How does your pricing model work?

Our pricing is based on the value and complexity of the dataset, not a per-seat license. We work with you to understand the scope and potential ROI, then structure a transparent fee that covers the initial engineering and ongoing management. This model ensures you are investing in a business outcome, not just a data file.

What is the typical timeline from request to first data delivery?

While every project is unique, a typical timeline for the first delivery of a validated, structured dataset is between 4 to 8 weeks. This allows for proper discovery, engineering, and quality assurance, delivering value significantly faster than most internal data acquisition projects.

Why shouldn't I just use a large, well-known data vendor?

Standard vendors sell the same generic data to everyone, including your competitors. We do the opposite. We build datasets that are uniquely tailored to your specific business question, giving you a proprietary source of intelligence that no one else has.

How do you ensure the data is ethically sourced and compliant?

Compliance is at the core of our process. We source data exclusively from publicly available information and through established legal frameworks. Every dataset comes with clear data lineage and is engineered to adhere to regulations like GDPR and CCPA, protecting you from legal and reputational risk.

What happens when our data needs evolve or a source breaks?

You are investing in a fully managed service, not a static file. We proactively monitor and maintain all data pipelines. If a source breaks, we fix it. If your needs evolve, we work with you to modify the dataset, ensuring it remains a valuable and reliable asset for the long term.

Custom enterprise datasets

Stop trying to force generic data to answer your unique business questions. We design, build, and deliver clean, structured datasets tailored to your exact specifications, fueling your most critical initiatives - from competitive intelligence to AI-powered forecasting.

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Dataset schema

refreshed

field	type
venue_id· primary	string
venue_name	string
category	enum
price· per item	decimal
lat / lon	geo
collected_at· utc	timestamp

CSV
JSON
Parquet
S3
API

We scrape data from over 5 000 sources

CHALLENGES

Why off-the-shelf data is costing you more than you think

At the enterprise level, you don't have generic problems, so why are you using generic data? Relying on standard feeds or tasking internal teams with ad-hoc data gathering creates a massive drag on your organization.

Your BI dashboards show an incomplete picture

Standard data feeds lack the specific attributes, granularity, and context you need, leading to flawed analysis and misguided strategies.

Your most expensive talent is stuck in the data mines

Your data scientists are spending up to 80% of their time finding, cleaning, and structuring data instead of building the models that drive revenue.

Your internal data projects are slow and brittle

The web scrapers and internal data pipelines your team builds are a constant maintenance burden, breaking with every website change and pulling focus from core business priorities.

You're missing critical market opportunities

By the time you get the data you need to assess a new market, a competitive threat, or a pricing opportunity, the window of advantage has already closed.

INDUSTRIES

See what your peers are building

Our clients don't buy data; they commission intelligence. Here are a few examples of custom datasets we've engineered to solve specific enterprise challenges.

Financial Services

A quantitative hedge fund asked for a dataset combining satellite imagery of retail parking lots with anonymized credit card transaction data to create a predictive model for the quarterly earnings of publicly traded retail companies.

Retail & CPG

A global consumer goods firm requested a daily dataset tracking competitor pricing, promotions, stock levels, and share-of-voice across the top 5 online retailers for their key product categories in North America and Europe.

Manufacturing

To prevent supply chain disruptions, an automotive manufacturer commissioned a dataset that monitors their top 300 global suppliers for signals of financial distress, geopolitical risk in their operating regions, and real-time raw material price fluctuations.

Airlines

A major airline requested a real-time data stream of all competitor fare and ancillary fee changes on their 50 most profitable international routes, delivered via a low-latency API directly into their proprietary revenue management engine.

E-commerce & Marketplaces

To protect its users from fraud, a leading online marketplace asked for a dataset to vet new international sellers by cross-referencing business registration data, online reviews from other platforms, and social media sentiment analysis.

Pharmaceuticals

To accelerate clinical trial recruitment, a pharma giant commissioned an anonymized dataset identifying patient populations that meet complex inclusion criteria for a Phase III oncology trial, using real-world data from specialist healthcare networks.

Logistics & Supply Chain

A national logistics provider requested a dataset combining real-time fuel prices with hyper-local traffic patterns and weather forecasts to dynamically optimize routing and reduce fuel consumption for their entire fleet of over 5,000 vehicles.

Commercial Real Estate

A commercial REIT (Real Estate Investment Trust) asked for a dataset layering zoning changes, new business permits, and shifts in foot traffic data to identify undervalued commercial districts in emerging metropolitan areas before prices escalate.

Energy & Utilities

To improve demand forecasting, a major utility provider commissioned a dataset that models power consumption by correlating long-range weather forecasts, real-time data from IoT smart meters, and schedules for major local events.

Audiobooks

A global audiobook service commissioned a dataset that analyzes trending charts and listener growth across major podcast platforms to identify breakout creators and emerging narrative trends, enabling them to proactively sign the next wave of audio talent.

Food Delivery

A leading food delivery platform asked for a real-time dataset that fuses hyper-local event schedules (concerts, sports), severe weather alerts, and real-time traffic data to proactively manage courier supply and launch targeted promotions in anticipated high-demand zones.

Ticketing

A major event ticketing platform asked for a dataset that monitors real-time pricing and inventory velocity on secondary resale markets. This intelligence is used to power their dynamic pricing engine for primary ticket sales, maximizing revenue while combating scalpers.

SECURITY

Enterprise-grade security and compliance

Your trust is our priority. We operate within a strict security and legal framework.

Data Security Management ISO/IEC 27001

Certifications

The platform is compliant with ISO 27001 standards.

GDPR Compliance

We act as a data processor. We provide a ready-to-sign Data Processing Agreement (DPA).

Data Locality

You have the ability to choose the data processing region (e.g., EU: Frankfurt, USA: Virginia).

Data Encryption

Full end-to-end encryption (TLS 1.3) and encryption of data at rest (AES-256).

Retention Policy

Your data is automatically and permanently deleted from our servers immediately after processing is complete.

Competitor visibility that accelerated growth

Our tailored scraping and data enrichment services allowed this Central and Eastern European qCommerce company to access critical competitor and market information. This facilitated informed decision-making and enabled them to refine their strategies, ultimately leading to accelerated growth and an enhanced competitive edge.

qCommerce

CEE Leader

Dynamic pricing built on competitor data

We provided this Central and Eastern European online grocery company with extensive scraping services, which allowed them to gather essential pricing and product data from competitors. This information helped them create a dynamic pricing strategy, resulting in increased sales and a stronger market presence.

Online Grocery

CEE Leader

Event and pricing data that grew market share

By leveraging our scraping services, this European ticket online sales company gained access to critical event and pricing data. This enabled them to refine their offerings, provide a more seamless user experience, and ultimately grow their market share in the competitive ticketing industry.

Online Ticketing

European Leader

Real-time restaurant data behind a better platform

By utilizing our comprehensive scraping services, this major European food delivery company gained access to valuable, real-time restaurant and menu data. This enabled them to optimize their platform and improve the user experience, resulting in increased customer satisfaction and revenue growth.

Food Delivery

European leader

FAQ

Frequently Asked Questions

Here’s what leaders want to know about building a strategic data advantage with custom-engineered datasets.

Let's discuss your custom dataset

Schedule a no-obligation consultation with our data strategists. Let's explore how a custom dataset can solve your most pressing challenge.

SLA-backed accuracy
Analysis-ready data
Zero operational overhead

Need an NDA first? Just mention it in the form - we’re happy to sign.

Custom enterprise datasets

Your BI dashboards show an incomplete picture

Your most expensive talent is stuck in the data mines

Your internal data projects are slow and brittle

You're missing critical market opportunities

Financial Services

Retail & CPG

Manufacturing

Airlines

E-commerce & Marketplaces

Pharmaceuticals

Logistics & Supply Chain

Commercial Real Estate

Energy & Utilities

Audiobooks

Food Delivery

Ticketing

Certifications

GDPR Compliance

Data Locality

Data Encryption

Retention Policy

Competitor visibility that accelerated growth

Dynamic pricing built on competitor data

Event and pricing data that grew market share

Real-time restaurant data behind a better platform

01How does your pricing model work?

02What is the typical timeline from request to first data delivery?

03Why shouldn't I just use a large, well-known data vendor?

04How do you ensure the data is ethically sourced and compliant?

05What happens when our data needs evolve or a source breaks?

Let's discuss your custom dataset