Automated, traceable, and compliant web research for decisions and processes

What is Web Data Collection and Research Agents?

Automated web research and data collection allows organizations to access relevant public information in a systematic, structured, and repeatable way, rather than relying on isolated manual searches that don't scale. Q2BSTUDIO designs agents specialized in obtaining data from authoritative public sources – business directories, official registries, marketplaces, job portals, news sites, open databases – with a rigorous focus on legal compliance, data quality and source traceability.

The first step of any web collection project is the definition of scope. We identify what information is needed, from which sources it can be legitimately obtained, how often it needs to be updated, and what restrictions apply. We review each site's terms of use, robots.txt files, privacy policies, and applicable regulations. We do not collect personal data without a legal basis, access authentication-protected content, or circumvent technical restrictions. If a source is not appropriate or sustainable, we propose alternatives or adjust the scope.

Web research agents operate with techniques adapted to each type of source. For sites with documented public APIs, we use the APIs respecting rate limits, authentication, and versions. For websites without APIs, we apply structured extraction techniques that interpret visible content, identify patterns, and extract normalized fields. For fonts that require complex navigation (filters, pagination, forms), agents simulate interaction in a controlled and respectful way with the site's resources.

The quality of the data collected is ensured at multiple points in the pipeline. Format validation verifies that the data meets the expected scheme: valid emails, standardised telephone numbers, reachable URLs, amounts in the reasonable range. Deduplication removes duplicate records from the same or different sources. Contrast between sources identifies discrepancies that suggest outdated or erroneous data. And periodic sampling by the team verifies that the extraction remains accurate as sources change their structure.

Traceability of origin is a differential component. Each data delivered retains the source URL, the date and time of collection, the version of the extractor used, and a quality indicator. This allows the receiving team to assess the freshness and reliability of each piece of data, return to the original source if necessary, and audit the collection process for internal or regulatory questions.

The use cases are diverse. Price monitoring allows you to monitor competitors, marketplaces or suppliers to detect changes in prices, availability and conditions. Corporate data enrichment complements CRM records with public information from directories, professional networks (within the terms of use), and official records. Market research collects data from relevant sectors, products, trends, and players. And regulatory tracking tracks changes in regulations, official publications, and industry standards.

The delivery of results is tailored to the process that consumes them. We offer delivery via REST APIs, structured files (CSV, JSON, Parquet), databases, visualization dashboards, or email and messaging alerts when relevant changes are detected. The refresh frequency is defined according to the value of the data and the capabilities of the source, avoiding excessive collections that do not provide new information.

Ongoing operation includes monitoring the health of extractors, detecting changes in source structure (which can break extraction), updating validation rules, and regularly reviewing compliance. Agents aren't systems that you set up once and forget about – web sources are constantly changing, and the system must adapt to maintain data quality and relevance.

It's important to be transparent about limitations. Not all web information is legally accessible, not all sources are reliable, and the quality varies by language, industry, and site structure. Fields that cannot be validated are marked as provisional instead of being presented as certainty. The goal is to provide useful and honest data, not volume without quality.