AI partner to a stealth-stage San Francisco startup building the next generation of voice-of-customer intelligence — an AI-native platform that consolidates support tickets, chat threads, and internal feedback into a single source of truth, scores every issue by real business impact on the customer base, and routes it with full per-account context across engineering, product, and customer-success teams. We own the generative-AI core end-to-end: production-grade RAG over heterogeneous customer evidence, an agentic LLM layer for deduplication, impact scoring, and cross-functional triage, and a measurement loop that follows every issue from first report through resolution and post-fix trajectory. Built to production-RAG and LLMOps best practices from day one.
The Challenge
In every product company, customer-success and support teams sit on the firehose of real user pain — tickets, chat threads, internal feedback, escalations — while engineering sits on logs, traces, and product telemetry. Neither side sees the other's data, and the result is well documented: high-impact issues drift for weeks while low-impact noise drives roadmaps, and the same problem gets re-reported across half a dozen channels before anyone connects the dots. Our partner, a stealth-stage San Francisco startup, is building an AI-native platform that closes that gap — turning scattered customer signal into ranked, business-impact-aware engineering work with full per-account context, severity tied to real exposure, and clear trajectory indicators showing whether each issue is improving or regressing. They needed an AI partner who could deliver the entire generative-AI core to production-grade standards from day one, not a notebook prototype.
Our Approach
We own the generative-AI core end-to-end. Retrieval is hybrid RAG over multi-channel customer evidence and internal product data — data-type-aware chunking across tickets, chat, and telemetry, dense + sparse retrieval, and cross-encoder re-ranking on a Milvus / Kafka / PostgreSQL backbone. An agentic LLM layer drives deduplication, business-impact scoring (severity, frequency, account exposure), and cross-functional routing across engineering, product, and support; a measurement loop tracks each issue's per-account trajectory from first report through resolution. Every component runs through continuous evaluation — Recall@k, nDCG, mAP, precision/recall/F1 — with regression gates and shadow-traffic A/B on every model and prompt change. We ship spec-first, with routing, caching, and graceful degradation built in to keep cost and latency in budget.
Results
The platform ingests live customer signal across every support channel alongside internal product data, deduplicates and clusters issues at scale, scores them by real customer impact, and routes them with full account-level context to whichever team can resolve them — closing the loop from first customer report through resolution and post-fix trajectory. Built to industry-best standards from day one — production RAG with continuous retrieval and generation evaluation, observable LLM operations with full cost and latency telemetry, spec-driven delivery — the system was designed to scale with the startup as it moves out of stealth, with the cost, latency, and quality telemetry needed to keep generative AI honest in production.
