About
More than 25 years inside the functions that run a business. Two decades of it in the room after the model already works.
Sales and revenue, marketing, finance, HR and workforce, customer operations, procurement — more than 25 years building the systems those functions depend on, two decades of it in data and AI. As a consultant embedded with the teams doing the work, not a vendor pitching a proof of concept from the outside.
25+ yrs enterprise delivery
20 yrs data & AI
Founder-led
Most of that time went into the unglamorous half of the job — the part that never makes it into a case study. Getting a model to work is the easy part. Getting it to survive contact with real operations — real data, real load, real edge cases nobody scoped for — is where the actual work happens, and where most of these programs quietly fail.
That is the half this practice is built on: the stretch after the demo, where a system has to hold up in production instead of a slide deck.
We also build VexioHQ, our product brand — an AI voice agent that answers inbound calls, places outbound calls, and books meetings directly on a real Google or Microsoft calendar. Not a summary sent afterward — an actual slot booked, at the time it says it booked. VexioHQ is early. A small number of design partners run it in production today, and we would rather say that plainly than imply a customer base that does not exist yet.
The thesis behind it is the same one more than 25 years of enterprise delivery taught us: voice agents and production AI are not really an “AI problem.” The model was never the hard part. Latency budgets, telephony edge cases, cold starts, timezone-boundary booking reliability, and messy real-world data are where these systems live or die. That operational reality — not a bigger model — is where we spend most of our time.
Who is behind DataTranquil?
A founder-led practice built on more than 25 years delivering the systems business functions run on — sales, marketing, finance, HR, customer operations — two decades of it in data and AI, hands-on; specifically the unglamorous half after a model works, where it must survive real operations. That lens now shapes VexioHQ, an early-stage AI voice agent that answers calls and books real meetings, built by a team that has already seen where these systems break.
| Area | What it means in production |
|---|---|
| Latency budgets | Where model round-trip time becomes a caller-perceptible delay, and what breaks first when it does. |
| Telephony edge cases | Carrier quirks, dropped calls, and DTMF handling — the phone network does not behave like a clean API. |
| Cold starts | The gap between a model responding and a system responding inside a live call. |
| Timezone-boundary booking | Calendar writes that have to resolve correctly across DST changes, midnight boundaries, and cross-timezone meetings. |
| Data quality | The messy real-world data pipelines a model never has to deal with in a demo. |
Get started
Start with an honest look at what production would actually take
An AI-readiness discovery before a build commitment — the same operational questions that decide whether a system survives contact with real use.