What if the AI breaks and I lose a customer?

You set the confidence threshold. Below it, the AI stops and pings the human you named: your assistant, your business partner, you. Above it, it acts and leaves a log you can audit any time. Your customer never sees a mistake first. You do, and you decide.

One click pauses outbound. Inbound keeps logging so you don't lose context, and it resumes only when you say so. No black box, no waiting on us.

Who reviews the AI's work?

You name the human reviewer in setup. They get pinged on every below-threshold message. For the first two weeks of any pilot, you can hand the review to us while you decide how you want it to run long-term.

What about data privacy and customer info?

Your customer data stays in the tools it already lives in: your WhatsApp, your CRM, your calendar. No separate warehouse. Per-pilot DPAs are signed before any production data moves.

How long until I see it work?

You see value in week one. First useful agent on your data in 5–10 working days. One workflow first. The rest only after you decide it's earning.

What if I want to leave?

Pilot ends, your data stays where it was: in your tools. You walk out with the audit log, the prompt library, and the config. Integrations unpoint. No lock-in.

Forward deployed engineering for AI agents that have to work

The demo is the easy part

If your AI model works in a controlled demo but stalls the moment it meets real customer data, real WhatsApp threads, and real workflows, you already know the demo was the easy part.

I'm available for Forward Deployed Engineering roles through SME Analytica. I take the FDE seat inside your team to close that gap directly: discovery, scoping, architecture, deployment, and weekly iteration from live logs.

This is the work I have been doing for the last two years. Mostly solo, close to customers, with enough production mess to keep the architecture honest.

Why FDE matters in AI now

Forward deployed engineering used to sound like a Palantir-specific job title. In AI, it is becoming normal. OpenAI launched a Deployment Company in May 2026 around engineers working close to customer deployment. Anthropic describes FDEs as builders who work inside customer systems and ship production AI applications.

That shift makes sense. AI products fail when a clean demo meets WhatsApp messages, half-complete PDFs, bad calendars, missing API fields, unclear handoffs, and staff who still need control.

An FDE turns that mess into a working system. They see the workflow, write the code, ship the integration, read the logs, and adjust before the customer gives up.

What I have shipped

The main example is Conversa, our WhatsApp AI system for real estate teams. It uses a 4-agent architecture over live WhatsApp traffic: router to calendar, property, or conversation. One client runs more than 1,000 customer conversations a month through it, with API and calendar integration.

We also compared GPT, Gemini, and Claude models on real customer traffic. Buyers asked for photos, visits, prices, location, availability, and follow-up in messy Spanish and English. Latency mattered. Tone mattered. Tool calling mattered. Recovery after a bad turn mattered.

I also shipped Facturia, a WhatsApp invoicing product for autonomos in Spain. It creates invoices and tracks expenses from typed messages, voice notes, PDFs, and screenshots. The hard part is extracting the right data, keeping the user in control, and fitting real invoicing habits.

The consulting shape

I can take the FDE role directly inside your team. The work usually looks like this:

Map the workflow with the people who do the job.
Scope the first production slice small enough to ship and real enough to matter.
Design the agent, tools, data flow, fallbacks, and human handoff.
Deploy into the customer environment, not a demo sandbox.
Review logs weekly and improve the system from live behavior.

I work remotely with teams anywhere, and travel when being in the room will shorten the loop. Native English, C1 Spanish.

Who this is for

This is probably a fit if you already have an AI product or model that works in controlled conditions, and the next bottleneck is customer deployment.

Maybe your team needs the first real implementation. Maybe a customer wants a custom workflow before they sign. Maybe sales keeps hearing "can it work with our system?" and the honest answer is "yes, but someone needs to build the bridge."

Talk to SME Analytica if this sounds like your problem.