4.8
on G2
Docsie helps enterprises wanting AI docs with their own models. Route all AI to your own LLM endpoints (vLLM, Ollama, Bedrock). Per-org isolation, encrypted keys, zero external API calls. ChatGPT for your docs on your hardware.
Join forward-thinking teams using Docsie
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You've invested millions in your own AI infrastructure. Your security team has vetoed every SaaS AI tool. But your teams still need intelligent documentation assistance.
Why Docsie
The only documentation platform built for organizations that run their own AI infrastructure
Your documentation never touches external AI services. Every query, every response, every piece of content stays within your infrastructure. Each organization gets fully isolated environments with encrypted credentials — no shared resources, no cross-contamination.
You've already built the AI infrastructure. Now use it to transform how your organization creates and consumes documentation. Connect to the models you've already deployed and tuned for your domain — whether that's healthcare, finance, defense, or proprietary research.
Built for the most regulated industries. No external dependencies, no data egress, no third-party AI providers. Your security and compliance teams can actually approve this — because it works entirely within the boundaries you've already established.
Organizations using their own AI infrastructure to power intelligent documentation
Defense contractors and government agencies need AI-powered documentation tools, but classified content can never leave secure networks. Docsie connects to your air-gapped language models, giving engineers intelligent search and generation without compromising security clearances.
Healthcare organizations need AI that understands medical documentation, but PHI can never touch public cloud AI. Connect your documentation to models you've fine-tuned on medical literature — all running in your HIPAA-compliant infrastructure.
Investment firms and trading operations have proprietary strategies documented across hundreds of systems. Your quantitative teams have already deployed internal language models — now connect them to your documentation so analysts get intelligent answers without sending trading strategies to OpenAI.
Everything you need to run AI-powered documentation on your infrastructure
Works with whatever you're running — vLLM, Ollama, AWS Bedrock, or your custom deployment
Every customer gets completely isolated environments — no shared infrastructure, ever
Your model credentials are encrypted at rest and never exposed to other tenants
No fallbacks, no backups to public AI services — if you don't configure it, we don't call it
See how your teams use AI-assisted documentation without sending data outside your walls
Tune response times, context windows, and behavior using your own infrastructure and budgets
Common Questions
Answers for security, compliance, and infrastructure teams
Q: What AI infrastructure do we need to already have in place?
A: You need a language model deployed somewhere your network can reach — whether that's vLLM on your GPU cluster, Ollama on local servers, AWS Bedrock in your VPC, or any custom deployment. If your teams can already query it, Docsie can connect to it.
Q: How long does it take to connect our existing models?
A: Most organizations are up and running in under an hour. You provide the connection details to your model, we handle the rest. No data migration, no model training, no infrastructure changes on your end.
Q: Can we use different models for different teams or documentation sets?
A: Absolutely. Some customers use smaller, faster models for general docs and larger, specialized models for technical knowledge bases. You control which documentation connects to which models.
Q: Does any of our documentation content ever reach your servers or external AI providers?
A: No. When someone queries your documentation, that request goes directly from Docsie to your language model infrastructure. Responses flow back the same way. We never store, log, or transmit your content to external AI services or our own infrastructure for processing.
Q: How do you handle multi-tenant isolation if we're in your cloud version?
A: Every organization gets completely isolated environments with separate encrypted credential storage. Your model connections, your queries, and your documentation are never accessible to other tenants. Think of it as dedicated infrastructure even in a shared platform.
Still have questions?
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