kapa.ai

Announcing the BIGGEST kapa.ai launch of 2026: code as a data source. "Wait, how hard could this be?" Trust me... We've been trying to crack code ingestion for over 2 years. We tried so many times. 30+ customers (and 75% of our largest logos) maintain some form of public code, and specifically asked for it. Because combining docs and code in one RAG system is insanely hard. A single repo can have 10x more content than all your documentation combined. And you can't just dump everything in to one giant .MD file. For 2 years, models just weren't good enough. We tried. And tried. And tried again. But in the last few months, something clicked. Our research team saw the opening and absolutely cooked. We finally did it. What we built: code-aware chunking that parses your codebase down to every function, every class, every method definition - directly integrated into our agentic RAG pipeline that understands the structure of your entire codebase. And STILL returns a cited answer with time to first token in under 2.5 seconds. The results are wild. In our experiments we found that 50-80% of your users' questions can be answered from source code alone. Documentation tells you the "why." Code tells you the "what." Now your AI agent speaks both languages. For technical writing teams, this meaningfully changes your roadmap. If your AI can pull implementation details directly from code, your writers can stop documenting every function signature and edge case. They can shift to the high-leverage stuff humans are great at - tutorials, architecture guides, the "why" behind the code. In short, this is going to change a lot, for a lot of teams. We're rolling this out now. Link in comments.

Exciting news! We're sending Kapa's allstars to the World Cup of semiconductor conferences: Embedded World in Nuremberg. If you live and breathe datasheets, come find us. One of my favorite things about these industry-leading events is seeing our industry-leading customers. Silicon Labs, Nordic Semiconductor ASA, and Espressif Systems all power billions of IoT devices. They've got the most complex developer documentation on the planet. They all use kapa.ai to turn that documentation into an AI assistant their users love. We'll see them at Embedded World, and we hope to see you too. If you're one of the 32,000 engineers or 1,200 exhibitors at Embedded World, come find us at Hall 4A / Booth 4A-101. We'll show you how AI can handle even the most technical docs. Looking forward to seeing everyone there!

Ever wonder why Claude Code is so good? It has access to your codebase. It knows your project. It has context. Now every company is trying to build the same thing for their product. Companies like Grafana and Amplitude have built AI sidebars that can do things like create_dashboard(), run_cohort_analysis(), and more. But here's the thing. Just like Claude Code needs your codebase, these copilots need to know your product. And that knowledge? It lives scattered across your docs, API references, help centers, wikis, GitHub repos, and forums. Without it, your copilot can't reason. It just guesses. We've solved it with one simple call. POST /retrieval { "query": "what alert evaluation intervals are supported?" } That returns the most relevant chunks from your entire product knowledge base, ranked, with sources. Behind that one call: state of the art low latency multi step agentic retrieval with query decomposition and reranking. 50+ managed data source types, auto synced, always fresh. Optimized for product knowledge. Hardened across 200+ production deployments over 3 years. You don't see any of that. You just get the right chunks back, fast. Plug it into your copilot as an API call. Or use search_product_knowledge() as a hosted MCP tool call. Either way, your agent goes from generally smart to product expert. You build the copilot. We handle the knowledge layer. Link below to get started.

🔥 We just shipped 3 HUGE updates to the best AI agent Slack bot. Kapa 2.0 gets a massive upgrade. We literally rebuilt it from the ground up, and here's what's new: 1. Drop files directly into the chat. Logs, screenshots, RFPs, CSVs, whatever context you need. Over 100 file types supported, so your AI agent can see what you're seeing. 2. Tag @kapa.ai into ANY thread. Yes, any thread. See a question in Slack that Kapa could answer? Just tag it. It will read the full thread context and jump in. 3. Real-time streaming responses. Just like ChatGPT, we stream answers live. See the answer as it appears, not after 10 seconds of waiting. Sounds small, feels huge. Companies like Airbyte, and Mixpanel use Kapa's Slack bot to handle hundreds of community questions every week. Coralogix uses it internally to unblock support engineers and CSMs in real time. This is the biggest update we've ever shipped for it. Your team will notice the difference immediately. Use the link below to upgrade - it takes 30 seconds.

Docker, Nokia, Grafana, monday.com, Mapbox, n8n, OpenAI, and 200+ others use Kapa in production. One of the coolest things about building Kapa is how visible it is. These deployments live on real docs sites, in real products, inside IDEs, support portals, and community channels. So we put our favorites into one place: the new Kapa Showcase. It highlights how companies are using Kapa today: • embedded docs assistants • native in-product copilots • community bots • MCP-powered assistants for Claude and Cursor • support ticket deflectors • internal copilots for technical teams The range is honestly pretty wild, and it’s fun to see how different teams shape the experience. If you want inspiration for what you can build with Kapa, or you just like seeing how other companies design their AI surfaces, check it out. Ping me if we should add yours! Links in comment.

We may have accidentally emptied a LEGO store in Paris yesterday… and Day 2 of apidays Paris is just getting started. Yesterday was packed: great conversations, lots of kapa.ai demos, and yes… we somehow bought half the LEGO store for conference swag (Danish roots obligate us). We are back at 𝗕𝗼𝗼𝘁𝗵 𝗔𝟯𝟯 all day. If you are at the conference, swing by to try Kapa, chat with the team, or grab some LEGO before it disappears. At 12:20pm I am speaking in the AI the Docs track (Room Aubépine 4) about something I care a lot about: what your docs AI chat can tell you about your users, and how those patterns can directly improve your product, your documentation, and your roadmap. Come say hi to Jonne Frankena, Anton M. and I. 👋