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ChattyDocs
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ChattyDocs Verified Tool

ChattyDocs creates conversational datasets from uploaded PDFs, text files and website links, allowing users to choose models, context allocation, system prompts and sharing settings. Users should upload authorized documents, protect confidential data, verify quotations and answers, inspect source context and retain expert judgment.

Last Update: August 20, 2026

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Starting price Free trial + paid plans

Tool Information

ChattyDocs creates conversational datasets from uploaded PDFs, text files and website links, allowing users to choose models, context allocation, system prompts and sharing settings. Users should upload authorized documents, protect confidential data, verify quotations and answers, inspect source context and retain expert judgment.

Use authorized inputs and least-privilege integrations. Configure privacy, retention, sharing, accessibility, disclosure, export, moderation and spending controls. Test representative cases, verify facts, calculations, citations, messages and media, preserve originals and retain accountable human approval before publication, outreach, deployment or operational action.

A free trial is advertised and paid credit-based plans are available, although the reviewed homepage does not expose a stable current entry amount. Upload credits, chat usage, models, datasets, sharing, renewal and taxes vary.

AI output can be inaccurate, biased, derivative, insecure, incomplete or misleading. Review consent, copyright, training and retention terms, renewals, refunds, platform rules and applicable law. Employment, education, health, finance, legal, marketing and customer-facing workflows require qualified human review.

F.A.Q (3)

ChattyDocs creates conversational datasets from uploaded PDFs, text files and website links, allowing users to choose models, context allocation, system prompts and sharing settings. Users should upload authorized documents, protect confidential data, verify quotations and answers, inspect source context and retain expert judgment.

Use authorized inputs and least-privilege integrations. Configure privacy, retention, sharing, accessibility, disclosure, export, moderation and spending controls. Test representative cases, verify facts, calculations, citations, messages and media, preserve originals and retain accountable human approval before publication, outreach, deployment or operational action.

Verified pricing: Free trial + paid plans. A free trial is advertised and paid credit-based plans are available, although the reviewed homepage does not expose a stable current entry amount. Upload credits, chat usage, models, datasets, sharing, renewal and taxes vary.

Pros and Cons

Pros

  • Chats with multiple PDF and text documents in one dataset
  • Can import content from website links
  • Lets users add or remove sources later
  • Indexes uploaded content in the background
  • Returns source references with document names and page numbers
  • Supports multiple persistent chat sessions
  • Lets users define a reusable chat context
  • Allows control over model; context size; and temperature
  • Supports custom assistant names; personalities; and languages
  • Creates public datasets that can be shared by link
  • Exports questions and answers
  • Creates Telegram bots from datasets
  • Works through desktop; mobile; embedded web; and Telegram interfaces
  • Provides GraphQL API access on Scale and Enterprise
  • ChattyDocs offers a free trial without a credit card
  • Publishes simple monthly prices
  • Offers a Docker-based self-hosting option by custom quote
  • Displays file quota consumption before upload

Cons

  • The free trial includes only twenty queries and 900 KB of uploads
  • Casual costs $9 per month and permits only 1;000 monthly queries
  • Casual is limited to twenty datasets and thirty documents per dataset
  • Casual provides only one Telegram bot
  • API access requires the $49 Scale plan or higher
  • Scale limits uploads to thirty-five megabytes per month
  • Enterprise costs $399 per month
  • Even Enterprise caps uploads at 200 MB and queries at 30;000 per month
  • Public share links can expose sensitive source content or generated answers
  • Telegram and embedded deployments introduce additional platform and privacy risks
  • Document answers can hallucinate despite displaying sources
  • Source references do not guarantee that the cited passage supports the answer
  • Website import may capture stale; incomplete; or irrelevant content
  • Scanned PDFs and complex tables may parse poorly
  • Model; context; and temperature controls require expertise
  • Query and upload quotas can make large research collections expensive
  • Self-hosting requires technical operations; updates; backups; and security ownership
  • The pricing page does not disclose every model cost or overage rule
  • Teams should verify retention; deletion; training; subprocessors; and regional hosting
  • A shared dataset needs access review when its source files change

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