Full-text, RAG, and indexing infrastructure
Full-text Search LLC helps teams index tens of terabytes of structured and unstructured data across databases, on-site systems, and cloud infrastructure. We design practical full-text, hybrid, and RAG retrieval systems that improve answer quality, expose useful metadata, and reduce unnecessary search spend.
Capabilities
Search quality and infrastructure cost are both decided before the first query runs: how data is extracted, normalized, segmented, enriched with metadata, indexed, refreshed, and retrieved. We design that layer around real customer pain points, not generic templates.
Pipelines for structured and unstructured data from databases, file systems, SaaS exports, on-site infrastructure, and cloud storage — including parsing, deduplication, field extraction, language handling, and incremental refresh.
Schema design, analyzers, tokenization, metadata fields, ranking signals, filters, faceting, and latency-aware layouts for systems handling tens of terabytes of data without unnecessary storage, compute, or rebuild cost.
Chunking, metadata strategy, embeddings, hybrid retrieval, reranking, citations, and context assembly for internal knowledge tools, customer-facing search, and agentic systems that need to retrieve, inspect, and use the right evidence.
Approach
We tune the indexing layer around the data model, query patterns, update rate, metadata requirements, latency targets, and operating budget of each system. The goal is not just better relevance — it is better relevance at a sustainable cost.
Identify sources, document types, database fields, metadata, freshness rules, access patterns, cost drivers, and the queries that currently fail.
Choose full-text analyzers, chunks, metadata, vectors, filters, rerankers, and ranking signals based on measurable relevance, latency, and cost targets.
Implement ingestion, transforms, index writes, metadata extraction, validation, observability, incremental updates, and rollback paths across your stack.
Use query sets, relevance review, latency traces, infrastructure analysis, and answer audits to improve quality while reducing storage, compute, and rebuild overhead.
Where we fit
Full-text Search LLC helps when search works in demos but struggles in production: too slow, too expensive, too hard to refresh, missing metadata, poor ranking, weak RAG grounding, or infrastructure spend that grows faster than usage.
Tens of terabytes of structured and unstructured data
Databases, file stores, cloud buckets, and on-site systems
Metadata extraction from full-text search results
Hybrid keyword, vector, and filtered retrieval
Internal knowledge search and customer-facing search
RAG and agentic workflows with traceable context
Infrastructure cost reduction and search performance tuning
Delivery
Review corpus shape, current indexes, ranking behavior, metadata usage, latency, rebuild process, cloud or on-site spend, and operational constraints.
Build a measurable full-text, hybrid, or RAG retrieval prototype using representative data, realistic queries, metadata, and quality checks.
Ship ingestion, indexing, retrieval, monitoring, refresh, and deployment workflows that fit your database, cloud, and infrastructure constraints.
Contact
We can help assess your indexing path, retrieve metadata more effectively, design a full-text or RAG architecture, optimize infrastructure spend, or build the production pipeline behind internal, customer-facing, and agentic search applications.