Kloo runs on deepgreen.
“It’s quietly become the most operationally important tool we have.”
The agentic workspace for business data and operations
Take on more ambitious data work. Run complex analyses, build production apps, and delegate ongoing workflows to AI.
Trusted by AI-forward companies
A full data platform.
Built for business-critical AI.
Bring data and AI together.
Connect business data, give AI the context to understand it, and build analysis, apps, and automations on an open foundation.
Book a demoProduction pipelines in minutes.
A complete data engineering platform for building and running production pipelines. Reliable at scale. Affordable to run. No infrastructure to manage.
Explore data engineeringRequest: Load supplier inventory from S3. Standardize the files and remove duplicates.
- Read supplier inventory files from S3: CSV and Excel files with different column names.
- supplier_inventory.py
@pipeline(schedule="hourly", retries=3)def supplier_inventory(s3, warehouse): files = s3.read_new("inventory/") rows = normalize_columns(files) keys = ["supplier_id", "sku"] rows = latest_per(rows, keys) warehouse.merge("supplier_inventory", rows, key=keys) s3.checkpoint(files) - Pipeline checks
- Column names match across files
- One row per supplier and SKU
- Repeat runs add no duplicates
- Pipeline deployedDeploying Every hour
supplier_inventory.pyFirst run complete2,418,207 inventory rows updated
A warehouse for engineers and AI.
An intelligent warehouse built on an open foundation, optimized for both fast queries and demanding analytics while keeping costs low at scale.
Explore the warehouseaccount_revenue.sql SQL Results 3 rows returned.
| Row | account_name | net_revenue | revenue_change |
|---|---|---|---|
| 1 | Aster | 12000.00 | -0.20 |
| 2 | Northstar | 9000.00 | -0.10 |
| 3 | Forma | 18000.00 | 0.00 |
Manage AI context at scale.
Build, monitor, and improve shared context across data, systems, and teams. Find gaps through automated reviews, trace AI outputs back to their sources, and test the answers the business depends on.
Explore the context layerPowerful analysis. For everyone.
Ask business questions in plain language and get answers you can trust. deepgreen’s AI is purpose-built for data, with tools across the entire stack to handle quick questions and complex analysis—grounded in governed data and shared business context.
Explore analysisUser: What’s driving revenue growth?
deepgreen AI analyzes revenue, customer, and product data, comparing Q2 with Q1. Revenue grew from $840k to $1.24m, an increase of $400k. Existing customers contributed $280k, or 70% of the increase. New customers contributed $120k.
Build any data experience.
Create dashboards, custom apps, and operational tools on live, governed data. Full lineage shows engineering, data, and AI teams where the data comes from and how it’s used.
Explore appsRequest: Build a staffing planner using bookings and team availability.
deepgreen reads bookings and team availability from the staffing data model, writes the app code, and builds a working calendar.
Staffing planner
Next weekops.staffingLive dataTurn data into dollars.
Put business data to work with agents and workflows that qualify opportunities, coordinate follow-ups, and take action across connected systems.
Explore automations-
Data trigger Triggered Northstar
Seat usage92 of 100Trigger when seat usage exceeds 85%
-
AI agent Qualified Northstar is ready to expand.
Product usage is growing across four teams.
-
Salesforce Task created Discuss additional seats
Northstar
Connected data. A shared financial model.
3 daysto centralize
the company’s data
Promotion analysis in plain language.
“We analyzed last month's promotion 99% faster in Deepgreen than we could have in Looker. It's nuts.”
Board-ready in 30 days.
“Questions that used to take a week of back and forth now take a few minutes. That changes what we bother to ask. We test ideas now, like whether a new lead referral partner is paying off, because checking is cheap.”
Build software the business can depend on.
Build apps, analyses, and workflows with AI, backed by shared business logic, controlled access, and managed infrastructure.
Trust the results.
Use shared metrics and rules. Trace results and test AI answers.
Control access and actions.
One permission model for what people, apps, and AI can see and do.
Keep work running.
Host apps and schedule workflows. Track runs and diagnose failures.
Grow and adapt.
Version code as needs change. Match compute and cost to the workload.
Interoperable by design.
Data stays accessible.
The lakehouse uses Apache Iceberg tables and Parquet files on ordinary cloud storage. Read them outside deepgreen with any compatible engine.
Control where data lives.
Choose storage managed by deepgreen or an S3 bucket the company owns.
Keep the existing warehouse.
Use deepgreen’s context layer, apps, and deepgreen AI with a compatible warehouse.
deepgreen lakehouse
Open formats
Parquet
Storage
Managed by deepgreen or company-owned S3
Existing warehouse
Snowflake
BigQuery
Fabric
Everything for IT to say yes
Access control
Control what people, apps, and AI can see and do with shared groups, roles, and resource-level permissions.
Guardrails
Give agents explicit access to tools. Review proposed changes to shared definitions and pipeline code before they’re applied.
Full visibility
Trace results to their sources. Inspect generated code, versioned changes, and workflow runs to understand what happened.
Secure infrastructure
Run data work on managed infrastructure, with authentication, credentials, and permissioned access handled by the platform.
No lab lock-in
Bring models to a shared environment for data work. Keep business context, tools, and permissions in deepgreen.
AI evaluations
Test AI answers against known business results. Find missed rules and measure improvements as context changes.