Tenali AI powers in-call intelligence with MongoDB and Voyage AI

THE CHALLENGE

Eliminating the “I’ll get back to you” deal-killer

For Aniket Patel, the founder of Tenali AI, the inspiration for his company didn't come from a lab, but from the stinging loss of a $2 million enterprise deal. After scaling a $100M franchise at VMware, Patel knew that momentum is the most fragile element of a transaction. During a critical live call, a buyer asked a deeply technical question that Patel couldn't answer on the spot. He uttered the phrase every salesperson dreads: “I’ll get back to you.” By the time he followed up with the correct information 24 hours later, the emotional energy of the deal had evaporated, the stakeholders had moved on, and the deal eventually stalled.

Patel realized that while the market was flooded with “post-call” intelligence tools that summarize meetings after they end, there was a massive vacuum for real-time support. To solve this, he taught himself software engineering and AI, and built Tenali AI—an in-call intelligence platform. The technical challenge, however, was immense. To be effective, Tenali AI had to listen to a live conversation, transcribe audio, search through a massive and disorganized enterprise knowledge base (including Slack, PDFs, CRMs, and past meeting transcripts), and surface the exact right answer in under one second.

In the world of live conversation, two seconds is an eternity; three seconds is an awkward silence. Patel needed a database architecture that could handle the fluid, unstructured nature of sales data while providing the sub-second latency required for a truly “live” experience. Traditional relational databases were too rigid for the evolving schemas of AI applications, and fragmented stacks—using one provider for operational data and another for vector search—introduced “architectural tax” in the form of synchronization delays and management overhead. Tenali AI needed a unified, production-grade platform that could scale as fast as a live conversation.

OUR SOLUTION

Using MongoDB Atlas and Voyage AI for Tenali AI

To build a solution capable of sub-second retrieval, Tenali AI consolidated its infrastructure on MongoDB Atlas, leveraging MongoDB Vector Search as the core engine of its Retrieval-Augmented Generation (RAG) stack. The architecture is designed for extreme speed: a native desktop app captures live audio, transcribes it, and processes it through Voyage AI’s high-performance embedding models to capture semantic understanding.

Tenali AI’s choice to use Voyage AI was driven first and foremost by accuracy, a non-negotiable requirement for high-stakes enterprise deals. For product viability, however, latency was just as critical; to power a truly “live” experience, the system had to keep pace with natural human conversation. After testing other models—including Google Gemini and OpenAI’s Ada—Patel found that the voyage-4-large set the standard for accuracy and rapid response times. Quantization and flexible output dimensions allowed Tenali AI to further tune its retrieval engine for extreme efficiency without sacrificing semantic depth. The accuracy improvements proved out in behavior, not just benchmarks: after switching embedding models, sales reps actively using Tenali's answers on live calls increased by over 40%—a signal that reps trust the answers enough to use them in front of a buyer. By using the voyage-4-large model, Tenali AI reduced retrieval latency from 300ms to 100ms and lower across P50, P95, and even P99—a critical benchmark for live, in-call intelligence where every millisecond counts. The flexibility of Voyage AI's embedding models also allows Tenali AI to use different models for different use cases, further tuning performance without sacrificing accuracy.

MongoDB Atlas serves as the “source of truth” for Tenali AI’s entire ecosystem. Because Atlas is a developer-focused document database, Tenali AI can store rich, multimodal metadata alongside vector embeddings. When a seller is asked a question about a specific product feature, MongoDB Vector Search doesn't just find a text snippet; it can point to a specific slide within a recorded video or a specific thread in a Slack channel. 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. This consolidation allows the team to focus on refining their user experience rather than troubleshooting database synchronization issues.

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. Reps can ask questions in plain English and get real answers pulled from their live pipeline data, deal history, or product documentation in seconds. This isn't theoretical AI—it's grounded in the customer's own systems. And because Tenali AI is already connected to these systems, the natural next step is agentic: updating a deal stage in Salesforce, sending a follow-up email, or logging notes—all handled by Tenali AI without the rep lifting a finger. The result is a seamless loop from real-time intelligence to autonomous action, powered by a single unified data platform.

OUTCOME

Maintaining Deal Momentum with Sub-Second Intel

The move to a unified MongoDB Atlas and Voyage AI stack has transformed Tenali AI from a promising prototype into a production-grade enterprise tool. To ensure the system could keep pace with human conversation, the primary benchmark for success was latency, and the results are definitive: Tenali AI now delivers expert-level answers in under one second. This speed allows sellers to maintain their “expert status" during live calls, answering technical objections instantly and keeping the deal moving forward without the friction of a follow-up email.

Beyond speed, MongoDB Atlas's reliability has enabled Tenali AI to achieve scale for a lean startup. Patel estimates that using a fully managed service saves the team dozens of hours of DevOps work every month, allowing them to reinvest that time into product innovation.

Customer trust in Tenali AI also increased significantly. Because MongoDB Atlas offers robust security and data isolation, Tenali AI can confidently handle sensitive enterprise data from multiple tenants. Sellers using the platform report a newfound “superpower" in meetings, feeling equipped to handle any technical curveball. That confidence is backed by data: since switching embedding models, live in-call usage of Tenali's answers has increased by over 40%, the clearest proof that reps trust the platform when it matters most.By eliminating the “I'll get back to you" moment, Tenali AI helps organizations increase their win rates and shorten their sales cycles. As Patel notes, real AI products are built for the moments that matter most; with MongoDB and Voyage AI, Tenali AI ensures those moments are never lost to silence or delay.