I build voice agents whose only job is to break other voice agents.

• A second voice agent on Pipecat and Twilio that places real phone calls into the production agent and tries to make it fail.
• Suites run from 40–50 cases for simple IVR routing to over 1,000 for agents covering fifty use cases.
• Four personas — compliant, rushed, confused, hostile from the third turn — run against every case.
• Every case runs five times. Agents are non-deterministic, so a single pass tells you nothing; the repetition method is adapted from tau-bench.
• Endpointing moved to Deepgram Flux, built for real-time turn detection, bringing detection to roughly 200ms — and eager end-of-turn prediction lets the model start work before the turn is confirmed, hiding part of the remaining latency.
• Providers benchmarked before the production agent shipped: Cerebras, Groq, OpenAI. Settled at 1.2 seconds end to end.

I built the evaluation function from nothing, and the company now runs on it.

• There was no eval charter before. The simulator above is one part of this.
• Before an agent ships: knowledge base and prompts built from the customer's own documents, cases written, simulations run, failures localised, every prompt change regression-tested.
• After it ships: calls scored against per-agent criteria — containment, correct routing, fabrication, interruption handling, identity verification, escalation cause.
• The judge is itself evaluated. Meta-evals score its output for faithfulness, completeness and conciseness, and its verdicts are checked against human-reviewed traces, with the judge prompt corrected where the two disagree.
• Prompts and model configuration are served from Langfuse, so a product manager can change a prompt or swap a model and watch production behaviour without waiting for an engineer.
• An MCP server, so running an eval doesn't mean leaving the tool you're already in.

I took the model out of most of this problem and accuracy roughly doubled.

• V1 photographed a phone box and asked a model to read IMEI, brand and SKU from it, with a separate prompt per brand. It reached 40–50%.
• V2 ran OCR on-device through ML Kit and matched per-brand regex against the result — the printing on a box is structured, and structure can be read deterministically.
• The disambiguation is the hard part, not the language: Samsung SKUs begin "SM-", and so do other strings printed on the same box.
• The model was kept for exactly one brand — the one that prints no SKU at all — resolved with grounded retrieval.
• 98–99% accuracy at roughly 100,000 requests a day, across 40,000+ merchants, with ingestion down to about eight seconds.

I built and ran a marketplace that made money without ever raising any.

• Cofounded with two others. For the first months I was the only engineer, and built all three surfaces: the customer marketplace, the lender dashboard, and the internal admin. Later I hired interns and managed them.
• Four categories, chosen deliberately over breadth.
• KYC on both sides — Aadhaar and OTP-verified PAN, with live webcam capture instead of an uploaded photo.
• Dockete, because off-the-shelf tools model a sale as an event, while a lease is an item with a time-varying availability calendar — it comes back, it can be double-booked, and it has to answer what's free on a date four months out. It also tracked payment status across every active lease.
• I also took the support calls and the sales calls: complaints, new leads, and vendor onboarding in person.
• Built while finishing a degree.