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Inference Wire

What happened in AI today, explained in 5 minutes.

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Manufacturing·AI Consulting & Strategy·4 weeks

Bloomlabel

Shifting a 47 Brand Operation From Paperwork to Production

Bloomlabel produces for 47 beauty and skincare brands simultaneously, and their batch documentation and production scheduling had outgrown the manual tracking that scaled them there, costing over 130 staff hours a week. They brought in our AI consulting team to audit their operations, pick the right tools, and get the ops team comfortable running them.

43 minutes

Batch Documentation Time

11 weeks

Time to Full ROI

4 hours

Quarterly Audit Prep

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A professional reviewing data dashboards and printed charts, illustrating the kind of operational metrics tracked during Hexaa's AI implementation.
“Their team showed us exactly what to use and where. The engagement paid for itself in eleven weeks.”
AG

Ana Garcia

Bloomlabel, Bloomlabel

Before

6 hour manual batch documentation, 3 day quarterly audit prep, 130 weekly hours on tracking

After

43 minute AI assisted documentation, 4 hour audit review, 31 weekly hours on tracking

Narrative

The full story.

The Challenge

Every production batch needed 12 to 18 compliance documents, and the team was pulling each one together manually from templates, lab results, and supplier certificates spread across five shared drives. Audit prep took 3 full days of senior staff time each quarter, most of it spent tracking single ingredients back through the supply chain.

What We Built

Our team reviewed Bloomlabel's documentation and scheduling workflows end to end, from how raw materials were logged on intake through how final batch packets were assembled and stored. We selected and deployed an AI document assembly tool that pulled structured data from their existing systems, paired it with a scheduling assistant connected to live inventory, and ran four workshops showing the ops team exactly how to use both. Nine days after everything was live the team was producing complete batch packets in under 45 minutes.

Results

Batch documentation dropped from 6 hours to 43 minutes per run, though the first week averaged closer to an hour as the team got used to it. Weekly hours on manual tracking went from 130 to 31 across the ops group. Quarterly audit prep that used to take 3 days now wraps in about 4 hours. The $85K engagement covered its own cost in 11 weeks between time savings and the capacity to bring on 6 new brand clients that quarter.

Technical proof

How it was built.

Implementation details and the stack behind the delivery.

Implementation highlights

Key engineering decisions from this project.

Batch documentation time reduced from 6 hours to 43 minutes per run
6 new brand clients onboarded the following quarter without adding staff
Full adoption confirmed by week 4 from kickoff
4 workshops delivered across the full 12 person ops team

Stack snapshot

Tools and platforms used in the delivery.

ChatGPT Enterprise AirtableMake Google Looker Studio Notion AI

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