Intelligent Document Q&A, Production-Ready
Trailhead retrieves from multiple views - chunks, entities, and tasks - to deliver precise, citation-backed answers from your knowledge base.
POST /chat
{
"query": "What is our refund policy?"
}
// Response with citations
{
"answer": "Refunds are available within 30 days [1]...",
"citations": [{"source": "policies.pdf", ...]
}Why Trailhead Wins
Don't settle for fragile prototypes. Built for production from day one.
Built for Intelligent Retrieval
Everything you need to build production-grade RAG applications that actually work.
Multi-View Architecture
Retrieve from chunk views, entity views, and task views simultaneously. Different queries find different types of information.
Semantic Search
Vector similarity finds conceptually related content, not just keyword matches. 'Password reset' finds 'credential recovery'.
Inline Citations
Every answer traces to specific document sections. Follow the chain: citation to chunk to document to source file.
Intelligent Chunking
Section-aware parsing respects headers, code blocks, and lists. No arbitrary splits mid-sentence or mid-thought.
Multi-Tenant Ready
Namespace isolation built into the data model. Serve multiple customers from one deployment with complete data separation.
Knowledge Graph
Automatic relationship tracking between chunks: sequential, hierarchical, and cross-references. Explore connections visually.
Multiple LLM Support
Choose your LLM provider. We support OpenAI, Anthropic Claude, and others - switch without changing your integration.
Production-Ready API
Clean REST endpoints, comprehensive error handling, and detailed documentation. Ready for your application.
Navigate Knowledge, Not Just Search Results
Trailhead automatically builds a knowledge graph as you ingest documents. Explore how content connects, discover related sections, and understand your knowledge base visually.
Multi-View Retrieval
- chunk_view Direct text chunks for precise, literal retrieval
- entity_view Extracted entities: people, organizations, products, concepts
- task_view Task summaries: FAQs, how-tos, policies, troubleshooting
Automatic Relationships
- next / previous Navigate sequential content flow
- parent / child Explore document hierarchy
- reference Discover cross-document connections
Your Documents Become a Knowledge Base
Trailhead automatically extracts and manages named entities from your documents. Build a structured knowledge graph without manual tagging.
Automatic Extraction
LLM-powered entity recognition extracts people, organizations, products, and concepts from your documents automatically.
Smart Deduplication
Merge duplicate entities across documents. 'Acme Corp', 'Acme', and 'ACME Inc' become one canonical record.
External System Linking
Connect entities to your CRM, HRIS, or product database. Link 'John Smith' to his Salesforce contact.
Role-Based Context
Track how entities appear: as vendor, partner, customer, author, or competitor. Understand relationships at a glance.
Built for Real-World Problems
See how teams use Trailhead to unlock their document knowledge.
Legal & Contracts
Search across thousands of contracts, NDAs, and legal documents. Find specific clauses, compare terms, and get instant answers about obligations and deadlines.
"What are the termination clauses in our vendor contracts?" - Contract clause search
- Obligation tracking
- Risk identification
Support Knowledge Base
Power your support team with instant access to product docs, troubleshooting guides, and past ticket resolutions. Reduce response time and improve accuracy.
"How do I resolve the API rate limit error?" - FAQ retrieval
- Troubleshooting guides
- Product documentation
Compliance & Policy
Navigate complex regulatory requirements across SOC2, HIPAA, GDPR, and internal policies. Every answer traced to the source for audit trails.
"What are our data retention requirements for EU customers?" - Policy lookup
- Audit trail citations
- Regulatory cross-reference
Research & Analysis
Explore research papers, market reports, and competitive intelligence. Extract entities, discover connections, and synthesize insights across documents.
"What companies are mentioned as competitors in our market research?" - Entity extraction
- Cross-document analysis
- Knowledge graph exploration
From Documents to Answers
Simple integration. Powerful results.
Ingest
POST /ingest Push your documents—text, markdown, or PDF. Trailhead processes and indexes them across multiple semantic views.
Search
POST /search Query your knowledge base with natural language. Retrieve relevant chunks, entities, and context with vector similarity.
Chat
POST /chat Ask questions and get answers with automatic retrieval. Every response includes citations to source documents.
Why Teams Choose Trailhead
Focus on your product, not infrastructure. We handle the hard parts.
Choose Your LLM
Switch between OpenAI, Anthropic Claude, or other providers. Your integration stays the same - we handle the complexity.
Predictable Pricing
No surprise vector database bills. Our efficient architecture keeps costs low as you scale your document library.
Enterprise Security
Your documents stay secure with namespace isolation, audit trails, and SOC2-ready infrastructure.
Scales With You
From hundreds to millions of documents. No re-architecture needed as your knowledge base grows.
99.9% Uptime SLA
Production-grade infrastructure with redundancy, monitoring, and automatic failover.
Dedicated Support
Direct access to our engineering team. Need a custom LLM integration? We'll build it for you.
Frequently Asked Questions
Everything you need to know about building with Trailhead.
What makes Trailhead different from other RAG solutions?
Trailhead uses a multi-view architecture that goes beyond simple chunk retrieval. Each document generates three types of searchable views: chunk views for direct text, entity views for extracted people/organizations/concepts, and task views for procedural knowledge. This means your queries match the right type of information, not just keywords.
How does the citation system work?
Every answer from Trailhead includes numbered citations that trace back to specific source documents and sections. You can follow the citation chain from the answer to the exact chunk, to the document section, to the original file. This provides full auditability and lets users verify any claim.
What document formats are supported?
Trailhead currently supports PDF, plain text, and Markdown files. Documents are intelligently chunked based on their structure - respecting headers, sections, and paragraphs rather than using arbitrary token limits. This preserves document semantics for better retrieval.
Which LLM providers do you support?
We currently support OpenAI (GPT-4, GPT-4o) and Anthropic Claude. Need a different provider? Let us know - we can add support for additional LLMs based on customer requirements. Your API integration stays the same regardless of which provider you choose.
How does multi-tenancy work?
Every document and entity in Trailhead is tagged with a namespace. All searches and operations are automatically scoped to the specified namespace, providing complete data isolation between tenants. This is built into the data model, not bolted on as application logic.
What's the knowledge graph used for?
The knowledge graph tracks relationships between document chunks (sequential, hierarchical, cross-references) and extracted entities. You can explore how concepts connect across your document corpus, discover related content, and understand document structure visually.
Do I need a separate vector database?
No. Trailhead uses PostgreSQL with the pgvector extension for vector storage and similarity search. This means your vectors, metadata, and relational data all live in one database - simpler operations, lower cost, and ACID-compliant transactions.
How is entity deduplication handled?
Trailhead's entity management includes merge functionality. When the same entity appears with different names across documents (e.g., 'Acme Corp', 'Acme', 'ACME Inc'), you can merge them into a single canonical record. All mentions are preserved and linked to the merged entity.
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