Our Solutions
Advanced Context Management and Fine-Tuning Techniques
TeraContext.AI employs a sophisticated toolkit of techniques to make large language models work effectively with documents that exceed standard context windows. We don’t rely on a single approach—we combine methods strategically based on your specific requirements.
Core Technologies
Retrieval-Augmented Generation (RAG)
What It Does RAG breaks documents into manageable chunks, creates semantic embeddings, and retrieves only the most relevant sections when answering queries.
When We Use It
- Documents with distinct, separable sections
- Query patterns that target specific information
- Need for citation and source tracking
- Compliance and audit requirements
Our Implementation
- Advanced chunking (semantic + structural) to avoid splitting key contexts
- Multi-modal embeddings for text, tables, diagrams, and even legacy PDFs
- Hybrid search: Semantic similarity + keyword boosts for precision
- Full citation trails linking responses to exact pages/sources for audits
GraphRAG
What It Does GraphRAG constructs knowledge graphs from your documents, capturing entities, relationships, and hierarchies—enabling reasoning across interconnected information.
When We Use It
- Documents with complex cross-references
- Need to understand relationships between entities
- Multi-document analysis and synthesis
- Questions requiring inference across document sections
Our Implementation
- LLM-powered entity/relationship extraction with 95%+ accuracy
- Custom ontologies tailored to your domain (e.g., legal clauses, engineering interfaces)
- Efficient graph traversal to fit LLM windows while capturing connections
- Seamless RAG integration for retrieval + reasoning
Multi-Layer Summarization (RAPTOR & Beyond)
What It Does Hierarchical summarization builds multiple abstraction layers—from detailed content to high-level overviews—allowing navigation from general to specific.
When We Use It
- Documents requiring both overview and detail
- Exploratory analysis of unfamiliar content
- Progressive refinement of understanding
- Executive summaries with drill-down capability
Our Implementation
- RAPTOR core with extensions for multi-document sets
- Domain-tuned hierarchies (e.g., spec divisions for construction)
- Query-driven layer selection: Overviews for broad asks, details for specifics
- Detail preservation via entity linking across levels
Hybrid Approaches
The Reality Real-world problems rarely fit neatly into a single technique. Our most powerful solutions combine multiple approaches:
RAG + GraphRAG: Fast semantic search enriched by graph relationships—ideal for cross-document queries in legal due diligence.
Multi-Layer + RAG: RAPTOR overviews for navigation, RAG drills for precision—perfect for construction spec reviews.
Semantic Search + Classic Keyword Search: For longer context documents, semantic (vector) searches can miss key details which conventional search finds easily.
Domain-Specific Optimizations
Legal Documents
- Contract clause identification and comparison
- Precedent analysis and case law citation
- Regulatory compliance checking
- Due diligence document review
Construction Specifications
- Spec section cross-referencing
- Code compliance verification
- Change order impact analysis
- Submittal and RFI correlation
Systems Engineering
- Requirements traceability
- Interface document management
- Design verification tracking
- Multi-subsystem integration analysis
Enterprise Knowledge Bases
- Policy and procedure lookup
- Historical decision context
- Cross-department information synthesis
- Institutional knowledge preservation
Implementation Flexibility
We integrate with your stack for seamless large-context document processing:
LLM Providers:
- Local (on-premise, self-hosted open source models such as Qwen, GPT-OSS, Gemma, Llama, Claude, Mistral, Granite)
- Cloud (OpenRouter including models from OpenAI, Anthropic, Google, Alibaba, Microsoft, Mistral, IBM)
- Fine-Tuning available for locally-hosted models
Front Ends / GUIs: TeraContext (Customized), RAGFlow, OpenWebUI, or AnythingLLM.
Vector Databases: ChromaDB, Milvus, Lance, Elasticsearch, Infinity, Neo4j, or Supabase.
Graph Databases: Neo4j, Elasticsearch, Infinity, or Supabase.
Deployment Options: Cloud api, dedicated cloud, on-premises, hybrid, or air-gapped.
Performance & Scalability
- Optimized for documents from hundreds of words to hundreds of megabytes
- Self-serve document ingestion or available as a service
- Incremental updates without full reprocessing
- Cost optimization for LLM API usage
- Local self-hosting for privacy and compliance
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