Parse
Format-specific parsers read technical sheets like an engineer would—tables, footnotes, and multi-column layouts—with OCR when scans are all you have.
Sylica builds model-ready inputs from technical datasheets. Heuristic algorithms and vision models compile scattered parameters by relevant grouping factors—package variant, test condition, and more. Format-specific parsers use OCR to extract key values, normalized into clean JSONs. We organize JSON in a weighted graph that encodes how components interact—queryable via REST API and MCP. When an agent designs with Sylica, it gets the parameters and context it needs at a fraction of the token cost. Sylica is the layer we need for hardware general intelligence.
Built for teams turning unstructured specs into structured, agent-ready context.
Structured for scale
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Parameters normalized into graph-ready payloads (illustrative)
Product
A pipeline built for messy real-world datasheets: layout-aware extraction, semantic grouping, and a graph that preserves relationships—not just flat fields.
Format-specific parsers read technical sheets like an engineer would—tables, footnotes, and multi-column layouts—with OCR when scans are all you have.
Heuristics and vision models cluster parameters by package variant, test condition, temperature grade, and other grouping factors so nothing lands in the wrong bucket.
Extracted values are cleaned and typed into consistent JSON—units, enums, and ranges aligned so downstream models see one schema, not a pile of vendor quirks.
JSON nodes and weighted edges capture interaction context. Query the graph over REST or MCP and ship compact payloads to agents instead of whole documents.
Use cases
From component selection to system-level design, Sylica keeps parameters attached to the conditions they were measured under.
Copilots and autonomous agents pull only the subgraph they need—pinouts, limits, and derating curves—without burning tokens on full PDFs or hallucinating table cells.
Talk to usTrace parameters back to package and test conditions for audits and second-source analysis. The graph makes “which spec applies here?” a query, not a spreadsheet hunt.
Talk to usExport consistent parameter sets for models and simulators, with relationships preserved so corner cases and dependencies do not get dropped at copy-paste time.
Talk to usEmbed Sylica behind your own portals and PLM hooks. REST and MCP interfaces fit CI, review workflows, and custom UIs your team already runs.
Talk to usHow it works
Multi-stage processing combines OCR, layout understanding, and graph assembly so outputs stay faithful to the source document.
Vision passes segment the page—regions, tables, figures—and OCR recovers text where vendors shipped scans instead of digital PDFs.
Models map labels to values in context, resolve footnotes, and assign parameters to the right variant or operating point.
Normalized JSON becomes nodes; interactions and dependencies become weighted edges. Your stack queries subgraphs via REST or MCP.
Platform
Coverage for the long tail of vendor formats—without maintaining a zoo of one-off scrapers.
Complex tables, merged cells, and small-type footnotes—handled with layout-aware models.
PDFs, scanned images, and mixed digital exports through one ingestion path.
Subgraph queries return token-efficient context tuned for agents and RAG backends.
Edges encode strength and dependency so ranking and retrieval stay physically meaningful.
First-class interfaces for services and for tool-calling agents.
Normalize divergent vendor tables into a stable JSON surface for your applications.
Enterprise
Security, scale, and support for teams that cannot afford silent extraction errors.
FAQ
Sylica is purpose-built for technical datasheets: grouping by package and test condition, graph edges for component interaction, and APIs that return subgraphs—not unstructured blobs of text.
OCR is part of the pipeline for scanned PDFs and images. Quality and accuracy depend on resolution and legibility; we can evaluate your corpus in a pilot.
Agents can query the datasheet graph as a tool: retrieve parameters, related components, and context windows sized for the task—reducing tokens versus dumping whole documents into the prompt.
Reach out via the contact section below. We will align on volume, formats, and deployment model, then provision keys and integration support.
Request a walkthrough of the graph model, API shapes, and MCP tools.