Enterprise Knowledge Search · Sales Automation
Tenali AI needed to help enterprise sellers answer deeply technical questions during live calls in under one second by searching Slack, PDFs, CRM data, and prior meeting content without the latency and synchronization overhead of a fragmented AI stack.
Reported impact
Tenali AI now delivers expert answers in under one second, reduced retrieval latency from 300ms to 100ms across P50, P95, and P99, increased live in-call usage of answers by over 40%, and saves dozens of DevOps hours each month while helping sellers preserve buyer momentum.
Tenali AI built a real-time retrieval-augmented generation stack on MongoDB Atlas so sales reps can get technical answers while a live call is still in progress. A native desktop app captures call audio and transcripts, then Voyage AI's voyage-4-large embedding model converts the conversation and enterprise knowledge sources into semantic vectors. MongoDB Atlas acts as the system of record for operational data plus rich multimodal metadata from Slack threads, PDFs, CRM data, past meeting transcripts, internal docs, and even video slides. MongoDB Vector Search retrieves the most relevant passage, thread, or slide in milliseconds and returns grounded context to the application, eliminating ETL hops between an operational store and a separate vector database. Because the same Atlas platform holds tenant data, vector indexes, and action context, Tenali can also trigger follow-on workflows such as updating Salesforce or preparing follow-up notes without adding more infrastructure.
Live call audio is captured and transcribed in Tenali AI's desktop app, embedded with Voyage AI, matched against vectors and metadata stored in MongoDB Atlas, and the retrieved context is returned to the seller as an in-call answer with optional follow-on actions against connected CRM and collaboration systems.
"By using a single data platform for both operational data and vector search, Tenali AI eliminated the need to manage complex Extract, Transform, Load (ETL) pipelines."
"By using the voyage-4-large model, Tenali AI reduced retrieval latency from 300ms to 100ms and lower across P50, P95, and even P99."
"Tenali AI built a real-time RAG stack using MongoDB Atlas and Voyage AI to provide instant, multimodal retrieval from a unified data platform."
"The flexibility of the MongoDB document model also allows Tenali AI to connect directly to a team's actual data sources—Salesforce, HubSpot, Slack, past meeting transcripts, and internal documentation."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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Tenali AI is a B2B software company in San Francisco, California.
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