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From retrieval-augmented generation to agentic workflows — the architectures and systems behind our solutions.
Our RAG pipeline transforms raw data into accurate, contextual answers — with hybrid retrieval, re-ranking, and production-grade reliability.
PDFs, APIs, databases, web scraping — structured and unstructured data sources.
Intelligent text splitting with overlap, preserving context boundaries.
Dense vector representations via OpenAI, Cohere, or open-source models.
Pinecone, Weaviate, or pgvector — indexed for millisecond retrieval.
Query expansion, HyDE, and multi-query strategies for better recall.
Hybrid search (dense + sparse) with cross-encoder re-ranking.
Prompt construction with retrieved chunks, metadata, and guardrails.
GPT-4, Claude, or open-source — with streaming and fallback chains.
A conductor-agent architecture where a planner orchestrates specialized tools — selecting, executing, and synthesizing results autonomously.
Natural language query or task request arrives.
Planner agent decomposes the task and selects tools.
Appropriate tools are chosen based on task requirements.
Tools execute in parallel or sequence with error handling.
Results are validated, refined, and synthesized.
Final answer or action delivered with citations and confidence.
The tools and platforms we use to build, deploy, and monitor production systems.