Is this for you?
- You have thousands of items with inconsistent or missing categories.
- Manual tagging is slow, expensive, or impossible at your scale.
- You need genres, topics, or tags inferred from text descriptions.
- Search, browse, or recommendations are suffering because labels are messy.
- You need confidence scoring and a human-review path for edge cases.
What you get
- Taxonomy audit and label-definition refinement (or creation if missing).
- Gold-set sampling plan and labeling guidelines.
- Classifier approach selection and training/adaptation.
- Evaluation report with precision/recall, thresholds, and failure modes.
- Labeling pipeline (batch + API interface) with confidence scores.
- Documentation and handoff, plus a 60-minute walkthrough.
Scope
What's in
- Taxonomy design or refinement
- Text classification modeling and evaluation
- Batch inference and scoring pipeline
- Review and QA workflow design
What's out
- Long-term MLOps monitoring and retraining
- Full ingestion pipeline rebuilds
- Editorial UI implementation
- Manual labeling at scale beyond a small gold set
Process
Intake
Day 1Kickoff, taxonomy and data access, success criteria alignment.
Discovery
Days 2–5Taxonomy review, data sampling, baseline classification experiments.
Modeling
Days 6–10Model selection, training or adaptation, and evaluation with thresholds.
Delivery
Days 11–15Pipeline packaging, documentation, and team walkthrough.
Pricing
50% to start, 50% on delivery. Includes one 30-minute follow-up call within 30 days of delivery.
One fixed price. No surprises, no “starting at” language. If we agree on scope and you pay the deposit, the engagement is locked in.
Questions
Do we need an existing taxonomy?
No. If you have one, we’ll refine it. If not, we’ll design a practical taxonomy with your stakeholders.
What if we don’t have labeled data?
We’ll build a small gold set and bootstrap from there; you don’t need millions of labels to start.
Can this run in our stack?
Yes — deliverable can be a containerized service, batch job, or script integrated into your pipeline.
Is this LLM-based?
Sometimes. We choose the most reliable and cost-effective approach for your data, which isn’t always an LLM.
About
I’ve delivered taxonomy systems that auto-assign genres and subgenres to tens of thousands of album products, trained on millions of rows of Discogs data. The work is grounded in production-scale classification, not theory.
More about the studioReady to start?
Book an intro call. If we're not a fit, I'll tell you on the call.