Why we started DataChi

The clearest pattern in B2B sales is one nobody designs around. A rep’s week fills up with everything that isn’t selling — CRM updates after every call, follow-ups that go out late or not at all, prep work for a meeting that starts in twenty minutes, the same six prospect-research tabs open at the start of every day. Selling, the work that’s supposed to be the job, ends up squeezed into the gaps.

We started DataChi because that pattern doesn’t look like a productivity problem to us — it’s a capacity problem, and the difference matters.

What we kept watching

Before this company, we spent years inside sales orgs of every size — early-stage, growth-stage, and a few post-Series-C teams that should have known better. The same thing held everywhere.

More reps didn’t help. Each new hire arrived, ramped, and inside a quarter their week looked the same as everyone else’s: roughly forty percent on calls, sixty percent on the admin that surrounded those calls. Strong reps had it a little better. The structural problem was identical.

Better tooling didn’t help in the way it was supposed to either. The CRM still needed to be filled in by hand. The sequencer didn’t research the prospect. The conferencing tool produced a transcript that wasn’t a recap. Each tool solved a slice and left the connective tissue where it had always been: on the rep.

The conclusion we kept landing on was that the gap wasn’t a missing tool. There was a tier of work missing — the admin layer that should have sat between the rep and the system. In most sales orgs that admin layer was the rep.

Why now

The obvious part: language models got good enough. Not perfectly, not autonomously on every task, but well enough that a careful integration into the existing stack could do real damage to the admin tier. That’s the part everyone notices.

The part that matters more to us is the regulatory shift. The European market started asking serious questions about where AI workloads run and who keeps the data. We’re based in Luxembourg, which means GDPR and digital sovereignty aren’t a marketing line — they’re the environment we operate inside. Customer data sits in EU regions by default, with the current region visible inside the workspace. DPAs are signed before deployment, not after the first audit asks. Inference vendors are listed openly on the Trust & Security page. None of that was a feature decision; it was the starting point we had to build inside.

What we built

DataChi is the admin tier of a sales team, automated. A handful of specialised AI teammates handle prospecting, pipeline intelligence, follow-ups, CRM updates, meeting prep, and inbound support. They sit underneath the sales team you already have, absorbing the work that shouldn’t have been a person’s job in the first place.

The principle we keep coming back to is the one I gave the Luxembourg press a few weeks ago:

AI should not replace sales teams, but help them focus on what truly creates value. By automating certain operational tasks, we aim to give teams back the time they need to focus on customer relationships, sales strategy, and decision-making.

That’s the company. Everything else — the agents, the integrations, the EU architecture — is downstream of it.

Where we go from here

The first version of DataChi is in market. Six agents, the main CRM and outreach integrations, and the deployment model that lets teams pick where their data sits. Over the coming quarters we’re deepening per-vertical playbooks, broadening language coverage in the regions our customers operate in, and adding the next two agents on the roadmap.

If any of this lands with how you’re seeing your team’s week, the easiest next step is a 20-minute walkthrough against your actual stack and your actual data.