AI in data work. Fewer manual reports, faster answers to management questions - on your GA4, BigQuery and Power BI data.

AI implementation in analytics Consulting and implementation

I help you use AI for data analysis, report automation and smart workflows - in your business context

Teams spend hours on repeatable reports and manual CSV → Excel → chart exports. Management asks "why did conversions drop?" - the answer takes days. AI can take over much of that work - if implemented well. Not generic chatbots, but tools that understand your data from GA4, BigQuery or Power BI, with control over costs, security and answer quality.

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Does this sound familiar?

Hours on manual reports every week

Export, formatting, trend summaries - the same process AI can automate.

Management questions - answers take days

Ad-hoc analysis needs an analyst, even though data is already in dashboards or BigQuery.

Everyone uses ChatGPT separately

No control over data, prompts and costs - shadow AI across the company.

PoC in ChatGPT works, production doesn't

No integration with data sources, quality monitoring or security.

Don't know which AI model to choose

Token costs, GDPR, answer quality - dozens of options, no clear recommendation.

Data exists, insights don't

Dashboards show numbers, but nobody explains "what this means for the business".

How we work together - 4 steps

  1. 01

    Use case audit

    Map of repeatable tasks, data sources and automation potential. Prioritized by ROI and ease of implementation.

    Deliverable: use case map + ROI estimate
  2. 02

    Proof of Concept

    Prototype on your data (GA4, BigQuery, Power BI). Test answer quality, costs and security.

    Deliverable: working PoC + test report
  3. 03

    Production deployment

    Pipeline with monitoring, Slack/Teams/email integration, access control for sensitive data.

    Deliverable: production solution + documentation
  4. 04

    Optimization and knowledge transfer

    Prompt iteration, model cost optimization, team training on best practices.

    Deliverable: playbook + team training

Offer - what you get

Data analysis with AI

Natural language → SQL queries, insights from dashboards, anomaly detection in GA4, BigQuery, CRM.

NL → SQL Anomalies Insights

Report automation

Scheduled AI-generated reports, KPI change alerts, automatic trend summaries.

Scheduled reports KPI alerts Trend summaries

Workflows and integrations

Pipelines connecting data with LLMs, integration with Slack, email, Teams, webhooks and internal APIs.

Slack Teams API

Security and GDPR

Private APIs vs public models, control over sensitive data, prompt policies and audit trails.

Private API GDPR Audit

Model selection and architecture

API (OpenAI, Anthropic, Google) vs self-hosted, token cost optimization, model choice per use case.

LLM API Costs Architecture

Team training

Prompt engineering, best practices, safe use of AI on company data.

Prompts Best practices Workshops
  • Who it's for: companies with data in GA4, BigQuery, Power BI or CRM, analytics teams processing repeatable reports, organizations seeking measurable AI ROI.
  • Model: use case audit → PoC (2–4 weeks) → production deployment → optional ongoing support (analytics retainer).
  • Outcome: less time on manual reports, business questions answered in minutes not days, controlled costs and data security.

Free tools - start on your own

Before booking a consultation, estimate AI implementation ROI or find the right model for your needs.

AI ROI Calculator

Calculate how much you'll save with AI - concrete numbers based on team time and costs.

Run calculator

AI Model Selector

9 questions about budget, compliance, scale and integrations - get model recommendations tailored to you.

Choose model

AI Prompt Creator

8 steps to a precise instruction for ChatGPT, Claude, Gemini - ready prompt templates.

Create prompt
Krzysztof Surowiecki

Krzysztof Surowiecki. I implement AI in the context of data analytics - on data from GA4, BigQuery, Power BI and CRM, not as a generic ChatGPT demo. I know model costs, API limitations and GDPR requirements when working with internal data.

  • Practice, not hype - use case with measurable ROI first, then PoC, then production.
  • Integration with your existing analytics stack - without replacing the whole data ecosystem.
  • NDA and data control - private APIs, no leaking sensitive information to public models.

Trust - why clients choose me for analytics

20+years experience
150+projects
50+clients
7expert tools
  • Single point of contact: I don't hand off your project between teams - you work directly with me.
  • Consulting approach: use case and ROI diagnosis first, then PoC with measurable impact - before you invest in full rollout.
  • Confidentiality and security: NDA and architecture suited to companies with sensitive data.

Contact - let's take the first step

Tell me about repeatable tasks, data sources and questions that take too long to answer. In a consultation I'll identify use cases with the fastest ROI and propose a PoC or rollout plan.

  • Discussion of data sources (GA4, BigQuery, Power BI, CRM) and pain points.
  • Recommendation: where AI delivers the fastest return and which model/architecture fits.
  • PoC or implementation proposal with timeline and pricing.
Book a consultation Calculate ROI first