Autonomous AI &
Custom RAG
“Production-grade AI systems that query your proprietary data accurately, execute multi-step workflows autonomously, and eliminate hallucinations.”
Transform passive documents, databases, and APIs into dynamic conversational interfaces and autonomous agents. We engineer state-of-the-art Retrieval-Augmented Generation (RAG) architectures with hybrid vector/lexical search, re-ranking filters, citation verification, and enterprise-grade security.
Key Deliverables
Production-ready RAG ingestion & chunking pipeline
Hybrid Vector Database setup (Qdrant, Pinecone, pgvector)
Autonomous multi-agent orchestration (LangChain, LangGraph, LlamaIndex)
Deterministic fallback & hallucination guardrails
Real-time streaming UI with citation provenance
Local Ollama / Private cloud LLM deployment options for data privacy
Case Studies & Scenarios
Enterprise Internal Knowledge Search
A company has thousands of PDF policies, Notion pages, and Slack threads that employees struggle to search.
Deploys an instant AI knowledge engine with 99%+ citation accuracy, cutting employee lookup time by 80%.
Autonomous Customer Support & Triage Agent
Handling complex support tickets that require fetching user account data, diagnosing issues, and triggering API actions.
Resolves 60% of common multi-step tickets end-to-end without human intervention, with clean human-in-the-loop escalation.
Automated Document Intelligence & Extraction
Processing invoices, legal contracts, or technical blueprints into structured database records.
Eliminates hundreds of hours of manual data entry while achieving zero data loss through schema-validated extraction.
Frequently Answered Questions
How do you prevent the AI from making up false answers (hallucinations)?
We implement strict grounding techniques: hybrid semantic + keyword retrieval, cross-encoder re-ranking, and dynamic context windows that enforce the model to cite exact sentence sources. If the answer is not in the source documents, the agent explicitly states it is unavailable.
Can this run completely privately without sending data to third-party APIs?
Yes. We support local and self-hosted open-weights models (like DeepSeek, Llama 3, or Mistral) running on private cloud instances (AWS/GCP) or on-premise hardware using Ollama and vLLM.
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