Failed matching at scale
Simple, rule-based matching creates data silos instead of insights. You're left with an incomplete picture of the market because you can't reliably connect products, locations, or competitors.
AI-powered data enrichment
Unify your internal data with external market signals. Our AI platform cleans, matches, and contextualizes web and mobile data, delivering a complete, analysis-ready view directly to your data warehouse.
- Free your data team from manual preparation
- Eliminate inconsistencies with AI-driven validation
- Unify disparate products, companies, and locations
We scrape data from over 5,000 sources
DoubleData is not affiliated with or endorsed by the companies shown.
Break the data pipeline bottleneck
Many teams spend more time fixing data pipelines than analyzing results. An in-house data acquisition process that once worked can quickly become a source of escalating costs and unreliable insights. See if these challenges sound familiar:
Poor data quality
Your current processes deliver inconsistent, error-prone data, forcing your team into endless cleaning cycles and undermining business trust in your reports.
Spiraling infrastructure costs
Your cloud and proxy bills are exploding as you try to scale, yet data quality isn't improving, leading to questions about ROI from management.
Constant firefighting
Your engineers are defeated by sophisticated anti-bot mechanisms, CAPTCHAs, and dynamic layouts, blocking access to critical data and turning their work into a reactive loop of fixing scrapers.
High team attrition
Your best data engineers are frustrated with the repetitive, low-impact work of maintaining scrapers, making them a flight risk and hindering your ability to focus on strategic, high-value analytics.
The mobile app blind spot
You lack the niche competencies to extract data from complex native mobile applications, leaving a critical source of competitive intelligence untapped.
From raw web data to business intelligence: our AI engine explained
AI-powered acquisition
AI-powered structuring
AI-powered matching
AI-powered quality assurance
The strategic choice: buy a guaranteed result
The decision to build an in-house AI data acquisition pipeline is a commitment to a multi-year R&D project with unpredictable costs and timelines. Before you dedicate budget to building, consider the Total Cost of Ownership (TCO) and the opportunity cost for your team.
| AI Data Enrichment | In-House AI Scraping |
|---|---|
| Time to First Data | |
A timeline you agree with us (for standard sources) | 6-12+ months (recruitment, development, training) |
| Upfront Cost | |
Zero | High (recruitment fees, salaries, setup) |
| Ongoing Cost (TCO) | |
Predictable, fixed fee based on data volume & SLA | High & unpredictable (salaries, cloud & proxy bills, tool licenses, attrition) |
| Data Quality & Uptime | |
Contractually guaranteed via SLA (e.g., 99.93% accuracy) | Dependent on team skill; no external guarantee |
| Risk | |
Low (managed by a dedicated expert team, backed by SLA) | High (project failure, budget overruns, key-person dependency) |
| Team Focus | |
Strategic analysis, building models, driving business value | Maintaining infrastructure, fixing scrapers |
| Features | AI Data Enrichment | In-House AI Scraping |
|---|---|---|
| Time to First Data | A timeline you agree with us (for standard sources) | 6-12+ months (recruitment, development, training) |
| Upfront Cost | Zero | High (recruitment fees, salaries, setup) |
| Ongoing Cost (TCO) | Predictable, fixed fee based on data volume & SLA | High & unpredictable (salaries, cloud & proxy bills, tool licenses, attrition) |
| Data Quality & Uptime | Contractually guaranteed via SLA (e.g., 99.93% accuracy) | Dependent on team skill; no external guarantee |
| Risk | Low (managed by a dedicated expert team, backed by SLA) | High (project failure, budget overruns, key-person dependency) |
| Team Focus | Strategic analysis, building models, driving business value | Maintaining infrastructure, fixing scrapers |
Trusted by data-driven industry leaders
Our technology has been vetted and adopted by companies that treat data as a core asset. We provide technical case studies that detail the architecture and the results.
- Client: Global e-commerce marketplace
- Challenge: Inability to accurately match millions of third-party seller products to their master catalog using traditional methods.
- AI solution: Deployed our semantic matching engine to process and link product listings, achieving a 99.2% accuracy rate.
- Business impact: Reduced manual data cleanup work by 500 hours/mo; improved competitive pricing intelligence by 40%.
Enterprise-grade security and compliance
We operate within a strict security and legal framework and are ISO/IEC 27001:2022 certified.
Data Security Management ISO/IEC 27001:2022Certifications
ISO/IEC 27001:2022 certified (DEKRA).
GDPR compliance
Our process is GDPR-compliant and targets only publicly available information. We provide a ready-to-sign Data Processing Agreement (DPA) on request.
Data locality
You choose where your data is stored and processed, and we set it in your contract.
Data encryption
Encryption in transit (TLS 1.3) and at rest (AES-256).
Retention policy
We keep your data for a period you agree with us.
Choose your plan
Starter
Business
Enterprise
Frequently asked questions
We believe a strong partnership starts with clear answers to tough questions. Here are the topics most frequently raised by data leaders like you.
This is our absolute priority, and we understand it is yours as well. Our process is GDPR-compliant. We focus exclusively on publicly available data and do not process personal user information. Our legal team oversees our legal and ethical framework.
Data errors undermine trust in the entire BI department. That's why our quality assurance (QA) process is multi-layered. Data undergoes automated validations, AI-driven anomaly detection, and in critical cases, is verified by our analysts (human-in-the-loop).
Our goal is to minimize the workload on your side. We deliver data in two primary ways: via a dedicated, well-documented API, or through pre-built connectors that load data directly into your warehouse (Snowflake, BigQuery, Redshift, etc.). The data arrives cleaned, structured, and matched.
It is ready for analysis, so you avoid disconnected datasets.
Our SLA is the foundation of our reliability. It typically covers API uptime and on-time data delivery guarantees. Higher-tier plans can also include specific data quality metrics. Our support process is clearly defined, ranging from a standard ticketing system, to a dedicated Slack channel (Business), to a dedicated technical account manager (Enterprise) who serves as your direct point of contact.
This is a key question of Total Cost of Ownership (TCO) versus building your own solution. Beyond developer salaries, building a scalable scraping system in-house includes the high and unpredictable costs of proxies and cloud infrastructure, recruitment challenges, high attrition for specialized talent, and above all, the constant race against new anti-bot technologies. Outsourcing allows you to convert unpredictable R&D costs into a predictable operational expense, freeing your best engineers to focus on high-value analysis.
Turn your raw data into actionable signals
- Enrich leads with buying signals like new hires or technology usage.
- Add competitive context to your products with pricing and stock levels.
- Transform static lists into dynamic assets for smarter lead scoring and market analysis.