Launching today
Linden
Validate AI Outputs Before They Reach Your Application
14 followers
Validate AI Outputs Before They Reach Your Application
14 followers
LLMs can generate malformed JSON, missing fields, schema violations, and inconsistent outputs that break production applications. Linden is an AI reliability layer that validates structured AI responses before they reach your system. Define schemas and business rules, then receive clear validation decisions: ALLOW, WARN, REGENERATE, or BLOCK. Integrate in minutes using our API and Python SDK.
Love the ALLOW/WARN/REGENERATE/BLOCK decision model, super practical. One thing that would make this way more useful for us is built-in support for streaming responses, since most of our LLMs stream output and we currently have to buffer everything before validation kicks in. Even partial validation per token chunk would be a huge win.
@tlinzheb Thanks, Tülin! Really appreciate the feedback. Streaming validation is definitely an interesting challenge, especially as more AI applications move toward real-time agents and user-facing experiences.
Right now, Linden focuses on validating complete structured outputs, but validating partial chunks as they stream is a great direction for reducing latency and catching issues earlier.
I’m curious — what kind of streaming workflows are you building with LLMs today (chatbots, agents, extraction pipelines, something else)? And if Linden supported streaming validation, what would be the most important behavior for your use case?
The Python SDK made it super easy to plug into my existing pipeline, and getting back clear ALLOW or BLOCK decisions instead of just error logs is genuinely useful. Wish I'd had this a few months ago when I was debugging a nightmare of malformed outputs from a smaller model.
@mcahitm1z5 Thanks, Mücahit! This is exactly the problem we’re trying to address — AI failures are often not just about detecting errors, but giving teams clear decisions they can act on. Glad the ALLOW/BLOCK approach was useful for your workflow.
I’m curious, when you were dealing with those malformed outputs, was this something you were handling regularly in production or more of a one-off debugging challenge? If you were using a reliability layer like Linden long term, what features would make it valuable enough to integrate into your stack?
Took it for a spin on some flaky outputs from our local LLM and the WARN and REGENERATE hooks made it way easier to debug. Setup was honestly under ten minutes with the Python SDK.
@smet0b2o Thanks, İsmet! Glad to hear the WARN and REGENERATE flows helped with debugging flaky outputs — that’s exactly the problem we’re trying to solve.
I’d love to learn more about your use case. Are you currently using this kind of validation layer in a production workflow, or was this more exploratory testing? Also, if Linden became something you relied on regularly, what would you need it to have for it to be valuable enough to pay for?