Wikora turns your technical references into a grounded AI assistant. Zero hallucinations. Exact citations. High developer trust.
To ensure idempotency, include an Idempotency-Key header in your POST request.
Searching for a single field in a 5,000-word reference page isn't work—it's a distraction. Developers need specific answers, not broad reading assignments.
Critical details are often buried in massive, auto-generated reference pages that browser search can't handle.
The authentication guide is in Notion, the API ref is on GitHub, and the SDK setup is in a PDF. Where do you look?
Developers waste hours debugging code based on v1.2 docs while the API is already running v2.0.
Searching for 'auth' returns 40 results. Which one is the actual implementation guide for JWTs?
API references show the schema but miss the context of how to actually chain multiple requests together.
Developers open L2 support tickets just to ask questions that are technically in the docs, but impossible to find.
doc_ops.Wikora sits on top of your current documentation stack. It doesn't replace your site; it makes it interactive.
Connect your Markdown, OpenAPI/Swagger specs, or synced folders. Wikora builds a vector index of your technical logic.
As your docs change, the engine updates. Developers only get answers relevant to the version they are currently implementing.
Developers ask technical questions like 'How do I handle webhook retries?' or 'What are the limits for the batch API?'
Wikora returns a synthesized answer sourced solely from your docs, complete with markdown code blocks and citations.
Unlike general-purpose LLMs, Wikora is restricted to your source documents. If a field isn't in your API reference, Wikora won't guess it exists. Every response is traceable back to a specific file or section.
Note: Answer quality scales with documentation coverage. Wikora identifies "unanswerable" questions so your DX team knows exactly where to write new content.
Developers don't need a chatbot that can write poetry. They need an engine that knows exactly which status code is returned when a rate limit is exceeded.
Generic AI models guess based on patterns in common APIs (like Stripe or AWS). When your custom API deviates from those patterns, the AI gives wrong, dangerous advice.
A developer's first instinct when an AI answers is to verify it. By providing direct links to the exact doc source, we eliminate the 'I hope this is right' anxiety.
Fuel the engine with these specific documentation types to provide the best developer experience.
?How do I refresh an expired OAuth token?
?What is the maximum payload size for bulk uploads?
?What does error code 42201 specifically mean?
?Can I use the Stripe integration with the test API?
?Where do I find my Client Secret in the dashboard?
?Is the /search endpoint case-sensitive by default?
?How many retries should I perform on a 503 error?
?How do I verify the HMAC signature of a webhook?
?What are the rate limits for the free trial tier?
?How do I restrict API keys by IP address?
?Does the pagination use cursors or offset/limit?
?Which endpoints return a 403 when SSO is required?
Identify documentation gaps by analyzing what developers are asking but the AI can't answer from current sources.
Stop answering repetitive implementation questions on Slack. Let the answer engine handle the L1 technical support.
Provide faster, more accurate ticket responses by using Wikora internally to find policy and technical details instantly.
questions.find(q => q.technical === true)
One wrong answer from an AI assistant can destroy developer trust permanently. We prioritize verifiability over conversation.
Every answer is backed by a source. If you can't verify it, don't trust it.
Follow the citation link directly to the source paragraph in your docs.
If your docs are silent on a topic, Wikora is silent too. No guesses.
grounded_answers = true; DX_boost = true;