Solutions

Automation Platforms for
Every Documentation Challenge

From semiconductor reference manuals to enterprise knowledge bases — we build AI-powered pipelines that eliminate manual bottlenecks and guarantee accuracy.

Solution 01

AI Document Generation

Automatically generate complete documentation chapters from architecture specifications, PDFs, and design databases. Our LLM-powered extraction pipeline structures raw technical information into publication-ready content — with human-in-the-loop review for guaranteed accuracy.

Claude APIGPT-4DITA XMLRAG
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📄 Architecture Spec PDF
↓
🤖 Claude API Extraction
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📝 Structured DITA Chapters
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👤 Human Review & Approve
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✅ Published Documentation
⚡
95% Automation
AI handles extraction and structuring. Humans review only the output.
🎯
100% Source Accuracy
All register names and addresses extracted directly from design databases.
🌍
Multi-Language Ready
Same pipeline generates Chinese, Japanese, and English documentation.
🔬 RTL Elaboration (RTL Design Elaboration)
↓
📊 Parameter Extraction JSON
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🗺️ Memory Map → IMAP Generator
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📐 DITA-OT + SVG Rendering
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✅ PDF + WebHelp Output
Solution 02

Design-to-Document Pipeline

Connect RTL design directly to documentation output. Our pipeline elaborates SystemVerilog/VHDL designs using RTL Design Elaboration or open-source Open-Source Elaboration, extracts every parameter with full type and hierarchy resolution, and flows them automatically into your documentation — zero manual transcription, zero errors.

RTL Design ElaborationOpen-Source ElaborationSystemVerilogVHDLDITA-OT
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🛡️
Zero Transcription
Parameters flow directly from RTL to documentation without human typing.
🔍
Full Hierarchy
Resolves defparam, generate blocks, and module instantiation overrides.
✅
RTL-to-Doc Validation
Cross-reference check catches mismatches before publication.
Solution 03

RAG Knowledge Bases & AI Chatbots

Turn your documentation into an interactive AI assistant. Our RAG pipeline chunks resolved documentation, embeds it into a vector knowledge base, and provides a natural-language chatbot that answers technical questions with precise source citations — reducing internal support queries by 40%.

RAGVector EmbeddingsFlaskClaudeSemantic Search
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💬 User: "What's the reset value of MCR?"
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🔢 Embed Query → Vector Search
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📚 Top-5 Relevant Chunks Retrieved
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🤖 Claude Generates Grounded Answer
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✅ Answer with Source Citations
📊
1
Detect Changes
parameter definitions diff on commit
2
Gap Analysis
diff parameter definitions vs DB
3
Contribution YAML
auto-assigned slots
4
Schema Validate
type·range·enum
5
PR + Merge
reviewed & merged
Solution 04

Parameter Management Platform

Centralize all documentation-specific configuration in a Git-versioned JSON database with CI-integrated gap analysis. When parameters change in your design, the platform automatically detects gaps, generates contribution YAML assignments, validates against the schema, and guides the change through review and merge.

JSON DBGit VersionedCI/CDYAMLSchema Validation
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Custom Solutions
Need Something Different?
Every layer is modular and swappable. Register format, elaboration engine, AI model, publishing engine, UI — customize any component to fit your existing toolchain.
Talk to Our Team →