Your lead's phone rings in five seconds.
Not a chatbot. A system that calls the lead, qualifies them against your criteria, books the meeting and writes it to your CRM — while a human is still reading the notification.
Five systems we run. Two live for paying clients.
Two are running today for paying clients. The rest are on real infrastructure and labelled honestly — alpha is alpha, pre-launch is pre-launch. They are ordered below by production maturity, not by how good they look. We would rather you knew.
AI Voice Lead Qualification
Built for Estate 360, a private capital advisory firm. A prospect submits an enquiry; within five seconds the phone rings and an AI voice agent — she introduces herself as Shreeja — is on the line. It confirms the enquiry, checks the investment criteria, gathers details and books the briefing — then writes the qualified lead to the CRM and notifies the team. Bilingual, switching between Hindi and English mid-call. Voicemail, wrong number, bad timing and early hang-ups all resolve without breaking the flow.
Watch a live call →Form → booked under 2 min
Human hours zero
Every call recorded & transcribed
AI Outreach Engine
Built for CSS Global, a client since 2017. Pulls verified contacts from the client's sheet, has a model write a genuinely personalised email for each prospect — by name, company and industry — and sends from the client's own domain with human pacing that protects deliverability. It replaced a manual prospecting process end to end.
Watch it run →Reply rate 18%
Manual work per contact none
PropConnect — AI property matching
A platform for real estate brokers. A broker describes a requirement in plain language; the engine filters deterministically on budget, type, location and size, then scores what's left — and every match returns its reasoning, so the broker understands why and makes the call. Built on an exactly-once matching architecture: match identity is derived from the requirement and the listing, so a retry converges instead of duplicating.
Visit the platform →Matching exactly-once
Every match explains itself
MCP servers over production data
We build audited, read-only Model Context Protocol servers that let a model answer from your live records instead of from memory. Read-only by construction — there is no write tool, and no database reference escapes the repository layer, so the model cannot mutate production data rather than merely being told not to. Contact data sits behind a single tool that demands a written reason and logs it. The first time we pointed Claude at ours, it found three data-quality defects in our own product.
Watch Claude query it →PII audited capability
Reads budgeted per session
JobScout — conversational agent on WhatsApp
A job search assistant that lives entirely inside WhatsApp. Upload a resume, it builds your profile; type "find jobs", it returns live matching listings. It also ships the commercial layer: usage metered per user, quota enforced inside the conversation loop rather than on a dashboard, and an in-chat upgrade with a payment link when you hit the cap.
Watch the conversation →Metering enforced in code
Payments live
The model reasons. It never decides.
Most AI projects die in production, and they die the same three ways. These are the rules we build to, and they are why our systems are still running.
The LLM converses. The orchestration writes.
No probabilistic node ever decides a state change against production data. The model reasons and talks; a deterministic layer routes, retries and writes. That single rule is why these systems don't corrupt anything.
Make the failure impossible, not detectable.
PropConnect's match identity is derived from the pair it represents, so a retry converges on the same record instead of minting a duplicate. The best guardrail isn't an alert on a duplicate. It's an architecture where a duplicate cannot be created.
If you can't measure it, you can't ship it.
Every call recorded and transcribed. Every run logged with duration and outcome. Failures classified, not just counted — that's how we cut one agent's intent misclassification from roughly 30% to under 8% in a week.
Autonomy is mostly deciding what the agent may not do.
We deploy and operate Hermes — Nous Research's open-source autonomous agent framework — on the Claude API, with human-in-the-loop approval gates. If you want an agent that runs on its own, we can configure one. The hard part is never getting it to act; it's drawing the line it doesn't cross, and putting a human on the other side of it.
Two engineers. No account managers.
You talk to the people who write the code and operate it afterwards.
Nine years in outbound marketing and revenue operations, then he founded a real estate brokerage in 2020 — and found the bottleneck wasn't selling. Brokers were sitting on requirements and listings that matched each other perfectly and had no way to find out. In 2021 he started writing code to fix his own problem. That became PropConnect, and the constraints it forced — a match that cannot duplicate itself on retry, a score that has to explain why, a model that reasons but never decides — are the rules on this page. He was the revenue-operations person who automated his own job; the systems he now builds for clients are the ones he first needed himself.
Twelve years in data engineering, including multi-terabyte pipelines at Bank of America on Spark, Kafka and Hadoop. Brings the enterprise-scale data discipline — schema design, streaming, reliability — that keeps automation honest once the volume is real.
Voice Vapi · ElevenLabs · Deepgram · WhatsApp Cloud API
Automation Make.com · webhooks · REST · Apollo.io · Zoho · Razorpay
Engineering Node.js · React · Python · Firestore · PostgreSQL · GCP Cloud Run · Docker · Claude Code
Data Spark · Kafka · Hadoop · Hive · PySpark · NiFi
Fifteen minutes. We'll show it calling a live number.
Book the 15 minutes →Working hours overlap the United States, United Kingdom and Australia.
KyrosAura Corp Pvt Ltd · Registered in Mumbai, India