Deployable AI Agent & RAG Platforms

A 2024 report found that 38% of employees admitted to pasting confidential work information into ChatGPT. Financial projections. Client contracts. Proprietary source code. Internal HR records. They're not doing it maliciously — they're doing it because the tool is useful and the alternative (using whatever their company's official knowledge base software is) is a pain.

The companies that recognized this early did one of two things: they blocked public AI tools entirely (which just pushed usage underground) or they deployed a private AI system that gives employees the same conversational interface, but keeps all data within their own infrastructure.

The Deployable AI Agent & RAG Platform is the technical implementation of the second approach. You host it. Your data never leaves your VPC. The AI answers questions about your internal documents and processes, not about the public internet.

What RAG Is and Why It Matters More Than Fine-Tuning

There are two ways to make an AI model knowledgeable about your company's specific information. Fine-tuning (retraining the model on your data) is expensive, takes weeks, becomes stale the moment your information changes, and still sends your data to a model provider. Retrieval-Augmented Generation (RAG) is different.

With RAG, your documents are indexed into a vector database. When a user asks a question, the system retrieves the most relevant document passages and provides them to the AI as context alongside the question. The AI answers based on what it just read, not what it was trained on months ago.

This means:

• Your data stays current — index a new document and the AI knows about it immediately
• Answers are grounded — the AI cites the exact source document it used to answer
• You can revoke access — remove a document from the index and the AI can no longer reference it
• No fine-tuning costs — the same base model is used for everyone; only the context changes

Automated Ingestion: The Part That Breaks in Every DIY Implementation

Building a one-off script to index a folder of PDFs is straightforward. Building a system that reliably ingests documents as they're uploaded, handles formatting failures gracefully, chunks content intelligently, maintains source attribution, and keeps the index updated as documents change — that's the engineering work that takes months.

This platform ships with a production-grade ingestion pipeline built on FastStream:

• File watchers monitor an S3 bucket or local directory for new files
• Format parsers handle PDF (with OCR fallback for scanned documents), Word, Markdown, HTML, and plain text
• Intelligent chunking splits documents by semantic boundary (paragraph breaks, heading changes) rather than naive character count — this matters because a chunk that splits mid-sentence produces low-quality embeddings
• Embedding generation using your choice of OpenAI, Cohere, or a locally-hosted model (for maximum privacy)
• Metadata preservation — filename, creation date, author, department, and custom tags are stored alongside each chunk so you can filter retrieval by department or document type
• Incremental updates — when a document is modified, only the changed sections are re-indexed

Conversational Memory for Multi-Turn Interactions

Most RAG implementations treat every query as isolated. Ask "What was the revenue in Q3?" and the system retrieves relevant documents. Ask "How does that compare to Q2?" on the next turn and the system has no idea what "that" refers to.

The platform uses Redis-backed conversation memory that maintains the last N exchanges per user session. The memory is injected into the retrieval query alongside the current message so the system understands context across turns. Memory windows are configurable — short for lightweight queries, longer for complex research sessions.

Agent mode enables multi-step reasoning: the AI can decide to retrieve additional documents mid-chain if its initial context isn't sufficient to answer a question. This produces significantly better answers on complex analytical questions.

Deployment: Inside Your VPC, Under Your Control

The entire stack runs on your infrastructure. The default deployment target is AWS (ECS + RDS for PostgreSQL, ElastiCache for Redis, S3 for document storage) with a provided CloudFormation template. On-premise deployments on Kubernetes are supported with Helm charts.

The AI model calls can be routed to:

• AWS Bedrock — runs foundation models (Claude, Llama) inside your AWS account, data never leaves AWS
• Azure OpenAI — Microsoft's enterprise OpenAI deployment with contractual data privacy guarantees
• Locally-hosted models — Ollama or vLLM for complete air-gap deployments with no external API calls

The codebase is fully open and documented. You're not locked into a vendor, a model provider, or a pricing model that changes under you.