Commercial team and AI Mates reviewing live sales data together

Une action commerciale décisive, au rythme des données en temps réel

Vos équipes commerciales et des Mates data spécialisés travaillent sur les données vivantes de l'entreprise, dans les canaux Slack ou Teams où se prennent les décisions. Sans SQL, sans file de tickets.

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Commercial agility depends on answers at the moment of the decision. allmates puts live enterprise data in the hands of the people who sell, with specialised Mates and your rules in the loop.

Commercial team working together on a laptop

One question, one threadQuestions start in the channel where the team works, and the answer stays visible to everyone who needs it.
Specialised MatesData, commercial and finance Mates, each with approved KPIs, the right systems and the model that fits the job.
Governed by your permissionsEvery answer respects who can see what, and every query is logged.

Trusted by enterprises to run their critical processes

Every question becomes a ticket. Insight that arrives weeks late is not insight.

The data is there, in your warehouse and your CRM. Reaching it still takes technical skills, manual queries and a place in the IT queue.

Ticket queue

Specialists on routine pulls

Business leaders wait for data specialists to run queries, while the specialists spend their time on requests instead of architecture.

Static reports

Dashboards out of date

Reports show last month while the market moves today, and the question you need answered is never the one on the dashboard.

Private answers

Insight that never travels

When someone does get an answer, it stays in a private chat or a spreadsheet instead of reaching the team that has to act.

Your commercial team asks in its own channel, Mates answer from live governed data, and everyone acts on the same facts.

One missed target. Understood, decided and acted on in a day.

Follow a regional sales question from Slack to a decision in allmates. Each person stays in their own app, each Mate runs on the model that fits its job, and every number keeps its source.

3 people3 Mates3 AI models5 apps1 shared context

01
Slack · #retail-performance→Snowflake

FrédéricRegional sales director→Data AnalystGPT

@allmates why did the North region miss its weekly target?

The Data Analyst queries live sales in Snowflake: footfall is stable, but conversion dropped in four stores after the promotion ended.

Approved KPIs
02
Slack · #retail-performance

Data AnalystGPT→Commercial IntelligenceClaude

The Commercial Intelligence Mate compares the dip with last year's promotions and recommends extending the offer in those four stores for two weeks.

Promotion history
03
Slack · #retail-performance

FrédéricRegional sales director→SophieCategory manager

@Sophie can we extend the promotion in these four stores?

Sophie joins the thread with the analysis and the recommendation already in front of her. Nobody exports a spreadsheet.

Promotion history
04
Teams · Category team→SAP

SophieCategory manager→Finance ControllerCopilot

@Finance Controller what does a two-week extension do to our margin?

The Finance Controller checks the impact in SAP against the pricing policy. The extension stays within the approved range, and the margin cost is quantified.

Pricing policy
05
allmates

SophieCategory manager→NinaFinance business partner

Nina reviews the numbers and their sources, then approves the extension in allmates. The decision stays on record with everything that led to it.
✓ Approved by Nina in allmates
06
Teams · Store managers→Slack

Commercial IntelligenceClaude→FrédéricRegional sales director

The Commercial Intelligence Mate briefs the four store managers in Teams, then reports next week's results back to #retail-performance.

Approved KPIs

Each Mate has a role, not a blank prompt.

Configured once with your KPIs, systems and rules, then available to every commercial team, wherever it works.

Data & BI

Data Analyst

Answers questions in plain language from your live warehouse and CRM, read-only.

Model

GPT

KnowledgeApproved KPIs and definitions

Tools

Revenue

Commercial Intelligence

Puts results in context against history, promotions and targets, then suggests the next action.

Model

Claude

KnowledgePromotion history, targets

Tools

Finance

Finance Controller

Checks margin and pricing rules before any commercial decision goes out.

Model

Copilot

KnowledgePricing policy, margin rules

Tools

Commercial team reviewing results together on a laptop

Commercial insight at the speed of the decision.

Ask, don’t queue
Self-service in plain languageBusiness users ask complex questions without SQL or a ticket.
Live data
Not last month’s reportAnswers come from live, governed data, within each person’s permissions.
Experts freed
Specialists back on strategyData teams spend less time on routine pulls and more on architecture.
Numbers → action
Context, not just figuresMates compare results with history to recommend what to do next.

Not another model. The layer that makes every model work for the enterprise.

Your commercial team keeps the models it trusts: GPT queries Snowflake, Claude puts the results in context and Copilot checks the margin rules. allmates routes each question to the right model under one set of rules.

Explore Systems→

Control is built in, so adoption can scale.

Every answer respects who can see what: the Data Analyst reads live sales data within each person's permissions, and no customer data leaves without approval.

Sovereign

Your data stays in your perimeter, with the deployment and region you choose.

Governed

Decide who, and which Mate, can see, use and act on what.

Traceable

Every human and AI action is recorded, attributable and reviewable.

Predictable

Budgets and caps per team and per Mate. No surprise bills.

Explore governance→

Already running at Bash.

Fashion retail · Commercial intelligence

Turning store data into decisive action

Store performance data was there, but reaching it took technical skills, manual queries and long turnaround. Commercial teams now ask in plain language, get contextualised answers from live Snowflake data and act locally.

€1M+ / yrSavings potential

No SQLData access for business teams

Real timeInsight at the decision point

Read the Bash story→

Team working together on a first AI process

Start with one commercial decision. Prove it in 8 weeks.

We pick one critical commercial process with your team, measure the value on real work, then extend it across regions and teams.

01Pick the process02Prove it in 8 weeks03Extend it

Discover the Lighthouse approach→

Put live data in the hands of the people who sell.

See one of your own commercial processes run in allmates, in a demo built around your data and tools.

Talk to an expert Map your commercial reporting