DocsGPT

DocsGPT's pitch is narrow and coherent: your data, your infrastructure, code you can read. Where a general-purpose assistant is trained on the public web and guesses at your internal specifics, DocsGPT answers from the documentation, source code, and Microsoft files you feed it. For an engineering team searching its own codebase, or a compliance team that cannot send contracts to someone else's cloud, that grounding is the whole point. The visual agent builder is the piece that separates it from a plain 'chat with your PDF' tool. You assemble an assistant by wiring blocks together rather than writing a retrieval pipeline from scratch, then publish it to the people who need it. Published multi-model support for GPT-4o and Claude lets you route different jobs to different vendors instead of locking into one. Deep research and document analysis cover the heavier multi-pass queries a single chat turn cannot finish. Governance is where the published plan structure earns its keep. SSO, granular access controls, audit logs, and encryption at rest and in transit sit on the Team tier rather than being bolted on later, and on-premises and private-cloud deployment are published at the Enterprise level. For a regulated buyer, that list is usually the difference between a shortlist and a rejection. The honest weaknesses: the published integration list is short — Slack, Google Drive, and Confluence — so if your knowledge lives across a dozen SaaS tools, expect to build the connections yourself rather than find them in a marketplace. Self-hosting, which is the feature that attracts most of this audience, is also the cost: upgrades, scaling, and model routing become your operational problem. And the free entry point published at 1 user, 100 MB, and 10 queries per day will not survive contact with a real team, so treat it as an evaluation and price the paid tiers before you commit.