Key Insights: What began as a single engagement for us has evolved into a 4+ year relationship across six active production platforms and three global business units. Our team works as an extension of the client's organization, translating evolving business priorities into production AI systems that serve hundreds of global users and automate complex cross-functional workflows.

About the Client

We’ve had the opportunity to work closely with a Fortune 100 global technology company across multiple AI and data initiatives.

The relationship started with one project and expanded through internal referrals, driven by the quality of the work, the speed of delivery, and team’s end-to-end ownership. Today, we work across six active projects within three major internal divisions: marketing operations, corporate finance, and global sourcing.

The Challenge

Our main business partners were strategic stakeholders rather than technical product managers. They approach us with open-ended business objectives, operational bottlenecks, or market opportunities, not pre-defined technical specs. 

For us, the core challenge goes beyond writing code: we need to translate business ambiguity into clear technical execution.

Across the different workstreams, the business needs were clear:

• Make market data usable faster: Reduce the manual work required to prepare external reports for analysis.
• Make internal knowledge easier to find: Sales resources, product information, and enablement materials were difficult to find across internal tools.
• Support global teams at scale: Provide access to knowledge across languages, regions, and model availability constraints.
• Bring structure to complex documents: Finance and sourcing teams needed a better way to query, understand, and structure complex documents.

We had to build, security-test, and deploy every single solution within the client’s highly restricted engineering standards and Kubernetes infrastructure.

Marvik’s Approach & AI Forward-Deployed Engineers Execution

We work through small, senior delivery squads that combine AI engineering, data engineering, and full-stack development. Each squad is led by a Tech Lead who owns the technical direction and stays close to the business context from discovery through production.

Instead of receiving fixed technical specs, our teams work directly with stakeholders to understand the business goal, define the architecture, and build the right solution inside the client’s enterprise environment.

This model has been applied across several workstreams:

• Marketing Intelligence & Data Pipelines: We automated the end-to-end ingestion, validation, and transformation of external market research into Snowflake, reducing manual data preparation and giving analysts faster access to usable insights.
• Global Knowledge Platform: We built a centralized resource platform for sales teams, combining intelligent search with a conversational interface to help users find materials and generate localized narratives across dozens of languages.
• Finance Retrieval System: We extended the same RAG foundation into corporate finance, helping leadership query market reports through source-backed conversational answers.
• Document Intelligence for Global Sourcing: We built a production-grade RAG platform with a custom ontology layer in just 5 weeks, improving how teams retrieve, connect, and reason over complex documents.

To keep the work aligned over time, our teams maintain continuous delivery cadences and regular onsite collaboration through strategy sessions, roadmap planning, feedback, and hands-on problem solving with stakeholders.

Results & Business Impact

• 4+ years of continued trust: What started as one project grew into 6 active AI engagements across 3 internal organizations, driven by internal referrals and proven delivery.
• Manual data work automated: We replaced manual report preparation with automated, auditable data pipelines integrated directly into Snowflake.
• Hundreds of global users supported: Teams can search, retrieve assets, and generate localized materials across regions and languages.
• Full-Lifecycle Ownership: We maintain, operate, and improve production AI systems that support critical business workflows across multiple organizations.

Why This Matters

Moving enterprise AI to production inside a Fortune 100 environment requires more than strong models. It requires business context, governance discipline, technical ownership, and the ability to operate inside strict enterprise standards.

This partnership reflects how we work at Marvik: small, senior squads working close to the business, translating open-ended goals into production systems. 

By combining business understanding, technical leadership, and hands-on execution, we helped the client move fast without compromising governance, and expand AI adoption across multiple organizations over time.