Every part from every manufacturer made directly comparable through the same fields, same units, same structure and comparable conditions.
Whether you are running cross-reference for a sales team or doing parametric analysis for strategic product development, the data and access is the same.
Write your own cross reference rules. Build on the Partlake API, connect with Excel or
point your whole team at the web interface for an out of the box experience. Your call.
We download and track every datasheet version. Extract parametric values, figures, and metadata. Normalise all of it. Present it in a unifrom way alongside 500,000+ products and growing.
The normalisation is where the real work lives. We go from unstructured to structured data through a
stack of heuristics, hard-won industry knowledge and in-house built AI models.
LLMs do not touch the data on the way in. Our pipeline is deterministic and every single value and data point traces back to a
specific datasheet, page, and figure or table, exactly as written. Every improvement we make to the pipeline is versioned for auditing.
Anything built on top, by us or by you, stands on data you can trust.
The semiconductor market does not stand still.
New products, datasheet revisions, lifecycle changes, new manufacturers.
Doing this well takes years of industry knowledge. Our team brings backgrounds
from semiconductor manufacturing engineering, marketing and analytics.
Our ingestion pipeline runs continuously, picking up changes as they happen.
And nothing gets discarded. Every datasheet version, every parametric change,
every lifecycle transition is preserved, so you can query the market today,
or the market as it was yesterday.
We are not a search engine, we are a market neutral data provider.
We take our market neutrality very seriously and as an indepenent entity, we will never purposfully bias any data or results for any reason.
As part of our neutrality we will never purposfully share our customers usage data without a written approval.
We preserve the datasheet content as written to the best of our ability.
Our internal policy is we would rather show no data than data at risk of being incorrect.
Where we have had to make an assumption, we will clearly label it in the data itself.
Furthermore, all assumptions are documented and available in our documentation area.