AI
AI-assisted extraction
Use page context to help identify, classify and map relevant content into your agreed data schema.
AI-assisted data extraction
AI-powered web scraping services for intelligent data extraction
Collect, extract, clean and structure public web data using AI-assisted scraping workflows built around your business requirements.
Built for your use case: one-time datasets, recurring monitoring and structured feeds in the format your team needs.
Your data pipeline
01Product pagesprices · SKUs · stock
02Public listingsdetails · ratings · offers
03Market sourcestables · metadata · signals
extract + validate
Structured output
{
"product": "…",
"price": 129.00,
"availability": true,
"captured_at": "…"
}CSVJSONAPI
From source to decision-ready data
- Custom field mapping
- Quality checks
- Flexible delivery
- Ongoing monitoring
Capabilities
Intelligent web data extraction built around your business
Conventional scrapers rely on fixed rules. AI-assisted workflows can help interpret page context, map content to defined fields and flag inconsistencies—while purpose-built extraction logic keeps the process controlled and testable.
JS
Dynamic website collection
Scope workflows for public content rendered across modern websites, listings and multi-page catalogues.
QA
Cleaning & validation
Normalize formats, remove duplicates and apply field-level checks before the data reaches your team.
↻
Scheduled monitoring
Plan recurring runs and source-change monitoring where ongoing price, listing or market data is required.
Data we extract
Extract structured data from websites at scale
Every project starts with a field map: what your team needs, where it is publicly available, how often it should be refreshed and how it will be used.
Share your target fields →01
Product & commerce dataNames, prices, SKUs, specifications, stock and promotions
↗02Ratings & public reviewsReview text, counts, dates, scores and visible themes
↗03Listings & market dataBusiness, travel, property, finance and marketplace records
↓04Articles, tables & metadataHeadlines, attributes, structured tables and relevant public content
↓Our process
From business question to dependable dataset
You receive a defined field schema, agreed delivery format and documented scope—not an unexplained dump of raw pages.
- 01
Define the objective
Align on target sources, required fields, approximate volume, refresh needs and intended use.
- 02
Assess feasibility
Review public availability, source behavior, project constraints and the route to the required records.
- 03
Configure extraction
Build the collection workflow and map page content into a consistent, agreed structure.
- 04
Clean & validate
Normalize values, remove duplicates and check records against the project’s quality rules.
- 05
Deliver & monitor
Provide the dataset through the agreed channel and maintain recurring jobs where required.
Industry solutions
AI web scraping for the signals your market moves on
Explore a focused solution for your industry. Each workflow is scoped around the sources, fields and decisions that matter to that market.
Commerce
eCommerce & retail
Monitor public product, competitor price, promotion, assortment and availability data.
Explore eCommerce scraping →TravelTravel & hospitality
Structure public rates, routes, listings, availability and guest feedback.
Explore solution →PropertyReal estate
Collect public property listings, attributes, prices and location-facing market data.
Explore solution →FinanceFinancial data
Scope permitted collection of public financial, pricing and market information.
Explore solution →FoodFood delivery
Track public menus, prices, availability, restaurant listings and offers.
Explore solution →ResearchMarket & media research
Organize public articles, metadata and competitor signals for structured analysis.
Explore solution →Delivery & integration
Get web data in the format your business needs
Choose a delivery approach that works for your analysts, applications or existing data stack. Available methods are confirmed during scoping.
REST APICSVExcelJSONDatabaseCloud storage
01
One-time datasetPrepared for a defined research, migration or analysis need.
02
Scheduled exportsRecurring files or feeds delivered at an agreed frequency.
03
System-ready integrationStructured data planned for dashboards, databases or applications.
Why CigmaSols
A custom web scraping solution, not a generic data dump
The workflow is designed around your target fields, business rules and delivery needs, with scope and limitations made clear before collection begins.
01
Custom workflows
Source and field mapping based on the question you need the data to answer.
02
Quality controls
Validation, normalization and duplicate handling defined for the project.
03
Scalable collection
Architecture planned around source volume, refresh frequency and delivery needs.
04
Flexible handoff
Outputs prepared for the people and systems that will use the data.
05
Maintenance
Recurring workflows can be reviewed as sources and requirements change.
06
Responsible collection
Projects focus on public information and consider privacy, legal and source constraints.
Illustrative example
See what a structured dataset can look like
This demonstration shows how public eCommerce fields could be organized for competitor price monitoring. It is illustrative—not a client result or live dataset.
- Business question
- How are selected public prices and availability changing?
- Example sources
- Retailer and marketplace product pages
- Delivery
- Scheduled CSV or JSON feed
DEMONSTRATION DATA
| product_id | price | stock | captured |
|---|---|---|---|
| SKU-1042 | ₹2,499 | Available | 09:00 |
| SKU-2158 | ₹1,899 | Limited | 09:00 |
| SKU-3301 | ₹3,249 | Available | 09:00 |
● Field validation● Duplicate check● Normalized currency
Planning resources
Explore the questions behind a stronger data project
These focused guides help clarify the extraction approach, fields and business use case before you request a scope.
Guide 01
View all industry solutionsAI-powered vs. fixed-rule extraction
See where AI assistance can add value—and where controlled extraction logic still matters.
Read the guide →Guide 02eCommerce competitor price monitoring
Explore the products, prices, promotions and availability fields a dataset can include.
Open the eCommerce guide →Guide 03Web data for market research
Start with the decision, then define sources, fields, validation and delivery.
Review the process →FAQ
Frequently asked questions about AI-powered web scraping
A practical starting point for scope, delivery, quality and responsible public-data collection.
What is AI-powered web scraping?+
It combines purpose-built collection logic with AI-assisted field mapping, classification or validation to turn publicly available website content into structured data.
Which websites can you collect data from?+
We assess each public source against the requested fields, volume, update frequency and project constraints before confirming feasibility.
How is a web scraping project priced?+
Pricing depends on source complexity, fields and record volume, refresh frequency, validation needs, delivery method and maintenance.
How fresh can the data be?+
Refresh frequency is agreed during scoping and depends on the business need, source behavior, data volume and project constraints.
What happens when a website changes?+
Recurring workflows can include monitoring and validation rules. Extraction logic can then be reviewed and adapted to the updated source.
How do you check data quality?+
Checks can include required-field validation, format normalization, duplicate handling, anomaly flags and sample review.
Which delivery formats are available?+
Projects can be scoped for CSV, Excel, JSON, API, database, cloud storage or scheduled feed delivery, as applicable.
Is public web data collection compliant?+
Each project should be assessed individually. We focus on publicly available information and consider applicable laws, privacy requirements, contractual restrictions and website terms.