# AI-Powered SaaS Usage Review and Alerting Agent (`lavgroup/ai-saas-usage-review-alerting-agent-1786129`) Actor

Actor for SaaS usage log review analyzing sudden drops or spikes using simple statistical anomaly detection, with AI-generated commentary and alerts. Supports input as usage logs or API JSON fetch, outputs detailed anomaly reports for customer success and ops teams.

- **URL**: https://apify.com/lavgroup/ai-saas-usage-review-alerting-agent-1786129.md
- **Developed by:** [Taku Anan](https://apify.com/lavgroup) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $50.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## AI-Powered SaaS Usage Review and Alerting Agent

This Apify actor analyzes SaaS usage logs or API-provided JSON data to detect anomalies such as sudden drops or spikes in usage. It provides automated alerts with AI-generated commentary explaining the potential implications of detected anomalies.

### Features

- Accepts either user-uploaded usage logs or fetches usage data from a provided API endpoint.
- Performs simple anomaly detection based on usage value deviations.
- Generates human-friendly AI interpretation comments for each anomaly.
- Pushes structured results including anomaly details and explanations.

### Input

Provide input as JSON with one of the following fields:

- `usage_logs`: An array of objects each containing at least `timestamp` (string) and `usage` (numeric) or `value` (numeric).
- `api_endpoint`: URL string to fetch usage data JSON array.
- (Optional) `api_headers`: object with HTTP headers for the API request.

Example:

```json
{
  "usage_logs": [
    {"timestamp": "2024-05-01T00:00:00Z", "usage": 120},
    {"timestamp": "2024-05-02T00:00:00Z", "usage": 30},
    {"timestamp": "2024-05-03T00:00:00Z", "usage": 115}
  ]
}
```

### Output

Pushed to dataset is a JSON object with:

- `status`: "success" or "error"
- `total_points`: number of data points analyzed
- `anomalies_count`: number of anomalies detected
- `anomalies`: list of anomaly details, each including:
  - `data_point`: original data point of anomaly
  - `commentary`: AI-generated explanation

### Usage

Deploy to Apify and run with your input. You can schedule runs or trigger via webhooks as needed.

### Notes

- This Actor uses basic statistical anomaly detection suitable for straightforward usage metrics.
- For complex needs consider integrating with more advanced AI or ML anomaly detection models.

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `results` (type: `string`):

Results stored in the default dataset.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("lavgroup/ai-saas-usage-review-alerting-agent-1786129").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("lavgroup/ai-saas-usage-review-alerting-agent-1786129").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call lavgroup/ai-saas-usage-review-alerting-agent-1786129 --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,lavgroup/ai-saas-usage-review-alerting-agent-1786129"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/EcxkGYiqf2WC8TxLg/builds/dx4Ly0Z2bMvMGZisB/openapi.json