Integrate GPT-4, Claude, Llama, and custom models into your products with production-grade RAG pipelines, hallucination guardrails, source citations, and enterprise access control. We build LLM applications that are accurate, auditable, and actually trustworthy.
The gap between a ChatGPT demo and a production LLM application is enormous. Hallucinations, prompt injection, data leakage, inconsistent outputs, and lack of source attribution make most LLM prototypes unusable for enterprise. QuantPi bridges that gap. We engineer LLM systems with retrieval-augmented generation, structured guardrails, fine-tuning, and rigorous evaluation — so your AI gives accurate, cited, and compliant answers every time.
Our RAG pipelines process millions of documents with semantic chunking, hybrid search (vector + BM25), re-ranking, and multi-step reasoning. We build on LangChain, LlamaIndex, Pinecone, ChromaDB, and Weaviate, and we support GPT-4, Claude, Llama, Mistral, and custom fine-tuned models. Every system includes evaluation frameworks, observability, and continuous improvement loops.
Schedule a DemoSemantic chunking, hybrid vector + keyword search, re-ranking, multi-hop reasoning, and citation generation. Optimized for your document types and query patterns.
Domain-specific fine-tuning using LoRA, QLoRA, and RLHF. Reduce hallucinations, improve tone consistency, and align outputs with your brand voice.
Extract structured data from contracts, invoices, medical records, and legal documents using LLM-powered classification, extraction, and validation.
Multi-layer guardrail systems: input validation, output grounding checks, factuality scoring, and automated red-teaming.
Route queries to the optimal model based on complexity, cost, and latency. Graceful fallbacks and load balancing across providers.
Role-based document access, PII redaction, conversation logging, and compliance audit trails for regulated industries.
Audit your document corpus, define retrieval requirements, and design the RAG architecture.
1-2 weeksBuild ingestion, chunking, embedding, indexing, retrieval, and generation pipeline with evaluation suite.
3-5 weeksImplement safety layers, fine-tune for domain accuracy, and optimize latency/cost tradeoffs.
2-3 weeksDeploy with monitoring, A/B testing, user feedback loops, and continuous improvement automation.
1-2 weeksInternal AI assistants that answer HR, IT, policy, and process questions using company documentation — with source citations and access control.
Learn moreLLM-powered contract review that identifies key clauses, risks, obligations, and deviations from standard terms in seconds.
Learn moreAI agents that resolve tier-1 support tickets using product documentation, knowledge bases, and conversation history — with human escalation.
Learn moreAutomated regulatory document analysis, policy mapping, and compliance gap identification for financial services and healthcare.
Learn moreOur production RAG systems achieve 90-96% answer accuracy with source citations. We measure with automated evaluation suites and continuous human feedback loops.
Yes. All data stays within your infrastructure. We deploy on your cloud with VPC isolation, encryption at rest and in transit, and zero data retention on third-party APIs.
OpenAI GPT-4, Anthropic Claude, Meta Llama, Mistral, Cohere, and any open-source model. We design for provider flexibility and easy switching.
Multi-layer approach: grounded retrieval, factuality scoring, output validation, automated red-teaming, and human feedback loops. We measure hallucination rates and continuously improve.
Start with a technical conversation. No pitch decks, no pressure — just a discussion about what’s possible.