Revenue AIYour challenges / Challenge 06

Your teams and your AI agents,
finally in the same system.

“Everyone uses ChatGPT, nobody sells better.”

An AI agent does not know what a qualified opportunity is. Someone has to teach it. We pick the use cases that matter for your sales, we make reliable the data and processes they run on, and we measure what they bring in.

Assess my AI maturityWhat we build

Where we work on your cycle

  • Demand generation, Cost per MQL
  • Discovery, MQL to SQL
  • Negotiation, Win rate
  • Closing, Signed ACV
  • Onboarding, Time to value
  • Adoption, Adoption
  • Expansion, NRR
At every stage, AI captures information and returns it at the right time. As long as the stage is well defined.
Sounds familiar

Plenty of trials. Little effect on sales.

  • Every rep has their own AI tools, and nobody knows which ones.
  • You paid for licences that half the team no longer opens.
  • The AI pilot looked great in the demo, then stopped.
  • Your customer data goes into tools nobody has approved.
  • Nobody can say how much AI brought in this quarter.

Who it is for

The teams and roles who live with this problem every day, and what changes for them.

  • LeadershipCEO, Executive teamAn AI roadmap that delivers measurable results, not proofs of concept.
  • SalesCRO, Sales DirectorTeams that prepare, follow up and sell faster.
  • OpsRevOps, OpsAI use cases plugged into reliable, governed and maintained data.
  • Field teamsReps and CSMsFewer admin tasks, more time with customers.
Our thesis

Your AI agents will never be better than the data and processes you give them.

Many AI projects never get past the proof of concept. The models are fine: they work on data scattered across dozens of tools, definitions that change from one team to the next, and processes nobody ever wrote down.

To take AI past the proof of concept, we structure the context first: reliable data, documented processes, accessible knowledge.

  1. 01 Scattered

    Dozens of tools and files, nothing connected. The AI sees nothing.

  2. 02 Clean up

    We remove duplicates, side files and whatever is no longer used.

  3. 03 Structure

    Every tool finds its place: foundation, context, activation.

  4. 04 Activate

    Your AI finally reaches a reliable context, and produces useful results.

In production

A day in sales, with its agents.

No proofs of concept: agents plugged into the CRM, working from morning to evening where your teams already are.

# sales-lea

Revenue agent

Morning Léa. Three accounts to call today, in this order:

  1. Northwind Raised funds, hiring 3 SDRsScore: 94 out of 100
  2. Brightlane New VP SalesScore: 86 out of 100
  3. Kestrel Visited the pricing page yesterdayScore: 68 out of 100

Contacts enriched and added to the sequence

Six use cases, built on verified foundations.

Each one is delivered in a Revenue AI Sprint.

  • Target

    The right accounts, enriched and prioritised

    Identifying the accounts that match your target, automatic enrichment, then prioritisation by buying signals. Your reps know who to call, and why.

  • Capture

    Call notes fill in the CRM

    Qualification, objections and next steps attached to the right deal, with no data entry.

  • Prepare

    Meeting prep in one minute

    Account history, latest exchanges, likely stakes, questions to ask.

  • Steer

    An assisted pipeline review

    At-risk deals flagged before the meeting, with the reason for the risk.

  • Forecast

    The forecast in plain language

    Leaders ask their question in plain English and get the number, with its detail.

  • Guide

    The playbook in the rep's tool

    The right resource at the right point in the cycle, with no digging through a drive.

Our point of view

AI speeds up a system that works. Anywhere else, it amplifies the mess.

  • An assistant plugged into a poorly filled CRM gives wrong answers with confidence.
  • That is why every sprint starts by checking the foundations: data, sales processes, tools.
  • When they hold, we deploy. When they are missing, we tell you, and we start there.

The Revenue AI Sprint

Three weeks to move from trial to decision.

  1. Check the foundations

    Data, sales processes, tools: what is ready for AI and what is not.

  2. Prove it on a real case

    A first use case plugged into your tools, used by a real team, measured.

  3. Decide what comes next

    A roadmap costed in impact. If the foundations are not ready, we start there.

The tools we deploy most

  • HubSpot
  • Salesforce
  • Claude
  • Notion
  • Modjo
  • Claap

Data in your CRM, intelligence with Claude, knowledge in Notion. Also deployable on the agent platform you already have.

Your contacts

The Ocobo experts on this challenge.

No salesperson followed by a junior: the people you meet at the first meeting are the ones who build with you. Hover over or tap a card to find out more.

Talk to one of them

  • Benjamin Boileux

    Partner

    Revenue AI expert

  • Damien Robert

    RevOps Manager

    Revenue AI expert

Is your Revenue organisation ready for AI?

In 30 minutes, a revenue architect assesses your teams' AI maturity with you. You leave knowing where to start.

Assess my AI maturity

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