Equipped AI.

02 · THE BRIEFING

1. I
   When to consider it
   Your team spends an hour a day qualifying incoming contacts, summarising calls, or classifying support tickets. A well-briefed AI does that work in a few seconds. That is often the moment, especially when request volume climbs.
2. II
   Why it matters
   Without equipped AI, you pay humans for tasks the machine does better and faster: triaging 200 incoming emails, summarising 30 Intercom conversations, scoring (rating quality) 50 contacts per day. The opportunity cost climbs with your volume. And humans wear out on repetitive work instead of focusing on judgement.
3. III
   What you get back
   Qualified time given back to your team for tasks that need judgement. Consistency in contact and ticket processing. An AI layer that leans on your real data via a custom MCP Claude bridge (connecting Claude to your private data), not on a generic model trained on the internet.
4. IV
   How we run it
   We start by looking at what eats your team time, before writing a single AI instruction (prompt). The target use case is defined in writing. We code MCP custom Claude connectors (the bridge connecting Claude to your private data) to GA4 (your analytics), HubSpot (your CRM) and your product base. The Claude API (Anthropic's model) on inference (when the AI replies). Mandatory human review before any external send to the client. Notion doc handed over to your team.
5. V
   What it unlocks
   A layer of intelligence on your catalogue and customer interactions. To combine with a server-side tracking layer so the AI works on clean data.

We get back to you within the week · scoping before any quote.

04 · WHAT WE WON'T WRITE IN AN RFP

Equipped AI is a supervised productivity tool. Not a replacement for your teams.

We code for your team, not against it. The rule we hold: the AI proposes, the human validates before any external send. A sales email never leaves to a client without a proofread. A contact score feeds the decision, it does not replace it. The value is in the data fed through custom MCP (the bridge connecting Claude to your private data), not in model sophistication.

• 01

  Qualified time given back

  High-volume repetitive tasks (qualifying incoming contacts, summarising calls, classifying tickets) leave the human calendar. Your team focuses on judgement, nuance and client relationship.

• 02

  Processing consistency

  All contacts scored on the same grid, all tickets classified on the same typology. No more Monday vs Friday variation, no more tired human judgement at end of day.

• 03

  AI plugged into your data

  Through MCP connectors, the AI works on your catalogue, your CRM, your conversation history. Not on a generic model trained on the internet. Reply relevance follows data quality.

• 04

  Systematic human supervision

  No external send without human validation. All the AI's outputs go through a review interface before leaving to the client. Hallucinations stay team-side, not in the inbox.

05 · THE PLAY-BY-PLAY

Four steps. Six weeks on average. No tool handed over without training.

1. 01

   We look at what eats your team time.

   Audit of repetitive tasks with heavy cognitive load: qualifying incoming contacts, summarising calls, triaging tickets, personalising emails. We note frequency, human time and error risk. Tools: direct observation, Notion for mapping, 2-week sampling.

2. 02

   We pick the use case that really pays back.

   Target list of 1 to 3 use cases by ROI order. Scope frozen in writing, client sign-off. If a task requires too much contextual judgement, we say no, we do not code it. No silent additions outside the quote, no general AI promise.

3. 03

   We wire the AI through MCP.

   Development of MCP custom Claude connectors (the bridge connecting Claude to your private data) to your source tools: HubSpot (opens in a new window), GA4 (opens in a new window), BigQuery (opens in a new window) or your product base. Claude API (opens in a new window) on inference with versioned instructions (prompts). Mandatory human validation interface before any external send. Logging of every inference.

4. 04

   We train and we document.

   Gain measurement: human time saved, AI suggestion acceptance rate, residual error rate. 4-hour training for your team on usage and proofreading. Detailed Notion doc per use case. Prompt versioning for internal takeover.

07 · NOT YET FOR YOU IF

Three cases where equipped AI is not the top priority.

• Your data is scattered and inconsistent.

  An AI plugged through MCP onto chaos gives chaotic replies. If your CRM contains duplicates and your tickets have no typology, we lay down cleanliness before the AI. Otherwise the model hallucinates on your data and team trust collapses fast.

• Your monthly request volume is under 100.

  Below this threshold, the entry cost of an equipped AI tool does not pay back. At these volumes, humans remain faster to deploy and more precise on context. The cost-benefit ratio becomes debatable. We would rather say it than walk you into the dark.

• You want an AI that decides for you.

  That is not what we code. The rule we hold: systematic human review before any external send. If you are looking for an AI that replies to clients on its own without validation, we are not the right provider. Supervision matters as much as the model.

We look at what eats your team time. We tell you what we equip, and what stays human.