Hamming AI

What do they actually do

Hamming AI sells a SaaS platform that automates testing, analytics, and production monitoring for voice AI agents that run over phone and WebRTC. Teams connect their agent via SIP/WebRTC or one‑click integrations (e.g., Retell, Vapi, LiveKit), then spin up realistic end‑to‑end calls without writing custom test harnesses. Hamming emphasizes quick time‑to‑first‑test (they show setup in minutes) and supports CI/CD hooks and APIs so tests can run on every PR or release (product, site).

Once connected, Hamming auto‑generates hundreds of scenarios (happy paths, edge cases, accents, background noise, IVR/DTMF flows), can run thousands of calls in parallel for load/stress testing, and evaluates calls on audio‑native metrics like latency, interruptions, hallucinations, sentiment, and compliance. They report 50+ built‑in metrics and ~95–96% agreement between LLM‑based scoring and human evaluators. In production, teams can continuously score “heartbeat” calls, get Slack/PagerDuty alerts on degradations, and one‑click convert failing calls into replayable regression tests. Enterprise features include SSO/RBAC, audit trails, data residency, and SOC 2/HIPAA BAA options (automated testing, site, blog, case studies, enterprise, FAQs).

Who are their target customer(s)

• Product manager for a phone/WebRTC agent: Needs predictable, reliable agent behavior but can’t easily reproduce or quantify failing production calls, slowing fixes and releases. Wants replayable failures and automated scenarios to speed validation.
• QA or engineering team responsible for voice test coverage: Spends time hand‑writing scripts and still misses realistic variations like accents, background noise, IVR/DTMF flows. Needs automated scenario generation and scalable runs to catch edge cases earlier.
• SRE/observability team for voice services: Hard to detect audio‑specific failures, trace ASR/LLM/TTS/telephony issues, or get timely alerts when quality degrades. Needs continuous scoring, component‑level tracing, and alerting.
• Compliance/security/audit team in regulated industries: Must prove calls met regulatory requirements and detect PHI/PII leaks or scripted noncompliance. Needs audit trails, configurable metrics, and enterprise controls (SOC 2/HIPAA).
• Contact‑center operations/performance engineering: Needs to validate capacity, latency, and agent behavior under load but can’t easily simulate thousands of concurrent callers or complex patterns. Wants large‑scale, configurable load and stress testing.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led outreach to YC/startup network and warm intros; run a live pilot that connects to the customer’s agent and produces first test results in minutes, converting real failing calls into replayable regression tests to show immediate value (site, automated testing).
• First 50: Leverage reference customers and publish short case studies/how‑tos with measured outcomes; run targeted webinars and HN/Product Hunt follow‑ups, open self‑serve trials with CI/CD hooks and one‑click integrations to reduce procurement friction (FAQs).
• First 100: Stand up an outbound motion by vertical (contact centers, healthcare, finance), emphasize compliance/auditability in playbooks, and build a partner channel with telephony/CCaaS/SDK vendors; scale inbound via targeted content/SEO and paid capture while leaning on SOC 2/HIPAA for regulated deals (enterprise).

What is the rough total addressable market

Top-down context:

Hamming sits across conversational AI, CCaaS, and software testing. Conversational AI is estimated at about $11.6B in 2024 and growing quickly, CCaaS is a sizable and expanding market, and software testing is ~ $55.8B in 2024 (Grand View Research – conversational AI, Grand View Research – CCaaS, Global Market Insights – software testing).

Bottom-up calculation:

Using the call‑center AI market as the closest proxy for near‑term voice‑agent spend (~$1.99B in 2024), and assuming 5%–15% of that software spend is allocated to QA/observability, the near‑term serviceable market is roughly $100M–$300M annually (Grand View Research – call center AI).

Assumptions:

• Baseline market for voice/call‑center AI software is ~ $2B in 2024.
• QA/observability tooling captures 5%–15% of platform/AI spend in the near term.
• Focus is on teams deploying phone/WebRTC voice agents (contact centers and regulated enterprise use cases).

Who are some of their notable competitors

• Cyara: Established CX assurance platform for contact centers with automated IVR and call testing/monitoring; notable because it already owns QA/monitoring budgets for telephony and IVR, though not built specifically for AI voice agents.
• Spearline: Global call quality and DTMF/IVR testing and monitoring; relevant for validating telephony paths and audio quality that AI agents depend on.
• testRTC: WebRTC testing and monitoring platform used by real‑time comms teams; overlaps with Hamming’s WebRTC load and quality testing needs.
• Observe.AI: AI‑driven call center QA/compliance from recorded conversations; adjacent because buyers use it for quality scoring and compliance, though it’s focused on post‑call analytics rather than pre‑prod testing of AI agents.
• CallMiner: Conversation intelligence and compliance analytics for contact centers; notable incumbent in QA/compliance budgets that Hamming’s monitoring may also target.