AI agents and automation, built on data that actually works.
Your competitors are deploying AI agents into their CRM. Most will fail — not on the model, on the data underneath it.
Why most AI projects fail
of enterprise AI projects fail for lack of adequate data infrastructure.
Source · Informatica — Enterprise AI Agent Engineering 2026
The real bottleneck
Bad data is the #1 documented cause of failed AI projects.
It's not the model. AI agents amplify whatever they run on. Point one at a CRM full of duplicates, dead contacts and empty fields, and you've automated the errors — faster, at scale, with less friction to catch them. We start where everyone else skips: the data.
The cost of inaction
What dirty data quietly costs you.
per rep spent fixing and entering CRM data instead of selling — ~500 hours a year.
Source · Optifai — B2B Sales Ops Benchmark 2025
of companies lose more than 10% of revenue a year to poor CRM data quality.
Source · Validity — State of CRM Data Health 2022
of reps already using AI agents say bad data is sabotaging their sales.
Source · Salesforce — State of Sales 2026
The framework
From a messy CRM to deployed AI agents. We walk the whole path.
Three poles, in order. Each one is the condition for the next.
Why us
We sit in the gap.
AI agencies don't have the commercial layer. Sales agencies don't have the AI layer. We have both: engineers who ship agents and automation, and people who've run B2B sales. We talk data to a developer, business to a VP Sales, and AI to both.
The sales layer
We've built and run B2B sales operations. We know what a pipeline, a forecast and a rep's day actually look like — so what we ship fits how you sell.
The AI layer
A collective of engineers specialized in Claude — API, MCP, agent design — deploying in real B2B contexts, not demos.
The collective
The people behind the work.
A French collective led by three heads — data, operations and automation — backed by 10+ engineers specialized in the Claude API, MCP and agent design.
Raphaël Masson
Founder & Head of Data
Data strategy, CRM and AI-agent deployment for B2B sales teams.
Geoffrey Vidal
Head of Operations
Systems architecture, operational processes and support functions. Salesforce expert.
Thomas Alpa
Head of Automation
Automation systems and workflow orchestration across the CRM stack.
The agents
Three agents. A CRM that works for you.
Each agent owns one dimension of your CRM — continuously, with no manual upkeep.
SDR Agent
Qualify and follow up in under 60 seconds, 24/7.
Every inbound lead loses most of its conversion odds once first contact passes five minutes. The SDR agent responds in seconds, qualifies against your ICP, and books the meeting straight into the right rep's calendar.
Leads contacted within 5 minutes are far more likely to convert — most teams never hit that window.
Source · Harvard Business Review — The Short Life of Online Sales Leads
Data Agent
Your CRM stops degrading after the cleanup.
Data quality decays 20–30% a year without maintenance. The Data agent runs continuously: deduplication, enrichment of critical fields, job-change detection, validation at entry.
20–30% annual data-quality decay without active maintenance.
Source · IBM — Data Quality Benchmark
Pipeline Agent
Decisions on real, real-time data.
Automatic deal briefing before every call, pipeline updated without manual entry, reporting generated for management, alerts on stalled deals.
Reps spend only ~28% of their week actually selling.
Source · Salesforce — State of Sales 2025
Three agents cover ~90% of recurring B2B CRM cases. For the remaining 10%, we build custom — on a base that can actually support it. All agents are built on Claude (Anthropic).
Already deployed in
qualified B2B databases
CRM automations
contacts cleaned
Claude API / MCP specialists
The model
US-level engineering, European cost structure.
A French collective of senior engineers, working value-based and project-based. You get the depth of a US firm without the US burn rate — and a team that treats your data like its own.
FAQ
Common questions.
It depends on your CRM. If it has never been structured, we start with governance. If the base is messy but the rules already exist, we start with the audit. The first call defines the entry point — you don't commit to the whole path up front.
No. An AI agent running on an unstructured base automates errors, not performance. Data and automation aren't optional steps — they're the conditions under which an agent is reliable. We'll tell you honestly what has to come first.
Salesforce and HubSpot primarily, but we adapt to any CRM, including in-house systems.
Claude, by Anthropic, for all our agents. Our consultants are experts in the Claude API, MCP, and agent design, with a direct line to Anthropic's teams.
An NDA is signed before any engagement — your data is never shared, sold, or reused on other accounts. We work directly with your security and data teams, sign a DPA on request, and scope access to the minimum needed for the work.
An audit and a first production pass take a few weeks each. Governance and automation run over a quarter or two, depending on your processes, team size, and data maturity. We scope it precisely on the first call.
Commercial AI starts with clean data.
From a messy CRM to deployed AI agents — Growth Wave walks the whole path: data, automation, agents.
Clean data. A CRM that works for you.
Contact
Tell us where your data stands.
Describe your CRM and where AI fits in. We'll come back within 24 hours with a straight read on where to start.