Your Data Science Team,
Without the Hire
Not a deck and an invoice. A senior team that joins yours.
Our Work
Real engagements. Practical tools. Click a use case to explore the work.
A $20B retailer couldn't answer who was buying or why.
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The company operated at massive global scale (80+ markets, ~$20B+ annual revenue), yet commercially it lacked a unified decision layer. No standardized customer segmentation. Reporting was fragmented and heavily manual. Leadership could see revenue totals, but couldn't consistently understand who was buying, where, why, or what impact marketing activities actually had.
Consolidated 20+ data sources into a single commercial view and built the company's first standardized global customer segmentation model based on socio-demographic profiling. Centralized analytics in GCP (BigQuery) with automated ETL pipelines publishing curated datasets into PowerBI. Built executive performance visibility across country, channel, age, gender, customer segment, and post-campaign sales impact. Created a structured budget allocation and scenario-planning framework using linear optimization.
Organization moved from fragmented reporting to a unified commercial intelligence system covering every global market. Manual weekly Excel consolidation became automated daily visibility across 80+ markets.
AI went from strategic ambition to operational capability.
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The company operated at global industrial scale (150+ countries, ~$6B+ annual revenue), yet critical governance workflows still relied on manual document review and human-dependent validation. Workflows were compliant, but slow. AI existed in theory, not inside operational reality.
Designed and deployed enterprise AI agents embedded directly into operational workflows inside Microsoft 365. Three workstreams: Engineering Change Notice Automation (an AI agent that extracts technical modifications and generates audit-ready SharePoint tickets. Financial Spot-Checking Intelligence (structured invoice risk scoring to prioritize supplier review. Innovation Knowledge Agent (conversational AI integrated into Teams for internal process knowledge retrieval.
Manual governance-heavy processes transformed into controlled, repeatable, AI-supported operations within four months. Instead of AI being a strategic ambition, it became an operational capability.
From AI hype to a working prototype.
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The startup was building a carbon accounting solution in the rapidly expanding EU climate regulation market. No consensus on whether AI models could reliably infer missing emissions data from financial records. Leadership needed clarity on which AI model(s) could be part of a robust product.
Led a structured AI evaluation and rapid prototyping process. Evaluated leading vendors (OpenAI GPT, Google Gemini, Copilot) for understanding semi-structured financial documents. Benchmarked performance on inference quality, consistency, and scalability. Built a working prototype using the best-performing model with API-driven workflows. Assisted the COO with SQL and database structuring for early analytic use cases.
Company finished with a working AI prototype, clear evidence on model selection, defined API architecture ready for product development, and a product roadmap grounded in real capability rather than hype.
Can an LLM make better Google Ads decisions? We ran the experiment.
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The company ran Shopping campaigns across apparel, accessories, and watches, generating thousands of new search terms daily. The marketing team was manually reviewing which terms to exclude, a process that couldn't keep pace with volume. Irrelevant queries from competitor brands, wrong-category terms, and adjacent products were burning budget.
Built and assessed an LLM-powered classification pipeline against 6 days of real Shopping campaign data (~50,000 search terms, ~197K impressions). The system evaluated each term against the client's actual product catalog and produced structured, auditable outputs: an action decision (KEEP, BLOCK, EVALUATE, or WATCH), a relevance score, a category tag, plain-language reasoning, and suggested negative keywords.
The pipeline classified all ~50,000 terms with structured justification, flagged 178 for immediate blocking, and identified ~8,800 negative keyword candidates, demonstrating that LLMs can deliver consistent, explainable search term decisions at a pace manual review cannot match.
Hiring costs are hard to compare. This makes the trade-offs visible.
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Companies and workers in Spain often compare compensation scenarios using different anchors: gross salary, worker net, company cost, or contractor billing. Social Security, IRPF, regional tax rules, contract type, flexible compensation, and the Beckham regime can materially change the answer, making quick decisions hard to validate.
Built an interactive calculator that compares standard employment, Beckham-regime employment, self-employed, and Beckham-regime self-employed scenarios from the same input. Users can anchor the model on net, gross, or total company cost, switch annual/monthly views, adjust regional and personal tax assumptions, and visualize how taxes and Social Security affect each scenario.
The simulator gives finance, founders, and talent teams a fast way to reason about Spanish employment costs before turning to formal accounting, legal, or tax advice.
What We Do
We help businesses grow smarter by combining strategy, technology, and execution, delivering measurable impact through data-driven decisions.
Strategic Advisory
We work with leadership teams to define clear goals, align strategy with execution, and build scalable operating models.
Data & Analytics
From data audits to dashboards, we structure and translate your data into actionable insights that drive decisions.
Technology Integration
We assess your tech stack, recommend better tools, and ensure seamless implementation without disruption.
Digital Transformation
We help you digitize workflows, empower your teams, and evolve how you operate, from the inside out.
Execution Support
Not just slide decks. We roll up our sleeves and help you implement, iterate, and deliver results that stick.
Growth Strategy
Whether you're scaling or pivoting, we help define where to go next and how to get there, faster and smarter.
Global Firm Discipline. Startup Speed.
We come from top-tier consulting. We bring that rigor, without the overhead or the timeline.
Tested at Scale
20+ engagements across 7 countries, from $20B retailers to early-stage startups. Banking, energy, industrial, e-commerce. We've seen the patterns.
Honest About What Works
We don't sell AI where it doesn't belong. If your problem needs a clean spreadsheet or a process change, we'll say so. Our job is the right solution, not the fanciest one.
We Ship, Not Slide
Working systems. Models in production. Pipelines that run. No 80-page decks. No "strategic recommendations" without follow-through.
Built for the Long Run
We accumulate context about your business, your data, and your people. After six months, we know things no outside consultant could learn in a project kickoff.
Senior Practitioners, Not a Sales Team
The people behind Evolve On C have spent years inside Big Four consulting firms and high-growth companies, delivering data analytics and AI across industries and continents. We don't parachute in for a 12-week project. We embed with your team, learn your business, and stay until the work is done.
Rafael Perez
Rafael brings over a decade of experience leading data-driven strategy across financial services, energy, and the public sector, spanning 7 countries. His career bridges Big Four consulting, startup leadership, and senior roles at established companies across diverse industries. He has designed performance frameworks, built commercial intelligence platforms, and guided executive leadership through complex data transformations. At Evolve On C, he ensures every engagement connects analytical work to business outcomes, bridging the gap between what the data shows and what the organization actually does with it.
Sebastian Paik
Sebastian has spent his career in data analytics across Big Four consulting firms around the world, working on 20+ international engagements spanning banking, insurance, energy, mining, and the public sector. He brings deep technical capability in machine learning, causal inference, and LLM-powered systems. He's the one writing the code, building the pipelines, and making sure every model works in production, not just in a notebook.
Lean by design. When you work with us, you work directly with senior practitioners. We partner with a curated network of specialist data engineers, ML engineers, and domain experts to scale around your needs, but the founding team is always hands-on.
Let's See If It's a Fit
No pitch deck. No pressure. Just a 30-minute conversation about your data and AI challenges.