WEB
Websites & Marketplaces
Public pages, listings, catalogs, search results, profiles, and dynamic web experiences.
- JavaScript-rendered pages
- Category and detail pages
- Location-sensitive content
Home / Services / Custom Data Extraction
Tailored data pipelinesCustom Data Extraction Services Built Around Your Business
Turn complex websites, documents, applications, APIs, and fragmented sources into structured data designed for your systems, analytics, and decisions.
✓ Custom field schema✓ Multi-source extraction✓ Quality validated✓ Flexible delivery
Tailored ArchitectureDesigned for your sources
Custom SchemaFields mapped to your use case
Multi-SourceOne normalized data model
Quality ValidationChecks before delivery
Flexible DeliveryFiles, API, cloud, or database
The service, explained
What are custom data extraction services?
Custom data extraction services collect specific information from one or more sources, apply client-defined field and business rules, validate and normalize the records, and deliver the resulting dataset in a usable format.
Instead of forcing your requirement into a generic template, Kvetoiq designs the collection pipeline around the source, schema, update frequency, validation rules, and destination your team actually needs.
Explore Supported Sources →From raw information to usable records
Your source structure should not dictate how your team works. We translate fragmented information into a consistent data model built for the decisions, applications, and workflows it must support.
When standard tools fall short
Built for data requirements that do not fit a template.
Custom pipelines solve the source, structure, and quality challenges that prebuilt extractors leave with your team.
01Niche or specialized sources
02Inconsistent page structures
03Complex field requirements
04Multi-step navigation
05Documents combined with web data
06Cross-source record matching
07Localized or dynamic content
08Sources that change over time
Source coverage
Bring complex information into one structured workflow.
Kvetoiq evaluates each source, access pattern, structure, and intended use before designing the extraction approach.
PDF
Documents & PDFs
Reports, filings, brochures, statements, specifications, tables, and searchable documents.
- Text and table extraction
- Metadata capture
- Document classification
APP
Web & Mobile Applications
Eligible public application data requiring sessions, rendering, navigation, or mobile context.
- Stateful journeys
- Mobile rendering
- Application-specific fields
API
APIs & Data Feeds
Combine accessible endpoints and feeds with web or document sources in one output.
- Endpoint integration
- Response transformation
- Schema unification
FILE
Spreadsheets & Flat Files
Normalize CSV, Excel, XML, JSON, and other exports into consistent records.
- Column mapping
- Format standardization
- Duplicate handling
OCR
Images & Scanned Material
Use OCR-assisted processing where information is embedded in eligible visual sources.
- Text recognition
- Field identification
- Human review options
DIR
Directories & Public Portals
Structure records from business directories, registries, institutional portals, and public databases.
- Profile records
- Location information
- Public record metadata
DB
Database & Legacy Exports
Map approved source exports into a modern schema for migration, enrichment, or analytics.
- Field transformation
- Data cleaning
- Record reconciliation
MULTI
Multi-Source Datasets
Combine and reconcile records from different source types into one shared data model.
- Entity resolution
- Cross-source matching
- Unified identifiers
Business data coverage
Extract the fields your decisions depend on.
Every category is scoped around your exact fields, source context, update needs, and validation rules.
↕
Product & Pricing Data
Titles, descriptions, variants, sellers, offers, availability, promotions, and historical changes.
Explore price monitoring →★
Reviews & Content
Ratings, reviews, media, listing content, attributes, and customer-feedback signals.
Explore digital shelf →⌂
Property & Location Data
Listings, property attributes, agents, locations, amenities, and market records.
Explore real estate →✈
Travel & Hospitality
Properties, routes, availability, amenities, reviews, and destination information.
Explore travel data →MENU
Restaurant & Menu Data
Menus, modifiers, locations, availability, ratings, delivery details, and promotions.
Explore food data →FIN
Financial & Legal Signals
Public filings, market information, notices, disclosures, and business-event data.
Explore finance and legal →+
Healthcare & Research
Eligible public provider, study, publication, product, trial, and directory information.
Explore healthcare →AUTO
Automotive & Mobility
Vehicles, specifications, listings, parts, dealerships, availability, and mobility records.
Explore automotive →Custom schema design
Your business model becomes the data model.
Define the fields, types, relationships, rules, and metadata needed downstream. Kvetoiq maps source-specific labels into a stable schema your systems can rely on.
Field definitionsNames, types, and relationships
Required rulesCompleteness and exception logic
Derived valuesCalculated and enriched fields
Data lineageSource, timestamp, and status
Raw source labelNormalized schema
Item Name
→
product_title : string
Current Offer
→
current_price : number
Availability
→
stock_status : enum
Review Score
→
rating : number
Last Updated
→
collected_at : datetime
Extraction and transformation
More than collection: structure, reconcile, and enrich.
Build usable datasets from sources that differ in layout, terminology, format, and record quality.
JS
Dynamic Page Rendering
Process content that appears through JavaScript, interactions, sessions, or asynchronous loading.
- Browser rendering
- Wait conditions
- Interaction-aware collection
DOC
Document Parsing
Identify and structure eligible text, tables, fields, and metadata from business documents.
- PDF parsing
- Table extraction
- Document metadata
AI
AI-Assisted Field Detection
Use adaptable extraction methods when relevant fields appear across varying layouts.
- Field identification
- Content classification
- Semantic mapping
MATCH
Entity & Product Matching
Connect records representing the same product, company, place, or entity across sources.
- Attribute comparison
- Match confidence
- Shared identifiers
CLEAN
Cleaning & Deduplication
Standardize values and resolve repeated or conflicting records before delivery.
- Duplicate detection
- Value normalization
- Exception handling
CHANGE
Change Detection
Identify relevant updates between collection cycles instead of treating every record as new.
- Field-level comparisons
- Change timestamps
- Update events
Multi-source normalization
One consistent structure across different sources.
Different websites and files rarely use the same labels, formats, units, or identifiers. Kvetoiq maps them into a shared model so downstream teams do not have to.
Explore Product Matching →Source fieldNormalized field
Item Name / Product / Listingproduct_title
Offer / Sale Price / Nowcurrent_price
Available / In Stock / Statusstock_status
Vendor / Merchant / Sold Byseller_name
Stars / Score / Ratingrating_value
Updated / Captured / Observedcollected_at
Quality assuranceSchema validation Required-field checks Duplicate detection Anomaly signals Record-level status Source metadata Exception reporting Human review options
Know what is complete, valid, and ready to use.
Quality rules are designed around the schema and business impact of each field-not applied as a generic final check.
Confirm fields, types, structures, and allowed values.
Flag missing business-critical information.
Identify repeated records and conflicting identifiers.
Surface values outside expected patterns or ranges.
Preserve validation outcomes for downstream handling.
Retain URLs, timestamps, and collection context.
Separate warnings, failures, and records needing review.
Add targeted review where source ambiguity requires judgment.
How it works01 - DISCOVER 02 - MAP 03 - PROVE 04 - BUILD 05 - VALIDATE 06 - OPERATE
From a data requirement to a maintained production pipeline.
Define sources and goals
Clarify where the information lives, who will use it, and which decisions it must support.
Design the schema
Document fields, relationships, formats, metadata, and quality rules.
Prepare a representative sample
Validate field coverage, structure, source behavior, and output expectations.
Engineer the extraction pipeline
Implement collection, transformation, normalization, and exception handling.
Check data quality
Run schema, completeness, type, duplicate, and business-rule validations.
Deliver and maintain
Send data to the agreed destination and monitor relevant source changes.
Delivery and integration
Deliver the data where your team already works.
Choose the format, destination, schedule, and completion pattern that fits your existing architecture.
CSVExcelJSONXMLREST APIWebhookAmazon S3Google CloudAzureSnowflakeBigQuerySQL Database
Business use cases
Custom data designed for the outcome-not just the source.
CI
Competitive Intelligence
Track relevant changes in offers, products, positioning, content, and market activity.
Explore digital shelf analytics →PM
Product & Catalog Intelligence
Build unified catalogs, identify equivalent products, and compare attributes across sources.
Explore product matching →AI
AI & RAG Data Pipelines
Prepare current, structured, traceable information for retrieval and model workflows.
Explore AI training data →LOC
Listing & Location Aggregation
Combine property, business, service, and geographic listings into a consistent database.
Explore real estate and local →SEO
Search & Visibility Monitoring
Structure rankings, categories, search features, and visibility signals across markets.
Explore share of search →REG
Regulatory & Market Monitoring
Collect eligible public filings, updates, notices, and business events into alert-ready records.
Explore finance and legal →Custom versus standard
A pipeline shaped around your requirement.
RequirementStandard extraction toolCustom Kvetoiq pipeline
Unique sourcesLimited to available templatesSource-specific architecture
Custom fieldsBasic selectors and exportsDefined business schema and rules
Multi-source matchingUsually handled downstreamBuilt into normalization
Quality validationGeneric or limited checksSchema and business-rule validation
Source changesYour team maintains the setupManaged monitoring and maintenance
DeliveryFixed export optionsFiles, APIs, cloud, and databases
Responsible data collection
Custom architecture, scoped with care.
Kvetoiq evaluates source availability, legitimate purpose, proportionality, intended use, access considerations, and data-handling requirements before production collection.
✓Public-source assessment
✓Controlled collection behavior
✓Defined data handling
✓Source-specific guidance
Related services
Choose the collection model that fits your application.
WEB
Web Scraping Services
Managed website extraction and delivery.
Explore →CRAWL
Enterprise Web Crawling
Recurring multi-source collection.
Explore →API
Web Scraping API
Programmatic web data access.
Explore →AI
AI-Powered Scraping
Adaptive extraction for varied layouts.
Explore →LIVE
Live Crawler
On-demand collection for applications.
Explore →DATA
AI Training Data
Structured datasets for AI systems.
Explore →APP
App Scraping
Eligible public mobile and app data.
Explore →→
Solution Planning
Map sources, fields, rules, and delivery.
Discuss your project →Frequently asked questions
What teams ask about custom data extraction.
What are custom data extraction services?
They collect specific information from defined sources, apply a client-specific schema and quality rules, and deliver structured records for business use.
What makes data extraction custom?
The sources, fields, relationships, transformations, validation rules, update frequency, and delivery destination are designed around your requirement.
Which sources can Kvetoiq process?
Potential sources include public websites, marketplaces, documents, applications, accessible APIs, feeds, spreadsheets, directories, and approved legacy exports.
Can you extract data from PDFs and documents?
Yes, eligible text, tables, fields, and metadata can be assessed for document parsing or OCR-assisted extraction.
Can you combine data from multiple sources?
Yes. Kvetoiq can map different source fields into a common schema and support entity matching, normalization, and reconciliation.
How is a custom schema created?
We document the required fields, types, relationships, allowable values, metadata, exception logic, and business rules before production implementation.
How do you validate extracted data?
Validation can include required-field, type, format, range, duplicate, anomaly, source-metadata, and custom business-rule checks.
Which output formats are supported?
Common options include CSV, Excel, JSON, XML, APIs, webhooks, cloud storage, warehouses, and databases.
Do you support recurring data updates?
Yes. Pipelines can support recurring collection or other agreed triggers, subject to the source and use case.
Can you handle JavaScript-rendered websites?
Many modern public pages can be assessed for browser-based rendering, dynamic loading, and interaction-aware collection.
Can custom extraction support AI and RAG systems?
Yes. Outputs can be structured with source metadata, timestamps, classifications, and fields suitable for retrieval, enrichment, and evaluation workflows.
How do we begin?
Share representative sources, required fields, expected volume, update frequency, validation needs, and preferred delivery destination.
Plan your data pipeline
Turn your data requirement into a reliable production workflow.
Share your sources, required fields, update frequency, validation rules, and preferred destination. Kvetoiq will map the appropriate extraction architecture.