Built for teams who live in data

From revenue reporting to marketing attribution, Contextary makes sure AI understands your data the way your team does.

RevOps & Sales

RevOps & Sales Analytics

Your pipeline metrics need to be right. Contextary ensures AI knows which stage means "committed" vs "forecast", how to calculate weighted pipeline, and which accounts to exclude from win-rate calculations. No more explaining your CRM's quirks every time you ask a question.

Q
"What's our weighted pipeline for Q2?"
Contextary automatically applies: stage probability weights, excludes churned accounts, uses fiscal Q2 boundaries, and filters to ACV > $0.
Pipeline velocity Win rates Stage conversion Forecast accuracy
Finance

Finance & Revenue Reporting

Board decks can't have wrong numbers. Contextary teaches AI your revenue recognition rules, currency handling, fiscal calendar, and the difference between bookings, billings, and revenue. When the CFO asks "what's ARR?", the answer is right the first time.

Q
"What was ARR growth quarter over quarter?"
Contextary applies: amounts in cents divided by 100, only 'active' subscriptions, fiscal quarter boundaries, excludes trial accounts and internal testing orgs.
ARR / MRR Revenue recognition Churn analysis Cohort retention
Marketing

Marketing Analytics

Attribution is messy. Contextary documents your attribution model, lead scoring definitions, and what "MQL" actually means in your system — so AI stops conflating signups with qualified leads and gives you numbers you can act on.

Q
"What's our MQL to SQL conversion rate by channel?"
Contextary ensures: MQL = lead_score >= 50 AND demo_requested, SQL = opportunity_created, uses first-touch attribution, excludes internal and partner leads.
Lead funnel Attribution Campaign ROI Channel performance
Data Teams

Data Team Onboarding

New analysts ramp faster when tribal knowledge isn't trapped in Slack threads and someone's head. Contextary is a living, queryable knowledge base that travels with your data — so your fifth analyst is as effective as your first.

Q
"Which tables should I use for customer churn analysis?"
Contextary recommends: subscriptions (canonical), explains churn = no active subscription for 30+ days, warns about the legacy customers_v1 table, and links to the churn metric definition.
Knowledge sharing Self-serve analytics Schema discovery Tribal knowledge

Your team's use case is next

If your team asks questions about data, Contextary makes the answers better.