Databricks
Data warehouse
Usage data that never leaves the lakehouse never reaches the rep who needs to see it before the call. Databricks holds the raw event and usage data teams land there, and Revenue Labs reads it and ties activity to the right account and deal.
Website
Adoption·8 adoption reads this week
Reading Ravensmoor Analytics… ›
TriggerProduct usage · Ravensmoor Analytics - reporting workspace down this week · Same week as last quarter's seasonal dip
Deploy agentDeploying Adoption agent on the account
Use skillLoading skill · Adoption Analysis · Value Realisation Assessment · Customer Health Assessment
Reference memoryReading memory · Adoption · Products · Customer Outcomes
ReasoningRavensmoor Analytics' usage dipped the same week it did last quarter - seasonal on its own, except this time the stakeholder who drove it, Wren Halloway, left the company two weeks ago; this one gets flagged, last quarter's didn't
ActionCompose adoption read → surface inside Customer Meeting Preparation ahead of the call
✓Read in 6 seconds ›
Surfaces inCustomer Meeting PreparationviaSlackTeams
Revenue LabsAPP1:37 PM
LIVE
CUSTOMER MEETING PREP: Ravensmoor Analytics | Check-in Call
WHAT'S CHANGED
Reporting workspace usage dipped the same week it did last quarter, seasonal on its own
Wren Halloway, who drove that usage, left the company two weeks ago
STAKEHOLDERS
Gideon Park · Ops Director · [Primary contact]
ADOPTION & VALUEADOPTION
4 use cases assessed this cycle · 1 gap flagged for the call
Reporting workspace behind expected pace since Wren Halloway left; confirm who owns it now
RECOMMENDED DISCUSSION
Confirm the new workspace owner and reset the adoption pace with Gideon Park
Join MeetingView AccountView Adoption Detail
The tool alone > AI bolted on > AI-native
What changes when Databricks feeds a system that learns
Three jobs Databricks already does, and what each becomes when its evidence joins live company, contact and deal state.
01 | The tool on its own
02 | With AI bolted on
03 | AI-native
Lakehouse tables
Tables, files, and notebooks in one lakehouse.
Databricks stores tables, files, and notebooks in a lakehouse. Data teams still define schemas, permissions, and usable joins.
Genie answers questions over governed data.
Genie answers within Databricks and depends on the supplied data model. The result is not resolved to a live deal across the stack.
Lakehouse data becomes live state.
Lakehouse usage, product, and pipeline evidence is associated with its matching company, contact, or deal.
SQL and notebooks
SQL and notebooks turn raw data into analysis.
SQL editors and notebooks let teams query and reshape data. The analyst still checks the query and decides which output matters to revenue.
Genie Code assists with SQL and code.
Generated queries remain an analyst's draft inside Databricks. They do not change the reasoning used by every capability.
Queries feed deal context.
The resulting signal sits beside conversations, support, and stages, where the next capability can use it without rebuilding the join.
Governance and lineage
Ownership, lineage, and access rules travel with data.
Governed tables carry ownership, lineage, and access rules. A data owner still defines business meaning and reviews exceptions.
Genie explains a result in natural language.
Query explanations stay tied to governed tables and the query. They are not checked against closed-deal results.
Closed deals show which rows matter.
Closed outcomes are compared with warehouse evidence that preceded them, while the source remains read-only.
Runs on it
Capabilities that act on connected evidence
Sales
Sales Analysis
Active
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The review opens with causes diagnosed.
Deals, decisions and outcomes are examined against sales process and qualification logic.
↑
The analysis pins rate changes to causes.
It links a shift to the stage, stakeholder gap or process change.
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Planning starts from what worked.
Each proposed process, qualification or coaching change has outcome evidence.
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RevOps sees which choices caused the shift.
Recorded deal decisions show which choices moved the result.
Humans validate causal interpretation and approve changes.
Sales
Forecasting
Active
›
The forecast category stays current.
It maintains category, confidence and deal evidence between calls.
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The rep category is checked against evidence.
It names the gap between the submission and the deal evidence.
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The manager sees disagreements first.
Rep and system judgements open with their evidence and gap.
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The forecast explains movement and coverage.
Leaders see movement, gaps and roll-up implications with evidence.
Managers and leaders own final commitments and overrides.
BUILT FOR SECURITY, CONTROL AND SCALE
Connect data securely, capture every agent's activity, and enforce permissions with ease.
Learn about our security →CERTIFIED + COMPLIANT WITH:
ISO 27001:2022
GDPR
CCPA
Data Protection Act 2018
The GTM teams that learn fastest will win.
Connect your stack to a system that learns.