Product Manager-Speech Analytics

As Product Manager for Agentic & Generative AI, Speech Analytics, you will own the end-to-end product vision, roadmap, and execution for Gnani's LLM-powered post-call and real-time analytics suite. This is a P&L-adjacent role where your decisions directly impact ARR, churn, and expansion revenue from enterprise accounts. You will work shoulder-to-shoulder with AI engineers, data scientists, and enterprise CX leaders to turn raw audio intelligence — processed through agentic AI pipelines — into measurable business outcomes.

You will be the person who:

▪ Defines what 'great' looks like for speech analytics in enterprise contact centers.

▪ Translates ambiguous signals from sales calls, NPS, and usage data into crisp product bets.

▪ Holds the line on quality and precision — in a domain where accuracy is mission-critical.

▪ Advocates relentlessly for the customer while keeping engineering velocity high.

KEY RESPONSIBILITIES

Product Strategy & Roadmap

▪ Own the multi-quarter roadmap for the Speech Analytics product, balancing near-term customer commitments with long-term platform differentiation.

▪ Define the product vision for Gnani's analytics capabilities — including transcription accuracy, LLM-powered call summarization, automated call categorization, sentiment and emotion detection, compliance audit copilots, and agent performance scoring.

▪ Conduct competitive teardowns of Verint, Observe.AI, Convin, Cresta, and emerging LLM-native analytics players; translate insights into defensible positioning.

Discovery & Customer Insight

▪ Run structured discovery with enterprise buyers across BFSI, telecom, and e-commerce verticals; extract signal from noise.

▪ Own the Voice of Customer (VoC) loop — from sales call notes and CS escalations to usage telemetry — and convert it into prioritized product hypotheses.

▪ Partner with presales and customer success to design proof-of-concept frameworks that accelerate conversion and land expansions.

Execution & Delivery

▪ Author crisp PRDs, user stories, and acceptance criteria that the AI engineering team can execute on without ambiguity.

▪ Own sprint priorities in collaboration with engineering leads; make hard trade-off calls with data and clear reasoning.

▪ Define and track the metrics that matter — transcription WER, category F1, latency p95, NPS, and adoption depth — and use them to drive iteration.

Go-to-Market & Commercial

▪ Partner with sales and marketing to develop product narratives, battlecards, and demo scripts.

▪ Support pricing strategy for analytics SKUs including usage-based, seat-based, and outcome-based models.

▪ Represent the product in enterprise deal cycles — including RFP responses, CXO-level demos, and technical due diligence sessions.

Platform & Ecosystem

▪ Collaborate with the Platform team to ensure Speech Analytics integrates cleanly with Inya (voice bots) and Agent Assist for a unified data story.

▪ Work with AI research to productize advances in Indic-language ASR, speaker diarization, agentic call analysis workflows, and LLM-based call summarization and insight extraction.

▪ Define API contracts and data export standards for enterprise integrations with CRMs, QA tools, and BI platforms.

QUALIFICATIONS

Must Have

▪ 6–8 years of product management experience, with at least 4 years in B2B SaaS targeting enterprise customers.

▪ Proven track record of owning and shipping complex, data-heavy products end-to-end — from discovery through adoption.

▪ Strong analytical instincts: comfortable writing SQL queries, reading dashboards, and making prioritization decisions grounded in data.

▪ Excellent written and verbal communication; able to write a tight one-pager for a CXO and a detailed spec for an engineer with equal ease.

▪ Deep familiarity with enterprise sales motions — you've sat in on demos, RFPs, and procurement discussions, and you know how enterprise buying works.

▪ Experience working directly with ML/AI teams on model evaluation, accuracy trade-offs, prompt engineering, and productization of generative and probabilistic AI outputs.

Highly Preferred

▪ Domain experience in speech analytics, conversation intelligence, or contact center technology (Verint, NICE, Genesys, Observe.AI, Convin, or equivalent).

▪ Hands-on experience with ASR, NLP, or LLM-based products; able to engage meaningfully with engineers on model performance, RAG pipelines, prompt design, and inference constraints.

▪ Exposure to telephony environments — SIP, PSTN, 8kHz audio, G.711 — and the quality constraints they impose on speech AI.

▪ Experience building or evaluating quality management (QM), CSAT prediction, or compliance audit features for contact centers.

▪ Familiarity with Indian enterprise buyers in BFSI, telecom, or large-scale e-commerce.

Nice to Have

▪ Prior experience in a voice-first AI startup or a conversational AI platform company.

▪ Understanding of multi-language/code-switched speech challenges in Indian language contexts.

▪ Background in building BI/analytics features: dashboards, filters, exports, alert frameworks.