Real-time analytics for your AI software with Phospho

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Things are moving a lot quicker now in the SaaS industry after the rise of AI and this has created just as many challenges as opportunities.

In the competitive market of AI SaaS, data is the new gold. Understanding your users is what underpins more effective iteration of your product.

For an AI SaaS there is currently no better way to understand your users than to deploy real-time analytics. Think about it, what better feedback is there than verbatim in-app conversations between your users and your AI?

We’re now left with just one question: how do we get real-time analytics for our AI software?

We need more advancesd AI analytics tools like Phospho.

You can try for free by signing up here.

How Phospho Revolutionizes Real-Time Analytics for LLM Apps

Phospho is an open source analytics platform you can plug into your tech stack for real-time insights about your users as they engage with your LLM app.

Why does this matter? Because user interactions with your AI are a rich source of data we should be collecting and leveraging for better iteration cycles. But monitoring user interactions also gives visibility into any patterns and potential issues as they happen. This allows teams to identify and rectify them on the fly or before they become more problematic.

The fact that Phospho is specifically built for LLMs positions it well to address the common challenges you would face with traditional tools such as context tracking, semantic understanding, and actually extracting actionable insights from unstructured conversational data.

We’ll quickly show you how it works.

Real-Time Monitoring Capabilities: Tracking and Logging User Inputs

Phospho goes a lot further than simply logging and monitoring user inputs within your AI software once it’s integrated.

As mentioned, if we’re collecting all our user inputs and LLM outputs, we can use Phospho analytics and visualisations to spot emerging trends or issues to quickly address them.

But we can also use Phospho to foster more collaboration and proactive optimisation.

Teams can annotate conversations in real time and flag or attach feedback to specific interactions to prompt further investigation.

You can also attach user feedback to specific interactions in the logs as well to provide more contextual analysis for better understanding towards how you can optimise the AI model’s outputs.

Tip: Once Phospho is up an running you can do this quite easily without any coding know-how, that way it’s not restricted to developers and more collaboration is possible. To try you can sign up to Phospho for free here.

Automated Insights Extraction and Custom KPI Detection

One of the pains of traditional analytics is the manual component to it. With Phospho you can automate your insights extraction by creating custom KPIs and events.

These bespoke metrics can be used when monitoring user interactions in real-time by flagging anything that meets those requirements whether they’re specific conversations or patterns in app-wide usage.

With each interaction that’s flagged, they can automatically be classified as ‘successful’ or ‘unsuccessful’ based on your own pre-defined criteria to enable more rapid insights extraction. This is particularly useful when you have high volumes of user interactions and/or you are trying to detect edge cases.

For an interesting deep dive on this, read our example walkthrough of Phospho in our previous article here. Or you can try Phospho out yourself by signing up for free here.

Continuous Evaluation Pipeline for Ongoing AI Model Optimization

User needs will always be evolving, and new pain points or bottlenecks will emerge - that’s a given. So it’s imperative that AI SaaS teams continuously evaluate their product’s performance.

With Phospho’s continuous evaluation pipeline you can automate that process. Phospho can monitor and assess the performance of your AI SaaS against any number of custom KPIs and metrics to ensure your AI model’s performance is aligned with real-time user needs.

With this running continuously you get a steady feed of real-time insights that help understand what specific updates or optimisations need to take place on your AI model.

Bonus: Phospho also provides automated A/B testing of different versions of your AI SaaS across multiple KPIs, so teams can qualitatively compare different iterations and whether performance has actually improved where they want it to.

These insights and automated evaluations make for effective iteration cycles that are both faster and more informed by real-time user understanding.

For a practical walkthrough of using Phospho and seeing what it can do, read this article here.

Seamless Integration with Popular Tools and Languages

Connect Phospho either to your AI SaaS with our API for real-time capability (see docs), or by uploading any data files to our platform to perform analysis on historical data, see how here.

If you already have a workflow for performing analytics, we’ve kept it very lean and simple for those use cases as well. Phospho can fit into existing data pipelines easily to add extra capability and insights to any current analytics workflows.

However, if you prefer a very lean stack for analytics, for AI software Phospho is the most comprehensive platform for collecting, analysing, and visualising data. This helps to reduce the need for multiple apps and tools each with its own learning curve. Sign up here to use Phospho for free.

if you’re overwhelmed with the options of tools at your disposal, you can read our previous articles where we draw comparisons between them here and here.

Conclusion: Empowering AI Teams with Phospho’s Real-Time Analytics

We all understand the value of a user centric approach to AI development, and by now, how Phospho ’s analytics can enable you do that. But there’s another factor thats growing increasingly important in determining the success of an AI SaaS… Velocity.

Speed is becoming more of a differentiating factor in the AI market. And by speed we mean the rate at which you can iterate WITH informed decisions.

That’s why real-time insights are necessary today - traditional analytics tools simply won’t cut it. You need real-time analytics tools like Phospho which is specifically designed for optimising LLM apps.

If you want to leverage real time analytics for rapid user centric iteration, try using Phospho for free by signing up here. We have plenty of documentation to support you.

Tip: if at any point you need specific questions answered for your setup process, you can converse with our documentation page with the search bar - it uses our own AI model, Tak.