01
Find where leads are getting stuck.
Compare advertising spend with HubSpot leads and meetings. Investigate rising costs alongside a growing backlog of uncontacted leads.
From an idea to something the business runs on.
Work across company data and connected systems, with shared business context.
Book a demoOne workspace for
business AI.
Conversational workspace
Ask questions, investigate follow-ups, and build tools with deepgreen AI in one place.
Explore AnalysisShared data and context
Everyone can work from the company’s governed data model and context layer, with shared business definitions and permissions controlling access.
Explore the context layerBuild pipelines and integrations
Describe which systems need to connect and how the data should move. deepgreen AI writes the code; deepgreen handles deployment and scheduling.
Explore Data engineeringNo black boxes
See the source data, business rules, and code behind the work. Inspect workflow runs and the actions they took.
Explore auditabilityWhy does Jamie need a call?
- Source data
- Lead created 2 days ago.
No call or email logged. - Business rule
- Follow up when no first contact is logged after 24 hours.
- Reason it matched
- Jamie is past the first-contact window.
Read the source rule
Sales playbook · First contact
Contact new leads within 24 hours. If no call or email is logged, create a call task for the lead’s sales owner.
Turn analysis into
tools and workflows.
02
Build an agent to catch missed follow-ups.
Ask deepgreen AI to build an agent that reviews recent sales activity and finds leads still waiting for a first response.
03
Create call tasks in HubSpot.
deepgreen AI writes the integration the agent uses to create call tasks in HubSpot, assigned to each lead’s sales owner.
Build the
foundation once.
Reuse connections and infrastructure across tools, with less integration work and maintenance. Start with one use case and add more as needed.
Use an existing warehouseConnections and data.
Use a compatible existing warehouse, or bring data into deepgreen’s open-format tables with maintained connectors and AI-written integrations.
Shared definitions.
Reuse metrics, relationships, and business rules across projects.
Hosting and execution.
deepgreen hosts applications and runs pipelines and automations.
Start with expert-built skills.
Add the team’s methods.
deepgreen AI applies built-in skills for API research, pipeline development, and validation. Extend them with the team’s methods and conventions.
Explore Data engineeringA governed workspace
for business teams.
Business users build and refine analyses and apps with deepgreen AI. Technical teams manage the shared connections, definitions, and access.
Test AI answers against known resultsManage access.
One permission model governs people, AI, data, and applications through groups, roles, and resource sharing.
Define allowed actions.
Grant automations specific tools. Review changes to shared definitions and documents before they apply.
Understand dependencies.
Trace the data used by apps and workflows. Inspect pipeline and automation runs.
Reconstruct
what happened.
Trace an outcome through data lineage, versioned code changes, and execution history. Inspect the inputs, rules, code, and actions behind the result.
Renewal follow-upRun history
Recorded outcome
Follow-up task created for Northstar.
Assigned to Maya in the CRM.
- 01
Inputs
Renewal in 21 days.
CRM data + product usage
Weekly active users: 80 → 48, down 40%. - 02
Rule
Renewal within 30 days + usage down at least 25%
Both conditions met - 03
Code
Renewal window widened from 14 to 30 daysrenewal_followup.py· v3 - 04
Execution
CRM task created → Maya
Action and run outcome recorded
Inspect the code change v2 → v3
Northstar’s renewal is 21 days away, inside the new 30-day window. Its 40% usage decline also meets the unchanged 25% threshold.
− renewal_window_days = 14
+ renewal_window_days = 30