Make messy product data usable at scale
Flexible extraction pipelines for large product datasets, starting with a small, measurable proof for your use case.
Experience processing product data from
Source experience, not partnerships or endorsements
Problems we love solving
- LLM text + image extraction
- Data reconciliation & standardization
- Metadata from weakly-typed DBs
- Agent development & integration
- Classical ML
- Vision & small language model fine-tuning
Foundation-model pretraining or GPU-cluster engineering? Not our strong suit; we'd point you to specialists.
Transforming messy data into a useful abstraction at scale
Different pack sizes. Inconsistent units. Missing fields. Comparing consumer goods items' EQV across data providers takes a pipeline that can reconcile them.
Phinest built a pipeline that reads product text and images, extracts packaging and unit information, and checks the results for use in EQV comparisons.
Read the case studyBring us your hardest data problems
Pick one data problem or agent task. Test it on a representative sample before committing.
Your first 14 days are our most crucial. We'll scope a two-week-sized sample of the problem you're facing and prove we can solve it, so you don't have to sign up for six months up front.
What you get
- 01
The Solution
A working pipeline, agent, or model tested on your sample.
- 02
Verifiable Results
Output quality checked against agreed examples, with failures and gaps called out.
- 03
A build-or-stop decision
Our recommended next steps based on our findings, and an informed blueprint of what your full-scale solution would require.
Data science, engineering, and product experience
Lean team based in Prague
Applied AI
Enterprise data science and deep-learning product development at H2O.ai. Teaching model fine-tuning at Czech Technical University.
Data science & agents
Data science in banking and at Avast (now Gen), followed by leading agentic AI integration in a US Fortune 500 corporation.
Engineering & product
Systems handling millions of requests a day. Leading engineering teams at US startups and serving as product lead at a 200+ person consulting firm.
Got a data or agentic problem worth solving?
Bring it to a 30-minute call. If we're not the right team, we'll tell you who is.