AI reshapes how you work before it reshapes your tools.
Your stock forecasts are worthless if nobody uses them to place orders. We build models wired into your business tools, with the teams who use them daily — not a notebook only the data scientist understands.
Real-time tracking reliability scoring
Building a proprietary tool with a multi-agent workflow to automate QA of your marketing data on your website.
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deployment
minutes
de recette
Two families, one requirement: move the financial result
What can a multi-agent workflow actually do?
A useful agent has a bounded scope, clean data and continuous evaluation. We start with one measurable use before widening the field.
Which customers deserve your attention?
Value, churn and propensity scores are pushed to wherever they trigger an action: preferred communication channel, CRM, sales prioritization.
How do you anticipate demand?
Every week the model recalculates how much you'll sell, by product and by channel, and checks it against reality. The result: fewer stockouts, less cash tied up in excess inventory, and an automatic alert as soon as the forecast drifts.
The method, in four stages
Use-case scoping
Expected value, available data, success criteria.
An evaluated prototype
A measured prototype in four weeks, not a demo.
Industrialization
Production release, monitoring, and wiring into the business.
Monitoring
Retraining, drift, and performance review.
We operate your stack, we don't resell it
What the teams we work with say
“Beyond their technical expertise, they have a real ability to understand the business stakes behind the numbers and to propose concrete solutions. The whole team is reliable, committed and a pleasure to work with. Smart Bees is a genuine asset for any company that wants to make better use of its data and steer its performance.”
“We've been working with Smart Bees for several months. They've become an extension of our own team across everything related to data collection and data more broadly. Thanks to their support, we now have a much better grasp of our own data, which makes our digital team measurably more effective.”
Frequently asked questions
Do we need an in-house data science team?
Not to get started. We deliver and monitor; the internal handover is prepared during the engagement.
Are your models explainable?
Yes: contributing factors documented and readable by the business teams.
What does an LLM agent cost?
It depends on usage volume. We cap it and track cost per request from the prototype onward.
And if the use case doesn't hold up?
We stop it at the prototype stage and say so. That's cheaper than industrializing something pointless.
Thirty minutes, one founder, a straight answer.
We look at your setup on a screen share and tell you whether there's a real engagement here or not.
