Ship Observable AI Features
You built a great product. Now you are adding AI. But shipping AI without observability is shipping blind. Add drop-in tracing to your product's AI in an afternoon with five lines of code and full visibility into every response.
Your AI feature is live but you are flying blind
You shipped an AI feature in your product. Maybe it answers user questions, generates summaries, or helps with onboarding. It works in demo. Your users are asking it real questions now.
And you have no idea what it is actually saying. Is it hallucinating? Is it citing the right sources? Is it telling users about features you deprecated last month? Without tracing, you only find out when a user complains, or worse, when they quietly leave.
As a solo developer or small team, building observability from scratch means setting up logging infrastructure, writing custom dashboards, instrumenting every pipeline step, and still not having behavioral verification. That is weeks of work that does not ship features.
Building your own tracing is tempting. You could wire up structured logging, build a trace viewer, add latency tracking, create validator hooks. Realistically, that is weeks of work and an ongoing maintenance burden. And you still need to solve the hallucination detection problem. Your AI feature is worse than useless if it confidently tells users about an API endpoint that does not exist and you have no way to know.
Five lines to full observability
Trainly handles all of the infrastructure. You connect your pipeline, get an API key, and integrate the SDK. Here is what it looks like:
That is it. The client sends the question to Trainly, which traces every step of your pipeline: prompt, completion, and validation. Every response is verified before it reaches your user, and every span is queryable in your dashboard.
If you want a drop-in React component, the React SDK gives you a pre-built chat interface that handles streaming, citations, and full tracing out of the box. Import it, pass your API key, and your product has an observable AI feature.
What you get without building
Here is what Trainly handles for you, so you do not have to build it yourself:
Build it yourself vs. use Trainly
Here is a realistic comparison of what it takes to build observable AI features yourself vs. using Trainly:
The build-yourself route makes sense if AI observability is your core product and you need full control over every component. For everyone else, Trainly lets you ship observable AI features in hours and get back to building the product your users are actually paying for.
What your users experience
A developer using your product asks: “How do I set up webhook retries for failed deliveries?”
To configure webhook retries, set the retry_policy field in your webhook configuration. The default policy retries failed deliveries 3 times with exponential backoff (1s, 4s, 16s). You can customize the retry count and backoff multiplier:
The user gets a complete, accurate answer with a code example pulled from your actual documentation. The citation links to the specific section so they can read the full context. Behind the scenes, you see the full trace: prompt span, completion span, validation span. If a response can't be verified against traced inputs, it flags it instead of guessing. Your users trust the AI because it is always right, and you trust it because you can see why.