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.
Datasheets → structured knowledge graphs. Queryable by AI. Built for hardware intelligence.
Built for teams turning unstructured specs into structured, agent-ready context.
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 usLive demo
One real datasheet. Three kinds of queryable information — parameter table row, performance curve, footnote condition.
TA = 25°C, VIN = 5.0 V unless otherwise noted
| Sym | Parameter | Min | Typ | Max | Unit | |
|---|---|---|---|---|---|---|
| VIN | Input voltage range | 2.7 | — | 5.5 | V | |
| VOUT | Output voltage | 1.2 | — | VIN−0.2 | V | ① |
| IOUT | Load current | 0 | — | 800 | mA | |
| PSRR | Power-supply rejection ¹ | — | 70 | — | dB | |
| Noise | Output noise density | — | 50 | — | nV/√Hz |
③ 1 Measured at f = 1 kHz, IOUT = 100 mA, COUT = 10 μF ceramic. See eval kit schematic for test board layout.
GET /graph/sx2048l/node/vout
{
"id": "vout",
"min": 1.2, "unit": "V",
"edges": ["iout","theta","psrr"]
}
GET /graph/sx2048l/curve/eff?load=100mA
{
"typ": 94, "unit": "%",
"at": { "vin": 3.3, "iout": "100mA" }
}
GET /graph/sx2048l/conditions/1
{
"freq": "1kHz",
"iout": "100mA",
"cout": "10μF ceramic"
}
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.