Use Crossbeam and Databricks together in your AI tools

Run your first AI workflow across both tools

Start with a prompt that brings Crossbeam and Databricks into the same conversation. Crossbeam brings the full partner picture: overlaps, performance, deal activity. Databricks brings your lakehouse: SQL on your tables, natural language answers from Genie, and search across your indexed content, all governed by Unity Catalog.

Account intelligence

Get partner context and plain-language answers on one account

Crossbeam shows which partners share the account, ranked by partner score, and the recent partner deals opened or closed won there. Databricks Genie answers natural language questions about that account, grounded in your organization's data and governed through Unity Catalog.

Example prompt

"Which partners share [Account] in Crossbeam, and what does our [Genie space] say about their revenue and usage over the last two quarters?"

Pipeline generation

Build a partner-sourced target list ranked by your lakehouse

Crossbeam finds the prospects that overlap a partner's customer list, segment by segment. Databricks runs the ranking SQL against the tables your Unity Catalog permissions already allow, so reps start with the accounts your own data favors.

Example prompt

"Pull our prospects that are [Partner] customers from Crossbeam, then rank them by [metric] with a Databricks SQL query on [catalog.schema.table]."

Deal acceleration

Spot quiet deals in your data and bring a partner in

Databricks queries your usage tables for open deals where activity has dropped off. Crossbeam recommends partners on those accounts backed by signals like recent wins, long-term relationships, and contacts you are missing.

Example prompt

"Run a Databricks SQL query on [usage table] for open opportunities with declining activity this month, then ask Crossbeam which partners have recent wins at those accounts."

Ecosystem building

Confirm partner attribution against governed revenue data

Crossbeam reports each partner's overlap counts, open deals, win rate, and attributed revenue. Databricks holds the booked revenue in your lakehouse, so you can check which partnerships actually move the number before you invest more.

Example prompt

"List our top 10 partners by attribution in Crossbeam, then compare against closed revenue for those shared accounts from [catalog.schema.table] in Databricks."

Co-sell execution

Prep a co-sell call with contacts and your account knowledge

Crossbeam returns the partner-shared contacts at the account, ranked by role signals like decision maker, economic buyer, and executive sponsor. Databricks AI Search pulls the relevant unstructured content from your vector search indexes, like support and knowledge base articles on that account.

Example prompt

"Before my co-sell call on [Account] with [Partner], pull the partner-shared contacts from Crossbeam and search our [index] in Databricks for recent notes on that account."