The DataWeave Blog

Tag: Data Accuracy

  • Fueling Agentic Commerce: Introducing the DataWeave Data Collection API

    Fueling Agentic Commerce: Introducing the DataWeave Data Collection API

    Commerce Is Entering Its Next Chapter

    Every major shift in commerce has been driven by data. A century ago, shopkeepers relied on ledgers to track sales. In the supermarket era, loyalty cards and barcodes turned transactions into insights. With the rise of eCommerce, clickstream data and online analytics reshaped how products were merchandised and sold.

    Now, we are entering the next chapter: agentic commerce.

    In this new paradigm, autonomous AI agents will handle the tasks that once required teams of analysts, merchandisers, and pricing specialists. Imagine an agent that monitors competitor prices across dozens of retailers, recommends adjustments, and pushes updates to a dynamic pricing engine, all in real time. Picture a shopper’s digital assistant scanning marketplaces for the right mix of price, delivery time, and customer reviews before making a purchase on their behalf.

    These aren’t distant scenarios. They’re unfolding now. Industry analysts estimate the enterprise AI market at $24 billion in 2024, projected to grow to $155 billion by 2030 at nearly 38% CAGR . Meanwhile, 65% of organizations already use web data for AI and machine learning projects, and 93% plan to increase their budgets for it in 2024. The trajectory is undeniable: the next era of commerce will be built on AI-driven decision-making.

    And what fuels those AI-driven decisions? Data. Reliable, structured, timely, and compliant data.

    The Data Problem No One Can Ignore

    Here’s the paradox: just as data has become most critical, it has also become hardest to acquire.

    For data and engineering leaders, the challenges are painfully familiar:

    • Old school scrapers that collapse whenever a site changes its HTML or introduces new interactivity.
    • Constant maintenance cycles, with engineering teams spending 20-40 hours a week debugging, rerunning, and patching scripts.
    • Low success rates, with in-house approaches succeeding just 60-70% of the time.
    • Complex infrastructure, from managing proxies to retry logic, pulls attention away from higher-value work.

    But the costs go far beyond engineering frustration.

    For retailers, broken pipelines mean competitive blind spots. A pricing team without reliable visibility into competitor moves can’t respond fast enough, risking lost margin or missed sales. Merchandising teams trying to optimize assortments are left with incomplete data, making poor stocking decisions inevitable.

    For brands, unreliable data disrupts visibility into the digital shelf. Products might be misplaced in search rankings, content could be outdated or incomplete, and reviews could signal issues, but without continuous monitoring, those signals are missed until it’s too late.

    For AI and ML teams, poor-quality training data means underperforming models. Without clean, consistent, and large-scale inputs, even the most sophisticated algorithms produce flawed predictions.

    Finally for consulting firms and research providers, fragile collection systems can compromise credibility. Clients expect robust, evidence-backed recommendations. Data gaps erode trust.

    The reality is stark: fragile pipelines don’t just waste engineering hours. They undermine competitive agility, customer experience, and business growth.

    Enter the Data Collection API

    This is why DataWeave created the Data Collection API, a self-serve, enterprise-scale platform designed to deliver the data foundation today’s enterprises need, and tomorrow’s agentic AI systems will demand.

    Data Collection API Dashboard_DataWeave

    At its core, the API replaces brittle scrapers and ad hoc tools with a resilient, adaptive, and compliant data acquisition layer. It combines enterprise reliability with retail-specific intelligence to ensure that structured data is always available, accurate, and ready to power critical workflows.

    Here’s what makes it different:

    • Enterprise-scale throughput: The API can process thousands of URLs in a single batch or handle continuous, high-frequency scrape. Whether you need daily pulses or near real-time monitoring, it scales with you.
    • Flexible access modes: Technical teams can integrate directly into internal workflows via API, while business users can configure jobs through a no-code interface. Everyone gets what they need without bottlenecks.
    • Adaptive resilience: As websites evolve, the API adapts automatically. No frantic patching, no firefighting.
    • Structured outputs, your way: Clean JSON, CSV, or WARC formats are delivered directly into your environment – AWS S3, Snowflake, GCP, or wherever your data stack lives.
    DataWeave's Data Collection API provides output in your preferred format
    • Built-in monitoring and self-healing: Automated retries, real-time logs, and usage dashboards keep teams in control without manual oversight.
    • Compliance by design: WARC-based archiving and SOC2 alignment ensure data pipelines are auditable, trustworthy, and enterprise-ready.

    This isn’t about scraping pages. It’s about creating a reliable data utility, a system that transforms raw web inputs into structured, actionable data streams that enterprises can trust and scale on.

    Who It’s Built For (And How They Use It)

    The Data Collection API isn’t limited to one role or industry. It’s been designed with multiple stakeholders in mind, each of whom can apply it to solve pressing challenges:

    Retailers and Consumer Brands

    Retailers live and die by competitive awareness. With the API, pricing teams can monitor SKU-level prices and promotions across channels, ensuring they don’t leave margin on the table. Merchandising leaders can track assortment coverage, identifying gaps relative to competitors. Digital shelf teams can measure search rankings, share of voice, and content completeness. The result is faster responses, stronger category performance, and fewer blind spots in shopper experience.

    Data Collection can be customised and scaled with our API

    AI & Machine Learning Teams

    AI teams depend on data at scale. Whether training a natural language model to understand product descriptions or a computer vision system to analyze images, the Data Collection API delivers the structured, high-quality inputs they need. Reviews, ratings, attributes, and product images can all be captured and delivered at scale. For teams building predictive models, from demand forecasting to personalization, the difference between mediocre and world-class often comes down to input quality. This API ensures AI systems are always learning from the best data available.

    Receive updates for your data collections

    Retail Intelligence & Pricing Platforms

    Technology providers serving retailers and brands face unforgiving client expectations. Missed SLAs on data delivery can mean churn. By using the Data Collection API as their acquisition layer, platform providers gain enterprise reliability without rebuilding infrastructure from scratch. They can scale seamlessly with client needs while maintaining the integrity of the insights their customers rely on.

    Marketing & Advertising Teams

    For marketing leaders, competition is visible every time a shopper searches. The API enables teams to track keyword rankings, ad placements, and competitor promotions with consistency. Instead of anecdotal data or partial coverage, marketers get a full picture of their brand’s digital presence and the strategies competitors are using to capture share of voice.

    Consulting Firms & Research Providers

    Consultancies and market research agencies deliver strategy. But a strategy without evidence is just opinion. The API allows these firms to back every recommendation with structured, large-scale data. Whether advising on pricing, benchmarking performance, or publishing analyst research, firms can deliver trustworthy insights without taking on the cost or distraction of building fragile data pipelines.

    The diversity of these use cases demonstrates why the API is more than a product. It’s a platform for collaboration across industries, ensuring every stakeholder, from engineers to strategists, has the reliable data foundation they need.

    Why DataWeave, Why It Matters

    Many vendors claim to deliver web data. Few can deliver it at enterprise scale, with commerce-specific expertise, and with proven ROI.

    What sets DataWeave apart isn’t just that we provide data; it’s the way we do it, and the outcomes we enable.

    • Commerce expertise baked in: With 14+ years of experience powering the world’s leading retailers and brands, DataWeave brings domain-specific intelligence that generic scraping vendors simply can’t. Our schemas are designed for commerce. Our defaults are smarter because they’re informed by retail realities.
    • Adaptability without firefighting: Most tools break when websites evolve. Our API adapts automatically, minimizing the need for engineering intervention. Teams stay focused on innovation, not maintenance.
    • Accessible to everyone: Whether you’re a senior data engineer automating workflows or a business analyst configuring a quick scrape, the API meets you where you are with both API and no-code interfaces.
    • Proven ROI, not promises: The results speak for themselves: $300K–$500K annual savings in engineering costs, 15–25% faster time-to-insights, and payback periods as short as 3 to 6 months.
    • Enterprise-grade trust: Reliability and compliance are built in, not bolted on. With SLA-backed delivery, SOC2 alignment, and audit-ready archiving, the API is trusted by enterprises that can’t afford uncertainty.

    This combination makes the Data Collection API not just a technical solution but a strategic partner for enterprises preparing for the age of agentic commerce.

    A Foundation for the Future

    The Data Collection API is more than an answer to today’s frustrating data problems. It represents a strategic foundation for tomorrow’s growth, designed to scale alongside the increasingly complex demands of commerce in the AI era.

    At the heart of DataWeave’s vision is the Unified Commerce Intelligence Cloud, a layered ecosystem that transforms raw digital signals into strategic insights. The Data Collection API is the entry point, the essential first layer that ensures enterprises have a reliable supply of the most important raw material of the digital economy: data.

    • Collection: Enterprise-grade acquisition of web data at scale. From product pages and search results to reviews and promotions, enterprises can finally count on continuous, structured inputs without worrying about fragility or failure.
    • Processing: Once collected, data is normalized, enriched, and matched across sources. What was once noisy and inconsistent becomes clean, comparable, and immediately actionable.
    • Intelligence: On top of this foundation sits advanced analytics, solutions for pricing optimization, assortment planning, promotion tracking, and digital shelf visibility, enabling sharper decisions at the speed of the market.

    This progression means enterprises don’t have to transform overnight. Many start small, solving urgent challenges like competitive price tracking or digital shelf monitoring. From there, they can expand naturally into richer intelligence capabilities, knowing that their data foundation is already strong enough to support more ambitious use cases.

    And as agentic AI systems begin to take on a larger share of decision-making, the importance of that foundation grows exponentially. These autonomous systems cannot operate effectively without clean, continuous, and contextual data. Without it, even the most sophisticated AI will falter, making poor predictions or incomplete recommendations. With it, they can operate at full capacity, powering dynamic pricing, real-time demand forecasting, and personalized shopping experiences at scale.

    The Data Collection API isn’t just about reducing engineering pain today. It’s about preparing enterprises to compete and win in an AI-driven marketplace that never sleeps.

    Getting Started

    The Data Collection API is available today via usage-based or enterprise subscription models. Many enterprises start with a proof of concept, scraping priority SKUs or a single retailer before scaling into production workflows. From there, the API becomes a natural on-ramp into DataWeave’s broader suite of intelligence solutions.

    For teams tired of fragile scrapers, this is a chance to reset. For enterprises preparing for the next era of commerce, it’s a chance to build a foundation that can scale with them.

    If your teams are still struggling with generic and inflexible data scrapers, request a demo now to see the DataWeave’s Data Collection API in action.

  • Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    In today’s expansive omnichannel commerce landscape, pricing intelligence has become indispensable for retailers seeking to stay competitive and refine their pricing strategies. The sheer magnitude of eCommerce, spanning thousands of websites, billions of SKUs, and various form factors, adds layers of complexity. Consequently, ensuring the accuracy and reliability of competitive insights presents a formidable challenge for retailers aiming to leverage pricing data effectively.

    At the core of any robust pricing intelligence system lies product matching. This process enables retailers to recognize identical or similar products across competitors. Once these matches are identified, tracking prices is a relatively more straightforward task, facilitating ongoing analysis and informed decision-making.

    Accurate matching is crucial for meaningful price comparisons and tailoring product assortments. The challenge is matching products is often complicated, especially for non-local brands, niche categories, or items lacking consistent global identifiers. It becomes even trickier when trying to match very similar but not identical products. A comprehensive approach that compares and analyzes multiple attributes like product titles, descriptions, images and more is essential.

    Artificial intelligence algorithms are commonly used to automate product matching, leveraging machine learning techniques to analyze patterns in images and text data. While AI can adapt and improve over time, the question remains: Can it fully address the complexities of product matching on its own?

    The reality is that many retailers still struggle with incomplete, inaccurate, or outdated product data, despite these AI-powered product matching solutions. This can lead to suboptimal pricing decisions, missed opportunities, and reduced competitiveness.

    Challenges in an ‘AI-only’ Approach to Product Matching

    While AI plays a vital role in automated product matching solutions, there are complexities that AI alone cannot fully address:

    Subjectivity in Matching Criteria

    Some product categories have subjective or hard-to-quantify criteria for determining similarity. AI learns from historical data, so it may struggle with nuanced aspects like:

    Aesthetics, style, and design: In the Fashion and Jewellery vertical, for example, products are matched according to attributes like style, aesthetics, design – all of which have some subjectivity involved.

    Quantity/packaging variations: In the grocery sector, variations in product packaging and quantities can introduce complexities that require subjective decision-making. For example, apples may be sold in different packaging like a 0.5 kg bag or a pack of 4 individual apples. Determining if these different packaging options should be considered equivalent often involves making a qualitative judgment call, rather than a clear-cut objective decision.

    Matching product sets: For categories like home furnishings, the focus is often on matching coordinated sets rather than individual items. For example, in the bedroom category, matching may involve grouping together an entire set of complementary furniture like a bed frame, dresser, and wardrobe based on their cohesive design and style. This goes beyond simply making one-to-one product associations, requiring more nuanced judgments about aesthetic coordination.

    Contextual Factor

    Products can have regional preferences, cultural differences, or evolving trends that impact how they are matched. AI may miss important context like Local/regional product names or distinct brand names across countries.

    For instance, in the image we see Sprite (in the US) is branded Xubei in China. Continuous human curation is needed to help AI adapt to this context.

    High Accuracy & Coverage Expectations

    Retailers rely on AI powered and automated pricing adjustments based on product matching for insight. To ensure that pricing recommendations and updates are accurate, accurate product matching is crucial. For this, simply identifying similar top results is not enough – the process must comprehensively capture all relevant matches. While AI excels at finding the top groupings with around 80% accuracy, even small matching errors can have significant consequences.

    As AI matching improves, customer expectations may rise even higher. If AI achieves 90% accuracy, for instance, SLAs may demand over 95%. Reaching such a high level of accuracy is very challenging for AI alone, especially when faced with incomplete data, contextual nuances, evolving trends, and subjective matching criteria across products and categories.

    The solution is to combine the power of AI with human expertise. This is the key to achieving true data veracity – the accuracy, freshness, and comprehensive coverage required for precise and reliable product matching.

    Human-in-the-Loop Approach for Elevated Product Matching

    Human intelligence and quality testing can elevate the AI powered product matching process by addressing key challenges:

    • Matching Validation: AI algorithms may identify product matches with 80-90% accuracy initially. Having humans validate these AI-suggested matches allows for correcting errors and pushing the accuracy close to 100%. As humans flag issues, provide context, and re-label incorrect predictions, it allows the AI model to learn and enhance its reliability for complex, high-stakes decisions.
    • Applying Contextual Judgment: For subjective matching criteria like aesthetics, design, and categorizing product sets, human discernment is needed. Humans can make nuanced judgments beyond just quantitative rules, ensuring meaningful apples-to-apples product comparisons. Their contextual understanding augments AI’s capabilities.
    • Continuous Learning Via Feedback Loop: Product experts possess rich category knowledge across markets. Integrating this human insight through an iterative feedback loop helps AI models quickly learn and adapt to changing trends, preferences, and context. As humans explain their match assessments, the AI continuously enhances its precision over time.

    By combining AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage demanded for actionable competitive pricing insights.

    DataWeave’s Data Veracity Framework: A Scalable Workflow Combining AI and Human Expertise

    Given the vast number of products, retailers, and brands that exist today, any product matching solution must be highly scalable. At DataWeave, we bring you such a scalable workflow to address these complexities by integrating human expertise with AI-driven automation. The image below outlines our approach for combining AI with human intelligence in a seamless, scalable workflow for accurate product matching:

    Retailers and brands can benefit in several ways with this workflow, as listed below.

    Several Rounds of Data Verification Due to Hierarchical Validation Teams

    The workflow employs a hierarchical validation team of Leads and Executives to efficiently integrate human expertise without creating bottlenecks. Verification Leads play a pivotal role in managing the distribution of product matches identified by DataWeave’s AI model to the Verification Executives.

    The Executives then meticulously validate these AI-suggested matches, adding any missing product associations and removing inaccurate matches. After validation, the matched product groups are sent back to the Leads, who perform random sampling checks to ensure quality.

    Throughout this entire workflow, feedback and suggestions are continuously gathered from both the Executives and Leads. This curated input is then incorporated back into DataWeave’s AI model, allowing it to learn and improve its matching accuracy on an ongoing basis.

    This hierarchical structure ensures that human validation seamlessly scales alongside the AI’s matching capabilities. Leveraging the respective strengths of AI automation and human expertise in an iterative feedback loop prevents operational bottlenecks while steadily elevating overall accuracy.

    Confidence-based Distribution of Matched Articles for Validation

    The AI model assigns confidence scores, differentiating high-confidence (>95%) and low-confidence matches. For high-confidence groups, executives simply remove incorrect matches – a quicker process. Low-confidence matches require more human effort in adding/removing matches.

    As the AI model improves over time with feedback, the share of high-confidence matches increases, making validation more efficient and swift.

    Automated, Standardized Process with Iterative Feedback Loop

    The entire workflow is standardized and automated, with verification metrics seamlessly tracked. At each step, feedback captured from both leads and executives flows back into the AI, enhancing its matching accuracy and coverage iteratively.

    DataWeave’s closed-loop system of AI automation with hierarchical human validation allows product matching to achieve comprehensive accuracy at a vast scale.

    Unleash the Power Accurate and Comprehensive Product Matching

    In summary, combining AI and human expertise in product matching is crucial for retailers navigating the complexities of omnichannel retail. While AI algorithms excel in automation, they often struggle with subjective criteria and contextual nuances. DataWeave’s approach integrates AI-driven automation with human validation, delivering the industry’s most accurate product matching capabilities, enabling actionable competitive pricing insights.

    To learn more, reach out to us today!