A Kapa.ai alternative
for your whole business, not just docs

They solve different problems. Kapa.ai builds a customer-facing chatbot from your developer docs, it answers questions from your product's users. Context Link gives your team one connected knowledge base its AI tools can read and write. If you need a docs chatbot for developers, Kapa does that well. If you want AI grounded in your whole business, Notion, Docs, email, websites, and files, with citations on every answer, Context Link is built for that.

Not a widget, no. Context Link does not ship an embeddable chat surface, and if a docs chatbot for your users is the whole requirement, Kapa is purpose-built for it. What Context Link offers SaaS teams is the layer behind a surface you build: the RAG White-Label plan gives each of your end customers a private vector index built from their website and/or the data you feed it from your own API calls, provisioned over a REST API and queried server-to-server under your brand. Your product calls GET /api/v1/question (Ask questions) for one cited paragraph, or GET /api/v1/context (Get context) for the matching passages, and renders the result however you like. End customers never see Context Link and no token reaches a browser, and there is no vector database or scoring algorithm to host. Overview at context-link.ai/for/white-label-rag-api, details at context-link.ai/docs/white-label.

Kapa indexes your documentation once and answers every user from that shared index. RAG White-Label indexes each end customer separately: their website (crawled, up to 500 pages, and chunked into searchable markdown) and/or the data you feed it from your own API calls, none of which you store or index yourself, so your features can draw on what that specific customer has published. Site-less accounts work too. You provision an account with POST /api/v1/api_accounts at signup (idempotent on your own customer id) and DELETE it at churn; a signed webhook tells you when the first index is ready, or you poll. Each account also carries a short top_level_context paragraph describing who that customer is, useful for personalising a screen before anyone asks a question. With your provisioning token you can search across every customer's index at once. Pricing is $50/month per bundle of 10 accounts, bundles add automatically, no token or usage fees on top, 7-day free trial. It is not a chatbot widget and not MCP for your end customers; you build the UI. Start at context-link.ai/for/white-label-rag-api, then the docs at context-link.ai/docs/white-label, or a single raw file for your AI coding agent at context-link.ai/llm/white_label. An agent connected to the admin MCP can call plan_integration first and get your live account state followed by the whole guide.

A Memory is a named, canonical document that holds the latest version of something your business wants AI to reuse: a positioning statement, an ICP definition, a pricing rationale, a decision and its reasoning. AI or a human saves it once, then any AI tool connected to Context Link can retrieve it later. Updating a Memory replaces the old truth while keeping version history, so AI always reads the latest version.

Sources sync every 24 hours, so the index reflects what your business says today. Update a Notion page or Google Doc and the change flows through on the next sync. For knowledge that lives outside any doc, save it as a Memory and update it as things change: every connected AI reads the latest version.

Every answer cites the exact snippets it was built from, with links back to the source. Past lookups are reviewable in the responses log, so you can audit what AI retrieved and when, and anything you don't want indexed can be excluded with a click.

Context Link still fits. Connect your internal docs, Notion pages, Google Docs, and your website, then ask source-backed questions from inside ChatGPT, Claude, or any MCP-aware agent. Developers get an MCP server they can wire into Claude Code, Cursor, or any agent stack instead of building their own RAG pipeline. And Memories give the team a place to save settled decisions that every agent reads the latest version of.

Kapa.ai requires contacting sales for pricing, with third-party sources reporting median deals around $19,000/year. Context Link has published, SMB-friendly pricing for teams of 3-200, no sales call required, no enterprise minimums.

Context Link connects to email inboxes (Gmail, Outlook, Zoho, Fastmail, custom domains via IMAP), Basecamp, Monday.com, Google Drive, and uploaded file stacks (PDFs, Word docs, Markdown files). Kapa focuses on developer-oriented sources like GitHub repos, GitHub Issues, Stack Overflow, and Discourse forums. And for any tool not in the catalogue, the custom-connections skill lets your AI (Claude, Codex) fetch data from any service it can reach and push it into Context Link. If Claude can read it, you can push it to Context Link.

Yes. Context Link is model-agnostic on the asking side. The same connected sources and Memories are reachable from ChatGPT, Claude, Gemini, and any MCP-aware agent. Different team members can use different AI tools and still pull from the same knowledge base.

Yes. Context Link gives AI source-backed material to work from instead of guessing. Semantic search retrieves the most relevant snippets from your connected sources, and every answer cites where it came from. It does not eliminate hallucinations entirely, but it dramatically reduces them for company-specific topics.

Yes. Alongside the primary "get context on [topic]" workflow, Context Link has an Ask Question skill invoked as `/ask-question [question]` or natural language ("ask Context Link what …"). It runs the same semantic retrieval across your connected sources, then a small fast LLM composes one grounded paragraph with numbered citations back to the exact snippets used. The difference: instead of logging into another app to ask, you invoke it from inside the AI you already use: ChatGPT, Claude, Gemini, or any MCP-aware agent. Ask Question is a Pro feature and counts toward the monthly LLM allowance (2,000 requests/month on Pro, shared across LLM-powered features).