AI systems for manufacturers who ship physical products at scale.
From print-file QA to vendor and compliance automation, we build AI systems that plug into how manufacturing and consumer products teams actually work — not a generic dashboard.
Manufacturing and consumer products teams run on a mix of production systems, retail-partner portals, and physical QA steps that don't fit a generic SaaS workflow. The AI systems worth building here aren't chatbots bolted onto a support page — they're classifiers and decisioning layers embedded directly in the print, packing, or vendor-sync pipeline that's already running.
That's the pattern across our manufacturing engagements: replace a brittle rules-based check or a stack of fragile point-to-point automations with a single model-driven step that a real production system calls directly. For Stupell Industries, that meant a Claude vision classifier catching a printing defect a computer-vision heuristic kept missing. For Alderbrook Home Goods, it meant collapsing 24+ separate Zapier automations into one AWS Lambda pipeline with an LLM handling classification and exception routing across five retail partners.
In both cases the win wasn't a new dashboard — it was fewer people doing manual review, fewer silent failures, and a system that gets more reliable as edge cases get folded back into the prompt or pipeline logic instead of staying tribal knowledge.
Where we focus
Tailored to your systems and data, but these are the core building blocks.
Computer vision quality control
Vision classifiers that catch defects a rules-based check misses, before print or shipment.
Order and vendor processing
Automating the sync between vendor portals, catalogs, and internal systems.
ERP and API integrations
Connecting production, inventory, and retail-partner systems into one working pipeline.
Document and compliance processing
LLM-driven classification and exception routing for compliance and catalog data.
Real numbers from named engagements.
Every figure links to the case study or source behind it.
Common questions about this work.
Straight answers, grounded in what we've actually shipped.
What does AI for manufacturing actually look like in production?
In our engagements, it's rarely a standalone AI product — it's a classifier or decisioning step embedded in a pipeline that already runs: a vision model gating a file before print, or an LLM classifying and routing exceptions inside an existing order or compliance sync. See our Stupell Industries and Alderbrook Home Goods case studies for two shipped examples.
Can computer vision QA replace a manual print or packaging inspection step?
Yes, for well-defined defect types. We replaced a brittle OpenCV heuristic with a Claude vision classifier for Stupell Industries, tuned with a taxonomy-driven prompt and calibrated for high recall — so ambiguous cases get flagged for human review instead of silently passing. It runs as a minimal API call from their existing production pipeline, not a separate tool people have to check.
How do you automate vendor compliance and order sync across multiple retail partners?
By replacing the point-to-point automations (often Zapier or Apps Script) with one pipeline that normalizes each partner's data shape and puts an LLM in the decisioning layer to classify records and route genuinely ambiguous ones to a person. For Alderbrook Home Goods this replaced 24+ fragile automations across Wayfair, Walmart, Target, Faire, and CommerceHub with a single AWS Lambda pipeline syncing to Airtable and BigQuery.
Does this require replacing our existing ERP or vendor portals?
No. Both of our manufacturing engagements connected to systems already in place — Amazon's SP-API, existing production pipelines, Airtable, BigQuery — rather than asking the client to migrate to a new platform. The AI layer sits in the decisioning step, not as a system of record.
Is this a good fit for a smaller consumer products company, not just enterprise manufacturers?
Both of our named manufacturing engagements were mid-size operators, not enterprise manufacturers — the pattern scales down well because the cost driver is usually a handful of fragile manual steps or automations, not overall company size.
See the full story behind these numbers.
Have a workflow worth automating?
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