--- title: "RAG Chatbots & Enterprise AI Assistants" description: "We build secure RAG chatbots that answer from your own documents with a citation on every claim. Stop hallucinations with hybrid search and strict re-ranking." image: "https://foundrysoft.co/api/og?type=page&title=RAG+Chatbots+%26+Enterprise+AI+Assistants&st=We+build+secure+RAG+chatbots+that+answer+from+your+own+documents+with+a+citation+on+every+claim.+Stop+hallucinations+with+hybrid+search+and%E2%80%A6" url: "https://foundrysoft.co/services/rag-chatbots" --- Primary / Retrieval Primary / Retrieval # RAG Chatbots & Enterprise AI Assistants We build secure RAG chatbots that answer from your own documents with a citation on every claim. Stop hallucinations with hybrid search and strict re-ranking. Get in touch [All services](https://foundrysoft.co/services) Focus Primary / Retrieval Engagement Fixed-scope or dedicated Timeline From 4 weeks Ownership 100% yours How we deliver ## Our process for rag chatbots & enterprise ai assistants A fixed four-step path from first call to production, with weekly demos and a hard launch date. Days 1–4 Step 01 ### Discovery & data audit We map your use case, evaluate data readiness, and define success metrics and guardrails up front. Deliverable Feasibility report & eval plan Days 5–9 Step 02 ### Model & pipeline design We architect the retrieval, model, and orchestration layers with cost, latency, and safety in mind. Deliverable Architecture & prompt/eval harness Weeks 2–3 Step 03 ### Build, evaluate & harden We build with a regression eval suite, add guardrails against prompt injection and PII leaks, and tune quality. Deliverable Tested system & eval dashboard Week 4 Step 04 ### Deploy & monitor We ship to production with telemetry, cost controls, and one-click rollback, then hand over full ownership. Deliverable Live system, docs & handover See where your project fits. [Book your systems audit](https://foundrysoft.co/contact) ### Reliable answers from your own data Chatbots that make things up are a massive liability to your business. When users ask questions about your policies, products, or internal data, they need facts, not creative guesses. We build enterprise-grade **Retrieval-Augmented Generation (RAG) chatbots** that ensure every answer is backed by your private documents, with clear, clickable citations for every claim. - **Smart Hybrid Retrieval:** We combine traditional keyword search (BM25) with modern semantic search (Vector Embeddings) to find the most relevant information. We then use Cross-Encoder re-ranking to prioritize the best results before feeding them to the LLM. - **Complex Document Parsing:** Most RAG systems fail because they can't read tables or charts. Our parsing pipelines can understand complex layouts in PDFs, spreadsheets, presentations, and raw transcripts to extract data accurately. - **Fact-Checked Responses (Zero Hallucination):** Every answer includes mandatory citations. If the system doesn't find the answer in your provided documents, it is programmed to say "I don't know" instead of inventing a plausible-sounding lie. - **Private & Secure Memory:** We build secure conversational memory that respects your data retention and privacy policies. Your proprietary data never trains public models. ### Technical standards and Architecture Every answer generated by our RAG chatbots can be traced directly back to its source document. We measure every part of the pipeline to ensure speed, accuracy, and security at scale. - **Grounding & Accuracy:** We aim for 100% citation accuracy, actively suppressing any claims that aren't backed by retrieved data chunks. - **High Recall Systems:** We test the retrieval system against specific domain datasets (Evaluations/Evals) to ensure it finds the right information every single time, even for nuanced queries. - **Speed & Latency:** We optimize both the retrieval database (Pinecone, Qdrant, pgvector) and the generation streaming (Vercel AI SDK) to provide sub-second Time-to-First-Byte (TTFB) response times. - **Multi-Agent RAG:** For complex queries (e.g. "Compare Q1 and Q2 revenue"), standard RAG fails. We implement Agentic RAG workflows where models can use tools to query databases multiple times and perform math before answering. Stop dealing with generic AI wrappers. Build a RAG system that actually understands your business. #### What's included - Production-grade evals & guardrails - Full telemetry and cost controls - Prompt & model version control - 100% IP, model & data ownership #### Have a project like this? Tell us the goal, we'll reply with a scope and a fixed quote within a day. Get in touch #### Next service [React Native Development](https://foundrysoft.co/services/react-native-development) Explore more ## Related services [All services](https://foundrysoft.co/services) [Primary / Agents ### AgentOps Run AI agents in production with telemetry, regression evals, and guardrails. We add observability, prompt versioning, and one-click rollbacks before launch. Learn more](https://foundrysoft.co/services/agentops) [Artificial Intelligence (AI) ### AI Agent for Customer Service Expert AI Agent for Customer Service services by FoundrySoft. We build scalable, secure, and modern solutions tailored to your business needs. Learn more](https://foundrysoft.co/services/ai-agent-customer-service) [Artificial Intelligence (AI) ### AI Agent Development Expert AI Agent Development services by FoundrySoft. We build scalable, secure, and modern solutions tailored to your business needs. Learn more](https://foundrysoft.co/services/ai-agent-development) ## Ready to build rag chatbots & enterprise ai assistants? Every project starts with a clear scope and a fixed timeline. Tell us what you're building and we'll reply within one business day. 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