our Pricing and process

Our pricing

Generally, we ask for 10% of the net value we created (within limits), determined by the lift we achieve on the KPI, and it's direct impact on profit. This aligns incentives, creates clarity and trust. This is the high level concept, technicalities and safeguards will be applied.

no wait, biweekly delivery

There's no 3-months long strategy, process and tech audit. We start working on your systems from week one. Once they are operational, we make them fail-safe. When data quality allows it, we start streamlining, optimizing, automating, and enabling AI.

Our guarantees

We do not enter a tier-2 contract (anything beyond quick fixes) if we can not project a 10x ROI on our work within 12 months.
We follow tight Scrum, delivering value every two weeks.
We work by very strict and legally binding Client Protection and Data Protection policies, that involves automatically signed NDA.
Everything we do comes with testing, documentation and KT.

📞 Keep it simple, let's talk

We start within days, we ship functionality every 2 weeks

[Free] We run a series of value-packed discovery calls.

[Free] We start by mapping out data and processes.

[SP#1] Within days, we start delivering quick short-term fixes.

[SP#2] In 2-week cycles, we fail-safe and streamline operations.

[SP#3] Once reliability is provided, we start automation and AI.

📞 Keep it simple, let's talk

Problems we solve for Marketing, Sales, support, and C-level:
The articles below are hands-on guides about root causes and possible solutions. No sales.

constant need for custom reports

You need a business data analyst to build dashboards that aggregate data across key dimensions, and a data engineer to develop the reporting layer in your data pipeline to enable this functionality.

confusing and conflicting figures

You need a data engineer to set up data quality checks in your pipeline, review data sources and calculations, and an analyst to ensure a single source of truth, even if your teams use their own independent data.

laborious data entry and reporting

As your company grows, the data processes that evolved organically are reaching their limits. A data engineer should streamline the pipeline and implement best practices, while an analyst addresses key issues.

missing and inconsistent data

You need a data engineer to implement quality checks at data entry points and resolve logical errors in the BI pipeline, while an analyst ensures process synchronization and data requirements.

Slow and failing report generation

A data engineer needs to review the data pipeline and infrastructure systematically, addressing complexity. Industry best practices must compensate the complexity inherent to organizational growth.

teams disagree on basic numbers

You need an analyst to align sales and marketing teams with shared goals and knowledge, while a data engineer ensures accurate, consistent reporting to support collaboration and avoid misalignment.

📞 Keep it simple, let's talk

See our service packages, processes and costs