Service
An agent that does the work, not just talks about it.
An AI agent doesn't just answer questions. It carries out a task end to end: it reads, decides by rules we define together, acts in your systems and reports back. In a project Shahar Peer led before founding Pele, at a medical equipment company, one agent built into the existing lead process lifted lead conversion by 56%.
Tasks with a start, an end and rules.
An agent suits a task with a clear goal and a defined way to reach it, even when every case is a little different. It works inside the tools the team already uses, and hands anything outside its limits to a person.
- First response to leads: replying, qualifying and booking a meeting
- Tracking orders and shipments and keeping the customer updated
- Handling recurring requests from customers and distributors
- Preparing documents and files on request, from several systems at once
Process first, tool second, oversight always.
The risk with an agent isn't that it makes mistakes. Every system does. The risk is that it makes them confidently, in convincing language, and nobody notices. So before a single line of code we define what a good outcome looks like and how we'll measure it, and after launch we read real conversations, trace what the agent decided and why, and compare it with what actually happened.
- Written limitsWhat the agent may do on its own, and when it hands over to a person.
- A full logWhat was sent, what came back and who approved it, for every action.
- Rounds of improvementAn agent always loses its first real conversation. The work is the rounds that follow.
Five steps, and each one earns the next.
- Diagnose
We go inside one department and pick the process that pays for itself fastest. And when there isn't one, we say so.
- Data, permissions & integration
We map what data exists across the ERP, the CRM and whatever comes back from the markets, at what quality, and who is allowed to reach it. That decides whether this is an eight-week build or an eight-month one, and we tell you before we start.
- Implement
We build the solution into the existing systems and run it with the real team, on the volume they handle every day.
- Prove
One metric, agreed before we start, measured against the day-one baseline.
- Expand, embed & monitor
Only once the number is in. The next process, with training and internal ownership.
- Permissions mapped before implementation
- No training on your data, and that is in writing
- Identifying information masked before anything reaches a model
- Data residency to your requirement, including GDPR
- A full log of what was sent, what came back and who approved it
Related
- Internal assistantsThe answer already exists somewhere: in a procedure, a spec sheet, a customer file or a veteran's head. An internal assistant connects that knowledge to your teams, answers in plain language and shows where the answer came from.
- Process automationOrder entry, CRM updates, quotes and reports that someone copies by hand. We take one process that costs you hours every week and rebuild it inside the systems you already run.
- A 56% lift in lead conversion at a medical equipment company.Case study. A company with a large distributor business and an early direct-to-customer channel, which wanted to reach the next stage without breaking what already worked. A project Shahar Peer led before founding Pele.
- Don't hammer a nail with a broom.Code is no longer the obstacle, and neither is implementation. What decides the outcome now is knowing which tool fits which job.
- How do you know what your AI is actually doing?The risk isn't that AI makes mistakes. It's that it makes them confidently, and nobody notices.
- Who are you in this scene?We got the casting of The Matrix wrong, and the mistake explains why most companies use AI badly.
Tell us the process that costs you the most hours.
Twenty minutes with both of us. You'll leave knowing whether AI belongs in that process, including when the answer is no.