Close the blind spot on every
Product managers launch AI features and then face a blind spot: no idea how the feature is used, what it costs, or whether it is working well. Igris closes that gap with connection-level analytics and quality guardrails for every AI feature, so you see usage, cost, and model drift, and test upgrades without making live users the experiment.
AI Feature Usage Analytics
You shipped the AI feature. Adoption looks reasonable based on the usage events in your product analytics platform. But what is happening inside the feature, which models it is calling, how often it fails, what it costs to serve, is invisible. If the error rate is climbing, you will not know until users start complaining or engineering notices in their own monitoring. If the feature quietly switched to a more expensive model after a library upgrade, there is no signal. You shipped it and then lost sight of it. This is the AI feature blind spot most product teams do not realize they have until something goes wrong.
Igris provides a connection level view for every AI feature: request volume, error rate, latency, and cost over the last 24 hours, plus a model breakdown showing exactly where traffic is going and whether it is drifting. Trend data in 12 hour buckets reveals peak usage times and whether adoption is growing. The audit events API supports deeper analysis by connection and time range for reporting beyond the dashboard view.
The blind spot closes. You see how the feature is actually being used, which models, what volume, what error rate, what cost, without needing an engineering rundown every time you want a straight answer about something you shipped.
Feature Cost Effectiveness
The feature is live and gets used. Whether it earns its infrastructure cost is a question you cannot currently answer with precision. You know the broad LLM spend on the invoice. You do not know how much belongs to this feature specifically, which model is driving most of it, or whether the average prompt size has crept up since launch. When leadership asks whether the feature justifies continued investment, the honest answer is that you are working from estimates, and estimates do not hold up well in that conversation.
Igris shows exact LLM spend for each feature through its dedicated connection, broken down by model so expensive options being used unnecessarily are immediately visible. Average token usage per request reveals whether prompts have grown beyond their original design. Cross reference with business metrics from your product analytics platform, activation rate, retention impact, usage frequency, and you have a defensible cost per outcome calculation that does not rely on rough extrapolation.
When the investment conversation comes up, you have a real number: what the feature costs, what is driving that cost, and whether it tracks with the value it delivers. That is a different conversation than one built on invoice estimates and gut feel.
Model Quality Guardrails
You tested the feature on a specific model. You validated output quality. You shipped with confidence. Then a library upgrade happens, a provider silently updates a model version, or someone changes a configuration in a shared environment, and the feature is suddenly running on something different. User experience degrades. You find out through support tickets, not a monitoring alert. Tracing it back to a model change takes longer than it should, and by then users have already formed an opinion about the feature.
Allowed model lists on each connection guarantee that only models approved for quality are ever used. Model shift detection raises an alert the moment the underlying model changes unexpectedly, before users feel the difference. The policy engine denies any model not on the approved list, regardless of what a library upgrade or environment change attempts to route through.
The model your users experience is the one you tested and approved. Changes do not slip through silently. And when something does shift, intentionally or not, you hear about it before your support queue does.
Safe Model Upgrade Testing
A new model version drops. Early benchmarks look promising, faster, cheaper, potentially more accurate. You want to know if it is better for your specific use case. But switching the live feature to find out means your users become the test environment. If the new model behaves differently on your actual prompts, you will learn that through degraded experience and rising support volume, not through a controlled signal. There is no clean way to evaluate in production without putting the live experience at risk.
Create a separate connection in Igris pointed at the new model version. Route evaluation traffic to it, or run parallel prompts against both connections, while the live feature continues running on the approved model unchanged. Compare error rate, latency, cost, and output behavior across both connections in the same dashboard. When you are confident in the new version, update the allowed model list on the live connection. If it underperforms, the live experience was never affected.
Model upgrades become a controlled evaluation, not a bet on live traffic. You validate the new version against your actual use case before a single production user sees it, and you have comparative data to back the decision either way.
Response Safety and Brand Guardrails
The feature runs on a model you selected, but the model does not know your brand guidelines, your legal constraints, or what an acceptable response looks like in your product context. Edge cases surface, an unusual prompt produces a response that is off brand, legally ambiguous, or contains something it should not. You find out when someone screenshots it. By then the response already reached the user, and you are managing the fallout rather than the prevention. There is no layer between the model output and the customer that catches the problem before it lands.
Content guards on the feature's connection scan LLM responses before they return to the user, flagging or blocking outputs that contain leaked credentials, internal information, or content that crosses defined lines. Policy rules enforce boundaries specific to your product and brand, not just the model's defaults. Every enforcement event is logged, so you can see patterns at the edge of the guardrails and feed that signal back into prompt or policy refinement over time.
What users see is what you have approved. The guardrails sit at the infrastructure level, not in the model, not in the prompt, not in the assumption that edge cases will not surface in production. They run on every response, automatically, before anything reaches your users.
Debugging AI Feature Quality Without Waiting for Engineering
A user reports a wrong, confusing, or harmful output from the AI feature. To understand what happened, you need the actual prompt and response from that session. To get it today, you open a ticket, write up the context, wait for an engineer to have capacity, and receive an answer days later, if the logs still exist and have not been rotated out. By then the user has moved on, the thread is cold, and you still cannot tell whether it was a one off or a pattern worth addressing before the next sprint.
When full content logging is enabled on the feature's connection, Igris retains encrypted request and response bodies for a configurable window, 30 days by default. Query the audit events API by connection, actor, or time range to pull the specific session. Inspect the actual prompt and response directly. No ticket. No dependency. No waiting. Every log is encrypted at rest, retained only within the defined window, and permanently deleted when it expires.
Quality investigations happen the same day, not the same sprint. You see exactly what the user sent and exactly what the model returned, and you can determine whether it is a prompt problem, a model edge case, or something worth escalating, with evidence rather than a reproduction attempt.
See what your AI feature is really doing
See how product teams track feature usage and cost, lock quality with model guardrails, and validate model upgrades before users notice.