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 study- products processed
- 4.71M+
- additional product attributes
- 9
- The client owns the pipeline
- We handle setup; the client retains the code, config, and infrastructure.
Bring 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.