Demo: AI Product Segmentation for Retail Pricing
Watch a five-minute DS Stream demo of AI product segmentation that sorts thousands of SKUs into margin drivers, traffic drivers, and inventory risk.
Paweł Szczepanik
Author
Paweł Szczepanik
SEO & AI Systems Specialist
Paweł Szczepanik has worked with DS Stream as an SEO specialist since June 2023. He built and runs SEOMind, the company's production SEO application. It holds a base of about 56,000 keywords in three language versions (EN, PL, DE) and publishes to Webflow only after content clears six mandatory quality gates. The same app produced the information architecture for the new dsstream.com: 79 menu items and 783 assigned keywords. He also runs DS Stream's landing page factory, with WCAG AA contrast verified live. On the company blog he has published an analysis of Gemini 3.8 Live benchmarks (97.7% on Big Bench Audio against 35.1% on τ³-Banking).
His background is paid media. From 2009 to 2024 he ran Google Ads, Meta and Allegro Ads campaigns with combined budgets of about PLN 2 million a year. For one e-commerce client, ad-driven sales grew 800% in one year. He was CTO of Mediasphere and CEO of Wertui, where he recruited and trained salespeople. Jaguar, Land Rover, Suzuki, AGH and the University of Warsaw are among the brands from that period.
He now builds AI systems that take manual work out of marketing: agent teams that split tasks between local and cloud models, end-to-end automations, and agent memory with hybrid keyword and vector search. He holds the AI_devs 4 Builders certificate (October 2026).
- 55
Articles
First article
Latest article
Areas of expertise
- Artificial Intelligence
- Data Engineering
- Data Science
- MLOps
- AI in Retail
- Data Pipelines
- Data Migration
Articles by Paweł Szczepanik
AI POC Checklist for Your First AI Factory Use Case
An AI POC checklist built as five gates, each with an artifact, an owner and a pass condition, ending in a go, stop or re-scope decision.
Paweł Szczepanik
AI Vendor Lock-In: An Exit Plan for Your AI Factory
AI vendor lock-in cannot be removed, but it can be priced. The seven places an AI factory gets locked in, and how to write the exit plan before go-live.
Paweł Szczepanik
Data Engineering Partner Evaluation: An RFP Scorecard
A weighted RFP scorecard for data engineering partner evaluation: knockout gates, seven scored criteria, a paid pilot on your data and reference checks.
Paweł Szczepanik
Agentic AI in the Enterprise: Where Agents Earn Their Place
Agentic AI in the enterprise belongs in a process one step at a time: where the path is unknown, results can be checked and mistakes can be undone.
Paweł Szczepanik
Internal AI Platform: Build the Parts Every Team Rebuilds
An internal AI platform is the set of parts every AI team would otherwise rebuild: model gateway, prompt registry, evaluation tooling, and templates.
Paweł Szczepanik
RAG Evaluation: How to Know Your Retrieval Actually Works
RAG evaluation works best as two checks: did retrieval find the right passage, and did the answer stay inside it. Metrics, golden sets, regression gates.
Paweł Szczepanik
Do We Have AGI Yet? Gemini 3.8 Live Extended Thinking
Gemini 3.8 Live Extended Thinking is not AGI, but it is a voice assistant that keeps working while you talk. Benchmarks, pricing, limits, and a sample.
Paweł Szczepanik
Pricing at Scale: How AI Helps Retailers Protect Margin Across 50,000+ SKUs
Live webinar on September 30, 3:00 PM CEST: how AI-powered pricing optimization helps retailers protect margin and clear inventory across 50,000+ SKUs.
Paweł Szczepanik
AI Governance Framework: Guardrails Inside the Pipeline
An AI governance framework that runs inside the delivery pipeline, with blocking, advisory and human gates, guardrails and evidence from every build.
Paweł Szczepanik
Meet DS Stream at NASSCOM Agentic AI Confluence 2026
DS Stream joins NASSCOM Agentic AI Confluence 2026 in Bengaluru on September 24. Meet us to talk decision intelligence for retail and CPG.
Paweł Szczepanik
AI Factory Reference Architecture: Layers, Not Racks
An AI factory reference architecture built from logical layers, not GPU racks: five layers, two planes, the contract each exposes, mapped to any cloud.
Paweł Szczepanik
Model Monitoring in Production: Drift, Decay and Alerting
Model monitoring in production has to catch decay, not just drift. Four signals, the label latency problem, thresholds that hold, and alerts people act on.
Paweł Szczepanik
AI Factory Cost: Budgeting Beyond the GPU Line
AI factory cost depends on integration, data work, governance and run rate more than GPUs. Seven budget categories, build vs run rate, cost per use case.
Paweł Szczepanik
AI Center of Excellence vs AI Factory: Which Model Scales
An AI center of excellence sets standards; an AI factory delivers them. Here is where each model breaks and what has to exist before the CoE can step back.
Paweł Szczepanik
How to Choose a Data Platform for AI Workloads
An AI data platform is judged by the workloads it runs: training, inference, vector retrieval, feature serving and model governance. Here is how to choose.
Paweł Szczepanik
AI Maturity Assessment: Positioning Your Organization on the Curve
An AI maturity assessment scores capability across seven dimensions, not one score. See what each level looks like in delivery terms and what to fix first.
Paweł Szczepanik
Building an AI Use Case Pipeline with ROI Tracking
An AI use case pipeline turns scattered ideas into a funded portfolio: structured intake, scoring, stage gates and ROI tracked from baseline to production.
Paweł Szczepanik
AI-Ready Data Foundations: The Real AI Project Blocker
AI-ready data foundations decide which AI projects ship: four readiness dimensions, a use-case-scoped audit and how to price the gaps.
Paweł Szczepanik
From Pilot to Production: Why Enterprise AI POCs Stall
AI pilot to production: five organizational reasons enterprise AI POCs stall, and the operating model that moves them into production.
Paweł Szczepanik
AI Factory: An Operating Model for AI at Scale
An AI factory operating model turns pilots into repeatable delivery: five building blocks, a governed use case pipeline and a phased path to scale.
Paweł Szczepanik
AI Assortment Optimization for Retail and CPG: A Guide
AI assortment optimization for retail and CPG: data readiness, demand transference, SaaS versus custom build, and proving the result on the P&L.
Paweł Szczepanik
Data Platform Modernization for Fintech and Insurance
Data platform modernization for fintech and insurance under DORA: target architecture, migration sequencing and keeping regulatory reporting live.
Paweł Szczepanik
Nearshore Data Engineering Teams in Poland: A Buyer's Guide
Nearshore data engineering teams in Poland: staffing or delivery, three engagement models, the EU jurisdiction case and how to vet a partner.
Paweł Szczepanik
Apache Airflow Managed Service: What It Should Include
What an Apache Airflow managed service includes, how it differs from MWAA and Cloud Composer, and how providers define, measure and accept data outcomes.
Paweł Szczepanik
AI Act Compliance Consulting: An Engineering Buyer's Guide
What AI Act compliance consulting covers, which deadlines apply to you, and how Articles 9–15 translate into engineering deliverables you can audit.
Paweł Szczepanik
LLMOps Consulting: When MLOps Isn't Enough
When your MLOps stack can't tell if an LLM feature is improving, LLMOps consulting fills the gap. What it covers, six signs you need it, how to choose.
Paweł Szczepanik
Enterprise RAG Implementation Services: A Buyer's Guide
What an enterprise RAG project actually includes: engagement phases, why demos stall before production, custom vs RAG-as-a-Service, and cost drivers.
Paweł Szczepanik
AI Agent Development Services: Scoping, Costs & Timeline
What AI agent development services include, how the level of autonomy drives cost, typical price ranges, delivery timelines and the ROI metrics to track.
Paweł Szczepanik
GenAI Proof of Concept Development: From POC to Production
A delivery team's view of GenAI proof of concept development: success criteria for production, six failure points after the pilot, and a 90-day roadmap.
Paweł Szczepanik
Data Governance for AI: Policy to Production
Data governance for AI extends classic governance to training data and RAG. What the EU AI Act requires and how to run catalog, policy and audit.
Paweł Szczepanik
Real-Time Data Pipelines: Architecture That Holds Up
A real-time data pipeline holds up in production when it handles state, out-of-order events, load spikes and failures without losing records.
Paweł Szczepanik
Batch vs Streaming Data Processing: How to Choose
Batch vs streaming data processing comes down to how fresh your data must be. A framework based on freshness, volume and cost, plus micro-batch and Kappa.
Paweł Szczepanik
Generative AI Consulting Services: What They Cover
What generative AI consulting services cover, from use-case strategy and RAG to governance and LLMOps, and why they move pilots into production.
Paweł Szczepanik
Snowflake Migration Services: What to Expect in 2026
What Snowflake migration services involve in 2026, phase by phase, plus the warehouse-sizing and credit pitfalls that inflate cost and how to choose a partner.
Paweł Szczepanik
Data Quality for Machine Learning: A Guide
Models degrade quietly when inputs go bad. Data quality dimensions that matter for machine learning, tests with contracts and dbt, and drift monitoring.
Paweł Szczepanik
Databricks Consulting Services: Implementation Guide
What Databricks consulting services cover, from lakehouse design and migration to Unity Catalog governance and DBU costs, plus how to judge a partner.
Paweł Szczepanik
In-House vs Outsourced Data Engineering: 2026 Guide
A decision framework for in-house vs outsourced data engineering, covering total cost, knowledge risk, scalability, and the hybrid model.
Paweł Szczepanik
Build vs Buy Data Platform: A Layered Decision Framework
Build vs buy is the wrong question for a data platform. A layer-by-layer framework, honest TCO, and a third option: buy plus an implementation partner.
Paweł Szczepanik
Context Engineering: The New Core Skill for AI Teams
Context engineering decides what your AI agents see at the moment they act. Here is what it is, why it emerged, and how to build the skill in your team.
Paweł Szczepanik
RAG vs Fine-Tuning: A Decision Framework for 2026
A decision framework for RAG vs fine-tuning: five criteria, a total cost view, and when to combine both, built for CTOs and Heads of AI in 2026.
Paweł Szczepanik
MLOps vs LLMOps: Key Differences in 2026
MLOps vs LLMOps explained: how operating LLMs differs from classic ML—evaluation, prompts, cost, monitoring—and what your team needs to run GenAI in production.
Paweł Szczepanik
Data Lakehouse vs Data Warehouse: 2026 Guide
Data lakehouse vs data warehouse compared: architecture, cost, governance, ML support, and how to choose the right platform for enterprise workloads in 2026.
Paweł Szczepanik
Agentic AI in Data Engineering: A 2026 Primer
How agentic AI is reshaping data engineering—autonomous pipeline agents, real use cases, risks, and what enterprise teams should adopt in 2026.
Paweł Szczepanik
Data Pipeline Architecture Explained (2026 Guide)
A clear guide to data pipeline architecture: core layers, ETL vs ELT, batch vs streaming, and the patterns enterprise data teams use in 2026.
Paweł Szczepanik
Enterprise RAG Architecture: A Production Blueprint
Enterprise RAG architecture explained: the layers, design trade-offs, and production concerns—retrieval, reranking, guardrails, cost, and grounding.
Paweł Szczepanik
MLOps Consulting Services: What They Cover & How to Choose
A model that never leaves the notebook is a cost. What MLOps consulting services cover, when you need them, and how to choose a partner that ships models.
Paweł Szczepanik
Databricks vs Snowflake in 2026: Which Platform Wins?
Databricks vs Snowflake in 2026: architecture, pricing, AI workloads, and lock-in compared, with a simple rule for choosing one or running both.
Paweł Szczepanik
Building AI-Ready Data Infrastructure in 2026
Most AI projects fail on data, not models. A practical executive guide to building AI-ready data infrastructure in 2026 — layers, pitfalls, and a 90-day path.
Paweł Szczepanik
ETL Process Optimization: 12 Proven Techniques (2026)
ETL process optimization: 12 proven techniques to cut pipeline cost, shorten batch windows and raise data quality, plus a 30-60-90 day plan to apply them.
Paweł Szczepanik
Data Science Analytics Services for Enterprise Decisions
Data science analytics services turn enterprise data into decisions that move revenue, cost, and risk. See what they solve and how to measure the value.
Paweł Szczepanik
Data Science vs Machine Learning: Where the Value Comes From
Data science and machine learning solve different problems. This guide shows where each one creates business value and when you actually need a model.
Paweł Szczepanik
Building a Data Science Pipeline That Survives Production
A practical, engineering-first guide to building a data science pipeline that holds up in production, from ingestion to monitoring.
Paweł Szczepanik
What a Data Science Consultancy Actually Delivers
A practical look at what a data science consultancy delivers, how engagements run, and how to pick a partner that ships measurable business results.
Paweł Szczepanik
Data Science Consulting Services: From Data to Decisions
What data science consulting services deliver for enterprises: pipelines, modeling, and MLOps that turn data into decisions you can defend and maintain.
Paweł Szczepanik