# Software Review Scraper — One Clean Schema (PeerSpot) (`nelliel_future/software-review-intel`) Actor

B2B software reviews in ONE normalized format: reviewer role & company, rating, full text, structured Q\&A sections, ISO dates, stable IDs for dedupe.

- **URL**: https://apify.com/nelliel_future/software-review-intel.md
- **Developed by:** [Nelliel](https://apify.com/nelliel_future) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 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

## Software Review Scraper — One Clean Schema (PeerSpot)

Honestly, I built this because I got tired of the same problem.

You want to know what real users say about Jira vs. Asana vs. ClickUp. So you scrape a few review sites, and now you have three different JSON shapes, dates in three formats, reviewer info buried in different places, and a pile of half-structured text you have to clean before it's useful to anything — a dashboard, a spreadsheet, or an LLM.

This actor skips that step. Every record comes out in **one normalized format**, no matter what it was scraped from:

- reviewer **role** and **company**, split into their own fields
- **rating**, title, and full review text
- the **Q\&A sections** PeerSpot reviews are made of ("What is our primary use case?", "What is most valuable?", "What could be improved?") kept as structured question/answer pairs instead of mushed into one blob
- **ISO dates** (the real ones — I parse "14 May 2026", you don't have to)
- a **stable review ID**, so nightly runs dedupe for you
- a per-product **summary record** (aggregate rating, total review count) — diff that between scheduled runs and you get live rating-velocity tracking for free

### What it costs

$2.00 per 1,000 results. A typical competitive-intel run (your product + 4 competitors, ~80 reviews each) lands around a dollar. Free-plan users can test it for pennies, which is how it should work.

### Usage

```json
{
  "products": ["Jira", "Asana", "https://www.peerspot.com/products/clickup-reviews"],
  "maxReviews": 100,
  "maxPagesPerProduct": 20,
  "includeQnASections": true
}
```

Plain product names are fine — they're resolved against PeerSpot's full catalog (23,000+ products). Slugs and full product URLs also work. If a product can't be resolved or a page fails, you get an honest `product_error` record telling you which one, not a silent gap.

### Example output

```json
{
  "record_type": "review",
  "review_id": "c9d10ee3f1293f98",
  "source_platform": "peerspot",
  "product": { "name": "Jira", "slug": "jira", "vendor": "Atlassian" },
  "reviewer": { "role": "SDE 2", "company": "Virtusa" },
  "rating": 4.5,
  "title": "Centralized sprint boards have transformed our planning...",
  "sections": [
    { "question": "What is our primary use case?", "answer": "My main use case for Jira involves..." },
    { "question": "What is most valuable?", "answer": "..." }
  ],
  "date": "2026-05-14",
  "source_url": "https://www.peerspot.com/products/jira-reviews?page=1",
  "scraped_at": "2026-10-24T21:36:16+00:00"
}
```

The full schema is published (`schema.json`) — validation errors on my side, not yours.

### Who this tends to be for

- **Competitive intel** — one scheduled weekly run over your product + rivals; watch aggregate-rating deltas and mine new reviews for pain points people mention about *your* category.
- **LLM / AI-agent data** — the Q\&A sections are already segmented prompt-shaped text; this is what they're good for.
- **Sales** — pull a competitor's 3-star-and-below reviews and you have an objection list that writes itself.

### Straight talk about limitations

- v1 covers **PeerSpot**. G2, Capterra, and GetApp block datacenter traffic hard; they're on the roadmap behind this same output format, not promised this week.
- PeerSpot review volume is thinner than G2 for long-tail products — you'll get 5 reviews for some, 300 for the big names. `product_error` and `review_count_total` tell you what you're working with.
- Public review pages only, pacing built in, in line with the site's robots.txt. Nothing behind logins, nothing personal beyond what reviewers publish themselves.

Questions or a product that won't resolve? Open an issue or message me — I actually fix things fast on this one.

# Actor input Schema

## `products` (type: `array`):

e.g. \["Jira", "Asana", "https://www.peerspot.com/products/clickup-reviews"]. Free-text names are matched against the full Peerspot catalog (23k+ products).

## `maxReviews` (type: `integer`):

Hard cap on review records for the whole run.

## `maxPagesPerProduct` (type: `integer`):

Peerspot shows ~13 reviews per page. 20 pages ≈ 260 reviews/product.

## `minRating` (type: `integer`):

Optional filter. Keep only reviews rated at or above this (1-5). Leave blank for no lower bound.

## `maxRating` (type: `integer`):

Optional filter. Keep only reviews rated at or below this (1-5). Useful for mining competitor complaints. Leave blank for no upper bound.

## `includeQnASections` (type: `boolean`):

Long-form Peerspot reviews answer platform Q\&A prompts; kept as question/answer pairs — ideal for LLM ingestion. Turn off for lean sentiment datasets.

## Actor input object example

```json
{
  "products": [
    "Jira",
    "Asana",
    "Monday.com"
  ],
  "maxReviews": 100,
  "maxPagesPerProduct": 20,
  "includeQnASections": true
}
```

# Actor output Schema

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

Dataset items. Each item has record_type: review | product_summary | product_error. Review items: review_id (stable sha1 id, safe for dedupe across runs), source_platform, product {name, slug, url, vendor}, reviewer {name, role, company, verified}, rating (1-5), title, body, sections \[{question, answer}] (pre-segmented Q\&A), date (YYYY-MM-DD), source_url, scraped_at (ISO 8601). product_summary items: product {name, slug, url, vendor}, rating_value (average), review_count_total (diff between scheduled runs = rating velocity).

# 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 = {
    "products": [
        "Jira",
        "Asana",
        "Monday.com"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("nelliel_future/software-review-intel").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 = { "products": [
        "Jira",
        "Asana",
        "Monday.com",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("nelliel_future/software-review-intel").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 '{
  "products": [
    "Jira",
    "Asana",
    "Monday.com"
  ]
}' |
apify call nelliel_future/software-review-intel --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,nelliel_future/software-review-intel"
        }
    }
}
```

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/YKe2hkNAlpzn9YcjW/builds/wXnhh79R2LzaK16pz/openapi.json