In a world of basic RAG implementations that hallucinate, drift, and bleed tokens, Katara delivers a production-grade system engineered for reliability at scale. Every layer—from ingestion to generation—is battle-tested with advanced techniques like hybrid retrieval, strict grounding, self-verification, and built-in cost controls.
Whether you're building internal tools, customer-facing agents, or mission-critical workflows, this isn't just RAG—it's the foundation that lets new AI developers ship confident, compliant, and cost-effective agentic applications faster than ever.
Full technical capabilities – built for reliability, observability, and enterprise-scale agentic applications
| Area | Capability | Description |
|---|---|---|
| Data & Ingestion | Data cleaning & deduplication | Normalized, deduplicated, versioned source documents |
| Provenance & metadata | Source, section, date, ownership stored as metadata | |
| Version handling | Explicit document versioning and update strategy | |
| Structure-aware splitting | Markdown / HTML / PDF layout-aware chunking | |
| Semantic chunking | Meaningful chunks (paragraphs, sections) | |
| Multi-granularity chunks | Paragraph-level + section-level chunks | |
| Embeddings & Indexing | Domain-tuned embeddings | Embeddings chosen or fine-tuned for domain |
| Embedding evaluation | Recall@k, clustering sanity checks | |
| Hybrid representations | Dense + sparse (BM25) embeddings | |
| Metadata filtering | Filters by doc type, date, confidence | |
| Multiple indexes | Separate indexes by type / time / trust | |
| Retrieval | Hybrid retrieval | Vector + keyword + metadata retrieval |
| Query intent detection | Fact vs comparison vs synthesis detection | |
| Query rewriting | LLM-based query expansion / normalization | |
| Multi-query retrieval | Multiple sub-queries per user question | |
| Score fusion | RRF or weighted score merging | |
| Reranking | Cross-encoder or LLM-based reranking | |
| Strict top-k control | Small, high-quality context set (≤10) | |
| Context Construction | Deduplication | Remove overlapping or redundant chunks |
| Logical ordering | Preserve document / section order | |
| Context annotation | Inject titles, sections, source IDs | |
| Token-aware packing | Dynamic context sizing by complexity | |
| Chunk prioritization | Importance-based context selection | |
| Generation | Grounded prompts | “Answer only from context” enforcement |
| Explicit citations | Answers reference specific sources | |
| Abstention handling | Model can say “I don’t know” | |
| Structured outputs | JSON / schema-validated responses | |
| Model routing | Small vs large models based on task | |
| Self-verification | Optional reflection or answer checking | |
| Evaluation | Golden test set | Curated Q&A with supporting passages |
| Retrieval metrics | Recall@k, MRR, hit rate | |
| Answer correctness | Human or LLM-assisted scoring | |
| Faithfulness checks | Hallucination / grounding metrics | |
| Regression tests | Ingestion, chunking, retrieval regressions | |
| Online evaluation | Live feedback & quality signals | |
| Observability | End-to-end tracing | Query → retrieval → generation traces |
| Retrieval inspection | Debuggable retrieved chunks | |
| Prompt versioning | Prompt changes tracked over time | |
| Experiment tracking | A/B tests for models & retrieval | |
| Drift detection | Query & data distribution monitoring | |
| Safety & Trust | Access control | Document-level permissions enforced |
| PII handling | Redaction / filtering before indexing | |
| Source transparency | Users can see where answers come from | |
| Confidence signaling | Surface uncertainty when relevant | |
| Performance & Cost | Async retrieval | Parallel search & reranking |
| Caching | Cached embeddings & retrieval results | |
| Early exits | Abort generation on low confidence | |
| Token budgeting | Hard limits per query | |
| Cost monitoring | Cost per query tracked and optimized |
Thank you for reading through the detailed feature matrix covering ingestion, retrieval, generation, evaluation, observability, and more. With Katara you get enterprise-grade, production-ready RAG solution.
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