Senior data and AI delivery for organizations navigating complex transformation.
Evolve On C helps multinational and operational teams move from fragmented data, manual review, and disconnected AI experiments into working analytics, automation, and decision systems.
Company snapshot
Core capabilities
We combine business context, data science, engineering, and adoption design around a defined decision or workflow.
Data and decision systems
Data models, pipelines, dashboards, segmentation, forecasting, optimization, measurement, and executive decision support.
AI feasibility and validation
Use-case definition, model benchmarking, retrieval and LLM evaluation, cost analysis, risk review, and prototype validation.
Operational automation
Document extraction, classification, routing, knowledge retrieval, exception handling, risk scoring, and human-review workflows.
AI transformation and governance
Operating-model design, controls, auditability, adoption, workflow redesign, training, documentation, and responsible handover.
Selected evidence
Unified commercial intelligence across 80+ markets
Consolidated more than 20 data sources into standardized segmentation, automated reporting, performance visibility, and planning tools.
Read the case study →Three AI agents deployed in live operational workflows
Built controlled systems for engineering review, supplier invoice risk, and internal knowledge support during a four-month engagement.
Read the case study →AI product feasibility tested before larger investment
Evaluated whether models could infer emissions from financial records and built the evidence needed to assess the product direction.
Read the case study →LLM-assisted paid-search review tested on live campaign data
Designed and tested a classification workflow to identify search-term waste and support more consistent campaign review.
Read the case study →Delivery model
- Define. Clarify the decision, workflow, users, data, constraints, and owner.
- Validate. Test feasibility, model behavior, cost, risk, and review requirements.
- Build. Deliver the smallest useful dashboard, pipeline, model, or assisted workflow.
- Embed. Document controls, train users, measure performance, and create a handover path.
Senior practitioners
Rafael Perez
Rafael brings over a decade of experience leading data-driven strategy across financial services, energy, public sector, retail, and growth contexts. His work bridges executive advisory, commercial analytics, performance frameworks, and data transformation.
Sebastian Paik
Sebastian has worked across international consulting engagements in banking, insurance, energy, mining, and the public sector. He builds the technical layer behind the work: machine learning, causal inference, LLM systems, pipelines, and production-ready analytics.
Sharon Sciammas
Sharon brings 20 years across data, analytics, AI, growth, product, and operations. She helps organizations turn data products into adopted business capabilities, connecting technical delivery with workflow design, growth, and practical change.
Engagement models and delivery controls
Engagement formats
Scoped discovery, feasibility assessment, prototype, delivery project, embedded senior support, or partner-led and consortium work where requirements align.
Responsible implementation
Clear assumptions, evaluation criteria, human review, access controls, limitations, documentation, ownership, and operating requirements.
Technology environments
Microsoft Azure and 365, Databricks, AWS, GCP and BigQuery, OpenAI and Azure OpenAI, Python, SQL, Power BI, and modern LLM orchestration tools.
Geographic fit
Amsterdam and Barcelona-based, working globally. Direct and partner-led opportunities are assessed against scope, eligibility, credentials, geography, and delivery capacity.
Start with the scope, workflow, or opportunity.
We will assess the fit, the smallest credible first phase, and whether direct or partner-led delivery makes sense.