Hamming

We’re hiring a GTM Engineer at Hamming. Not an SDR. Not an AE. - You’ll do outbound, discovery, demos, closing, and expansion; basically, whatever gets customers successful. - Voice AI is a technical sale. You’ll be on screen shares with CTOs and engineering teams. Atypical backgrounds encouraged: ex-founders, lawyers, FDEs, chiefs of staff from small teams. Send us a one-pager, PDF, or Google Doc. Resume optional. 1. Why Hamming? 2. Why you? 3. What would you do in your first 30 days? 4. One strong opinion on voice AI.

"Not only can we have Hamming do it instead of us, we can have Hamming do it four or five times, all at once instead of having one person call and do it one time." - Tosh Toida, QA Lead, Mia

"I find talking to the agents completely socially exhausting. The platform offers all of these different personas that the team is not able to replicate." - Blake Jones, AI Engineer, Basata

Voice AI breaks differently than text AI: silent failures, exhausted QA, edge cases that only show up at scale. Three customer quotes from the last quarter:

Building voice agents is 70% testing. "It's like going from manual labor to using a tractor. You can prompt an agent in 30-45 minutes, but testing takes the next 2-3 hours. Building voice agents is 70% testing. Hamming makes that 70% manageable." Ahmad Rufai Yusuf, Forward Deployed Engineer at Bland Labs: Manual testing eats engineering days. Hamming runs them in parallel. Read the Bland Labs case study. 👇

Three Bay Area events this week. Three different rooms shipping the same hard problem: agents that hold up in production. SaaStr AI Annual is where the B2B SaaS buyers are. LangChain Interrupt is where the agent-debug crowd is. AI Council is where the agent-eval builders are. If you're building voice or agentic features, find us. 👇 saastrannual.com · interrupt.langchain.com · aicouncil.com/sf-2026

Voice agents that survive production are tested like infrastructure, not chat. Regression suites on every change. Simulation across thousands of personas. Real-time drift monitoring. Incident response runbooks. Production voice quality in 2026: hamming.ai/blog/best-prac…

95-96% agreement with human evaluators. That is our automated voice agent eval accuracy across 4M+ production calls. If your eval system disagrees with humans, you can't trust it. If it agrees but doesn't scale, you're back to manual QA. We solved both.

Voice agent latency isn't one number. It is a chain. ASR finalization, LLM TTFT, TTS TTFB, audio buffering, network jitter. Optimizing one segment without measuring the others can make perceived latency worse. Full breakdown: hamming.ai/blog/voice-ai-…

@twilio ConversationRelay crossed 25M+ developer minutes. Voice plumbing is solved. The bottleneck moved up the stack: STT errors at authentication, silent model updates, background noise dropping digits. These don't show up in load tests. They show up in production.

Voice AI teams in production: What is the failure mode you can never reproduce in dev but always see at scale? We've seen accent edge cases, API rate limits, codec-specific TTS bugs. What's yours?

We'll be at Stripe Sessions in SF, Apr 29-30 at Moscone West. Voice + payments are converging. When a voice agent takes a payment, semantic quality isn't enough. You need numeric fidelity and assertion-level verification. DM if you're there.

Most voice agent teams discover production failures from customer complaints. Days late. Traditional APM tools weren't built for voice. ASR drops, turn-taking glitches, and mid-call tool failures all slip through. Full guide: hamming.ai/blog/native-vo…

Most voice agent teams cannot define WER, TSR, and FCR on the spot. These are the metrics that decide whether an agent ships or leaks revenue. With Project Voice, Stripe Sessions, SIGNAL, and Cerebral Valley all in the next week, the metric conversations are about to get sharper. hamming.ai/blog/voice-age…

Regal shipped Copilot: voice agents that improve from real call data. ElevenLabs shipped on-prem + on-device. Look unrelated. They aren't. Both bet that the next phase of voice AI is not a better model, it's a better deployment environment. Model is commodity. Deployment is the product.

We'll be at Twilio SIGNAL in SF, May 6-7 at the Marriott Marquis. Telecom and voice AI are converging faster than any other layer. Telnyx hosted LiveKit. Twilio + Genspark. ElevenLabs in watsonx. If you run voice agents in production, DM us and let's compare notes on the show floor.

Retell AI at $50M ARR. Wing VC Enterprise Tech 30 now includes Retell, ElevenLabs, and Decagon. Conversational AI is no longer an emerging category. It's infrastructure. Buying committees changed. Quality bar moved. Both are testing problems, and most teams are under-invested there.

YC W26 shows voice AI splitting into build vs. verify. Build: Callab, Samora, Leaping, Vogent. Verify: Sentrial, Roark, Cekura, Hamming. Same pattern as cloud 15 years ago. First the platforms, then the testing/monitoring stack. Both sides are getting funded.