SEMICONDUCTOR PRODUCT DATA
EXTRACTED, NORMALISED, ACCESSIBLE AT SCALE
250,000+ DEVICES ACROSS 100+ MANUFACTURERS
Analyse semiconductor market trends, product release cycles, whitespots and more
SOLUTIONS
BUILT FOR EVERY ROLE THAT TOUCHES A PART NUMBER
/01
DISTRIBUTION
Move parts. Win quotes. Satisfy customers.
/02
MANUFACTURERS
From silicon to socket. The full signal.
TOOLS
OUR SCALE IS YOUR EDGE
Structured component data, built for engineers, analysts, and the systems you run
AI READY
TALK TO YOUR PART DATA
Cross-reference 2N7002
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Ground your AI in real electronics data
Language models are built to reason, not to calculate. We supply the values they reason about, taken from the manufacturer's own datasheets.
GROUND THE AI
LLMS ARE STATISTICAL MACHINES
Language models produce the most statistically likely answer, which is not the same as the correct one. A datasheet parameter has one right value, and it exists in a specific document on a specific page. We extract those values the same way every time, normalize them, version them, and keep the link back to the source PDF, so the model reasons over known values instead of approximating them.
RIGHT TOOL FOR THE JOB
Let AI do the reasoning, and tooling handle the scale
Access the data and tooling through the Partlake MCP server. An MCP server is an industry-standard bridge that lets AI models call outside tools and data directly, grounding the model in verifiable facts instead of probabilities. Our lookups are deterministic and resolve in milliseconds, with dedicated tooling for cross-referencing, device comparison and more across the full catalogue of every manufacturer in the world. Your model does not have to approximate anymore.
AI AND MATHS
LLM GENERATION IS NOT CALCULATION
Numbers reach an AI language model as text fragments rather than quantities. An AI tokenizer can split the number 380 into a single token and the number 381 into two tokens, so the relationship between digits is gone before the model ever sees them, and comparing two values becomes pattern-matching rather than arithmetic.
Everything it reasons about, such as the parametric rows, must also be placed into its context, and that context window sets a further ceiling: doubling the data quadruples the work, and accuracy falls for anything sitting in the middle of a long context. Give a model results that are already computed and compared and it reasons over them well. Ask it to do the calculations and it approximates a confident-sounding answer.
EVALUATE STRATEGIC FIT FOR YOUR ORGANISATION
Contact us to get a tailored overview of our data and its capabilities, API and AI integration possibilities, and implementation approach.