the tests to prove it. You are left with supportable, maintainable code your engineers own. No lock-in, and pETL is never needed in your production runtime.
verified_user
SECURE & GOVERNED
Portable by design. Private by default.
Bring your own model
Pluggable LLM modules let you run the model of your choice, on your licensing and terms.
No lock-in
We build on your platform of choice and emit native code. Everything is portable by design, and pETL is never needed in your runtime.
Your data stays home
Built-in anonymization and synthetic-data tools keep real, sensitive data out of LLM vendors' hands.
On top of the platform
The platform is just the beginning.
The value is what you put on top: insight your business can act on, and agents that act for you.
How it's different
Provably correct, not plausibly close.
Pointing an AI at your data gets you plausible answers. Getting correct ones takes guardrails, feedback loops with people who know your business, and reconciliation against source at every step. That discipline is the difference between a demo and a system you can run on.
A human approves every step
Your data never leaves your perimeter, zero retention
Every requirement and design decision lives in one repository
Next steps
Let's turn this into a plan.
01
Open discussion
Bring your questions. We'll go deep on pETL, analytics, or agents.
02
Identify a pilot
Pick one data source or workflow to prove the model on.
03
Schedule discovery
Book a session to scope timeline, access, and stakeholders for a proof of concept.