IronCloud runs modern AI in secure environments
without giving up compliance, privacy, or control.
IronCloud runs modern AI in secure environments
How IronCloud Works
IronCloud runs fully self-hosted AI that connects to your internal systems, indexes your content, and makes it searchable, audible, and usable — all inside your own infrastructure.
It’s built for teams that need real results without external APIs, SaaS lock-in, or compliance risks.
The GPT Experience — Without the SaaS
A secure, self-hosted chat interface that feels just like ChatGPT — but runs entirely on your infrastructure.
IronCloud delivers the same powerful chat interface users expect from platforms like ChatGPT — but running entirely inside your environment. The interface you see here is LibreChat: a fully self-hosted frontend that looks and feels like familiar commercial tools, while operating securely on your own infrastructure.
Users can ask natural language questions, explore documents, and generate output across multiple tasks — all with real-time responsiveness and zero external dependencies. LibreChat supports model switching, chat history, inline code rendering, and search integration out of the box.
It’s designed to feel intuitive for non-technical users while giving your internal teams access to powerful LLM-backed reasoning tools. And because it’s integrated with IronCloud’s backend, responses can be grounded in your internal data — whether that means project wikis, tickets, documents, or source code.
This isn’t a simplified chatbot. It’s a real AI interface, deployed securely, using only the models and data you authorize.
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Contextual Conversations
Users can start structured chats grounded in internal data — directly within a private interface.
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Inline Code + Reasoning
Responds with executable logic, document references, or task breakdowns — all locally generated.
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Model Flexibility
Swap between self-hosted models securely. No cloud calls. No public API traffic.
Fast, Private Search Over Your Own Data
IronCloud builds semantic indexes over your internal systems — enabling retrieval-augmented generation without exposing a single query.
Your content is only useful if you can find it. IronCloud uses secure, self-hosted indexing pipelines to turn your internal documents, tickets, wikis, and source code into a searchable knowledge base — all behind your firewall.
Powered by Azure AI Search and custom indexers, the platform builds semantic indexes that work across structured and unstructured data. Content becomes instantly accessible via natural language queries, and the results are fast, permission-aware, and traceable.
Unlike public-facing AI tools, IronCloud doesn’t send queries to the cloud or rely on external APIs. All indexing, embedding, and retrieval happens locally — so you retain full control over what’s stored, what’s searchable, and who has access.
This is the foundation of our retrieval-augmented generation (RAG) pipeline: a clean, scalable search layer that feeds your LLM with real context from your environment — not hallucinated answers from the web.
Connect to the Systems You Already Use
IronCloud pulls real-time data from Jira, Confluence, Monday.com, SharePoint, Git and many more — with zero plugins or vendor lock-in.
Your teams already work in tools like Jira, Confluence, SharePoint, and Git. IronCloud doesn’t try to replace them — it connects directly to them through our lightweight integration layer based on the Model Context Protocol (MCP).
This allows IronCloud to ingest data from your internal platforms securely, index that content for search, and make it available for retrieval-augmented generation and document interaction — all without breaking your existing workflows or needing third-party plugins.
Each connection honors existing access controls and permission models. No new user databases. No risky syncs. Just real-time, read-only visibility into the data your teams already rely on.
The result: your AI isn’t guessing. It’s working with context pulled directly from your actual project tickets, internal documentation, codebases, and repositories — with full control over what’s visible, searchable, and auditable.
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Search Across Code and Projects
Query issues, source files, and internal documentation — all from a single private interface that understands your tools.
And of course there is more to explore.
Other noteworthy features are:
Retrieval-Augmented Generation (RAG)Text-to-Speech (TTS) & Speech-to-Text (STT)Enterprise SearchMCP Integration LayerCompliance ArchitectureFully Self-HostedSearch Indexing & Retrieval
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) enables powerful, context-aware AI responses by combining large language models with an enterprise knowledge base. Instead of relying solely on model training data, RAG dynamically queries your internal documentation, policies, or repositories and augments responses with the most relevant information. This approach dramatically improves factual accuracy, ensures traceability, and aligns AI output with company-specific language and content.
At IronCloud, we implement RAG within secure government cloud environments using open-source tools, allowing organizations to layer AI reasoning on top of their SharePoint sites, Confluence spaces, Git repositories, and more—all without compromising security or compliance. Our RAG infrastructure supports hybrid indexing strategies and document chunking for nuanced search and precision. Content remains entirely within your network perimeter, and queries are optimized for low-latency retrieval from Azure AI Search or other engines.
Whether you need to surface technical manuals, contract clauses, or internal HR policies in real time, IronCloud’s RAG capabilities turn your data into a responsive, searchable intelligence layer for your LLMs.
Text-to-Speech (TTS) & Speech-to-Text (STT)
IronCloud integrates both Text-to-Speech (TTS) and Speech-to-Text (STT) capabilities to enable voice-based interaction with your LLMs in secure environments. We deploy open-source voice models such as Coqui TTS and Whisper STT inside fully self-hosted, containerized environments that run within AWS GovCloud, GCC High, or air-gapped networks.
TTS lets you convert any LLM output into natural-sounding audio responses. This is ideal for accessibility, hands-free workflows, or deploying conversational AI assistants in operational environments. Conversely, STT enables real-time transcription of user voice input, supporting dictation, transcription, or verbal command interfaces.
These systems are optimized for low-resource use and high accuracy, with pre-loaded voice profiles and custom vocabulary injection. IronCloud’s voice pipeline works fully offline, ensuring that sensitive audio never leaves the boundary of your network.
Enterprise Search Infrastructure
IronCloud equips organizations with the ability to perform advanced, semantic search across their internal data. Built on secure, scalable architectures using tools like Azure AI Search and custom indexers, our system ingests, indexes, and surfaces content from diverse sources including SharePoint, Confluence, file shares, and Git repositories.
Our solution supports metadata tagging, vector-based retrieval, and multi-source federation to deliver highly relevant results within milliseconds. It is the backbone of both RAG and standalone internal search engines for engineering, legal, and operations teams.
Unlike traditional keyword search, IronCloud’s semantic indexing architecture understands context, synonyms, and domain-specific phrasing. We ensure that access control and permissioning is respected at every level, making sure users can only retrieve data they are entitled to see. Indexes are rebuilt securely on a recurring schedule and kept in sync with changes to underlying systems.
MCP Integration Layer (Jira, Confluence, Git, SharePoint)
Our Model Context Protocol (MCP) integration layer enables IronCloud LLM to connect directly to common enterprise tools like Jira, Confluence, GitLab/GitHub, and SharePoint. These integrations allow users to interact with organizational knowledge and project activity through a unified LLM interface.
Want to ask “What tickets were updated this week in the propulsion subsystem?” or “What decision did we make in the most recent design review?” MCP handles the secure, structured retrieval of that data in real-time. Our integration layer provides read-only adapters that interact with these tools using access-controlled service accounts, ensuring zero risk of unauthorized write operations.
MCP servers run in containers and communicate via REST or stdio, depending on architecture. Each integration is isolated, rate-limited, and logged for auditability. The resulting data is transformed into structured prompts and securely routed to the LLM engine for interpretation and summarization. This turns fragmented data across tools into coherent, queryable intelligence for your team.
Compliance Architecture
IronCloud LLM was designed with compliance as a first principle. Every component—from model orchestration to data storage—is architected to meet strict requirements such as ITAR, CUI, and FedRAMP High. Our deployment models avoid all public traffic, enforce network segmentation, and rely solely on compute inside your secure cloud boundary (e.g., AWS GovCloud or Azure GCC High).
Audit logging, identity management, access controls, and encryption at rest and in transit are baked in. Our infrastructure supports user-based policies and RBAC enforcement for sensitive data access. For customers operating under DFARS or CMMC mandates, we ensure data localization and provide full documentation for SSP and POA&M development.
We prioritize transparent infrastructure that your ISSO, AO, or cybersecurity team can inspect, control, and validate. Our platform includes prewritten documentation templates and example policy references to streamline compliance processes.
Fully Self-Hosted
IronCloud is 100% self-hosted. Every container, model, and integration service runs within your infrastructure—not ours. There is no third-party data handling, no SaaS backend, and no telemetry back to the vendor. You own everything.
This architecture is ideal for agencies, defense contractors, and enterprises that cannot allow data to leave their secure boundary. Whether running in an air-gapped enclave or within a tightly restricted cloud VPC, IronCloud gives you full control over orchestration, data flow, and monitoring.
We deploy via Docker Compose or Kubernetes, with preconfigured modules for each subsystem (LLM inference, search indexers, voice pipeline, MCP servers). Documentation, Helm charts, and automation scripts are included, along with training and handoff support. You’re never dependent on a cloud API key or outside gateway to operate your LLM stack. IronCloud is your private AI platform.
Enterprise Document Indexing
IronCloud supports full-scale enterprise document indexing and retrieval across your private data sources—without ever sending a byte to the cloud.
Our system crawls, extracts, and indexes content from platforms like SharePoint, Confluence, Jira, Git, and file repositories, then builds custom search indexes tailored to your organization. We use open source pipelines and metadata enrichment to tag content with context, permissions, and source system lineage.
The result: a search layer that understands your files, your tickets, your documentation—and returns relevant results instantly through secure, local endpoints.
This capability powers everything from “chat with your docs” assistants to secure retrieval-augmented generation (RAG) applications for technical teams, program managers, and leadership. Whether you’re querying recent commit messages, Confluence SOPs, or ITAR-tagged PDFs, your data stays private, accurate, and instantly searchable.
IronCloud indexing works on-prem, in GovCloud, and inside air-gapped enclaves—no cloud connections, no tracking, no dependencies.