The framework
Data → Automation → AI Agents.In that order, for a reason.
Most data projects fail because someone skipped a step, automating before cleaning, deploying agents on a base in chaos. Our framework runs in sequence because each stage is the condition for the next, not because it looks tidy on a slide.
Why AI projects fail
An agent on a broken base doesn't fix anything.
It reproduces the errors mechanically, at greater scale, with less friction to spot them. Executives feel the pressure to ship AI agents now, but skipping the data layer doesn't save time, it guarantees a rebuild.
of enterprise AI projects fail for lack of adequate data infrastructure.
Source · RAND Corporation
The data debt
Four ways the debt builds up, quietly.
Data collected without cleaning or normalization
Years of accumulation, zero structured maintenance.
Sales processes evolve, the CRM doesn't follow
The tool stays frozen while the way you sell keeps moving.
Fields, tags and rules multiply without governance
Every team adds its own, no one arbitrates.
No continuous update of contacts and accounts
Data ages with no process to refresh it.
“Skipping a step doesn't save time. It guarantees you'll redo it.”
The sequence
Three stages. Each one earns the next.
This isn't a menu. Govern and clean, keep it alive, then deploy agents on a base that holds. The base is your source of truth: a CRM, an ERP or a custom app. The sequence doesn't change; what it runs on can.
Data
Govern, audit, clean. A structured, reliable base, the only thing an agent can safely run on. Everything downstream presupposes this.
Explore 02 · Keeps it aliveAutomation
Cleaning is a one-time event; decay is continuous. Automation keeps the base current with no manual upkeep, presupposes a governed base.
Explore 03 · Works on topAI Agents
Agents that ship and hold in production, presupposing data that's clean and kept clean. This is where the ROI lands.
ExploreCase studies
They had the same problem. Before you.
Four real engagements, measured results. The sector, the problem and the key figure are visible here; the full method is available by leaving your email.
B2B Technology · Retail · Salesforce CRM
A Salesforce CRM covering most of the French retail market. Inconsistent data, duplicates never analyzed, campaigns sent blind.
Result
34% of accounts were duplicates
B2B Media · Risk intelligence
A decision-maker database built over years, that nearly 7 in 10 contacts could not be reached at. Unverified emails, no segmentation.
Result
70% invalid contacts · -80% bounce rate
Consulting firm · Finance & accounting placement · HubSpot CRM
Recognized expertise, zero acquisition process. Pipeline 100% dependent on the network. No qualified database, no inbound CRM flow.
Result
200+ qualified meetings generated
SaaS · Cybersecurity recruitment · Boond Manager CRM
A network of cyber consultants on one side, companies hiring on the other. The link between the two was made entirely by hand.
Result
10 to 15 h of manual prospecting saved per week
Start where it matters: the data.
We'll tell you honestly which step you're actually on, and what it takes to get to agents that hold.