At High Alpha, we’ve seen firsthand how the promise of AI often collides with the reality of enterprise data chaos. While every company talks about becoming “AI-first,” the vast majority struggle with a fundamental challenge: their data isn’t ready for AI.
That’s why we’re excited to announce our investment in Datalinx, an AI “data refinery” that solves one of the most critical bottlenecks in enterprise AI adoption: data readiness. High Alpha is leading the oversubscribed $4.2 million Seed round, with co-investment from Databricks Ventures and Aperiam, along with angel investors Frederic Kerrest, co-founder of Okta and 515 Ventures; Ari Paparo, founder and CEO of Beeswax and Marketecture; Arup Banerjee, founder and CEO of Windfall Data; and others.
Here's why we invested.
The Data Readiness Crisis
In a survey by Wakefield Research on behalf of Informatica, of 600 data leaders from companies with $500M+ in revenue from the U.S., UK/EU and APAC regions, 87% had adopted or planned to adopt GenAI in 2025. However, 67% had thus far been unable to successfully transition even half of their GenAI pilots to production. Failed AI pilots and inaccurate, unreliable outputs undermine both ROI and trust in the technology. Why?
Quality & Continuity
The top cited obstacle impeding and undermining those GenAI pilots: data quality, completeness, and readiness. We’ve witnessed this challenge across our portfolio companies and conversations with enterprises. Companies accumulate massive volumes of data in their data warehouses but struggle to locate, clean, and structure all the specific data tables and elements they need. Compounding the challenge, companies’ AI applications often require enriching their existing data with new, external data, creating additional complexity and maintenance. Datalinx’s AI agents make this work painless and stay “always on”, preventing future drift and quality issues.
Speed & Cost
Data cleanup work was historically done by legions of data scientists and data engineers, either hired or contracted; talent that is very difficult and expensive to get. This required not only millions of dollars in sunk cost but months or years before an AI project’s hypothesized results could be realized. Under pressure from the board and C-suite for AI adoption, data and IT leaders don’t have that kind of time. Of surveyed data leaders, “92% believe others in the C-suite expect GenAI initiatives to generate ROI a lot faster than they will.” With Datalinx, deployment times and costs decrease dramatically.
Skills & Satisfaction
Data scientists and data engineers don’t like to do data cleanup. It’s menial and tedious janitorial work historically done begrudgingly because it needed to be done. “One of the main reasons data scientists are hired is to develop algorithms and build machine learning models for organizations. Most of the time, however, their time isn’t really spent on those tasks. Data practitioners spend 80% of their valuable time finding, cleaning, and organizing the data. This leaves only 20% of their time to actually perform analysis on it – which is the most enjoyable part of the role for most” (Pragmatic Institute). With the proliferation of data-dependent AI projects, data pros don’t have time for “data janitor” work, and they don’t want to do it. Datalinx liberates data pros to do more meaningful, valuable work.
Siloes & Ontologies
Data engineering and data science teams are typically centralized in an organization, a shared service supporting many business units and functions. Therein lies another impediment to project and product deployments. Line of business and functional teams submit requests and wait. Requests often lack required detail. Further, data teams lack context about the specific data source or business use case. And line of business or functional teams lack context about data tables and schemas. What results are time-consuming back-and-forths and misinformed judgment calls. Datalinx addresses this in two ways:
- Focus: Rather than being broad and generic, Datalinx targets marketing and advertising use cases, with AI agents governed by use-case specific ontologies, with relevant context, terms, relationships, and best practices built in.
- Bridging siloes: Datalinx gives data teams an “easy button” for marketing/advertising data cleanup, observability, and maintenance, while empowering the marketing/advertising business users with direct access to the data and insights they need.
A Differentiated Technical Approach
Key differentiators include:
- Local deployment within customer cloud data warehouses & platforms (e.g. Databricks, Snowflake, AWS, GCP, Azure), ensuring security and eliminating data movement requirements
- Commercial ontologies designed to put your data to use fast, with high fidelity and governance.
- Domain-specific AI agents trained to build and maintain clean data feeds into your marketing and advertising models and applications
- End-to-end data management and observability from discovery through activation, simplifying access, controls, and predictability
- Natural language interfaces that enable business users to prepare, enrich, understand, and activate their data without technical expertise
A Massive Market Opportunity With Powerful Partners
As AI adoption accelerates, most enterprises will need robust data readiness capabilities.The data preparation and integration market in marketing and advertising alone represents a $24B serviceable addressable market. We’ve seen the potential firsthand through customer stories. A major telco built a powerful personalization engine that generated hundreds of millions in incremental revenue, but required hundreds of personnel and years to establish the data foundations. These aren’t incremental improvements – they’re transformational business outcomes enabled by properly prepared data. Imagine if they had Datalinx.
Datalinx is already delivering for early customers, including Sallie Mae. From Li Lin, vice president of engineering: “We selected Datalinx as our co‑development partner to simplify and accelerate the data product development lifecycle. By automating the most time‑consuming aspects of the pipeline, enabling natural‑language data exploration, and embedding domain expertise into how we build data products, we’re already seeing promising early results with the potential to significantly accelerate our go‑to‑market delivery.”
I’m also excited about Datalinx’s partner positioning. Enterprises have been consolidating to fewer platform vendors and desiring composable solutions, leveraging their investments in their existing data warehouses/platforms. Datalinx complements data warehouse vendors like Databricks, Snowflake, AWS, and GCP, as Datalinx solves a major customer need while leveraging the data platforms’ advanced, foundational tools spanning industries and vertical use cases. The complement was clear enough that Databricks co-invested with us in the round and selected Datalinx as one of five startups for their inaugural Databricks AI Accelerator cohort. Downstream software vendors, system integrators, model developers, and marketing agencies benefit as well, with Datalinx accelerating implementations, time to value, and customer success.
Why Now?
Several factors create a unique window for Datalinx:
- AI adoption acceleration is creating unprecedented demand for data readiness solutions
- Cloud data platforms (Databricks, Snowflake) provide the infrastructure foundation for scalable deployment
- GenAI capabilities enable intelligent automation of previously manual data preparation tasks
- Enterprise recognition that data readiness is the critical bottleneck, not model development
The Datalinx Team
What drew us to Datalinx wasn’t just the market opportunity; this team has lived the data preparation pain firsthand and, more importantly, they’ve built solutions that work at scale in both startup and enterprise contexts.
I recently sat down with Datalinx CEO and Co-Founder Joe Luchs to explore his story and why he started Datalinx. Watch his fireside chat below:
The founding team:
- Joe Luchs (CEO) brings rare expertise in data activation and AI, MarTech, and AdTech infrastructure across both startups and large enterprises. During his five years at Amazon he built a new business unit integrating AWS and Amazon Ads that scaled to over $5 billion in total contract value. Before Amazon, he was a commercial founder at Beeswax (acquired by Comcast) and an early employee at BlueKai (acquired by Oracle Data Cloud). This isn’t his first time turning complex data challenges into valuable business outcomes. Joe is a smart, thoughtful, likeable human. I’m impressed with how strategically he’s crafted the product and go-to-market strategy and how effective he’s been at enlisting others to join the journey, including investors and co-founders.
- Jeff Collins (CTO) brings over 30 years of technology experience spanning startups to major enterprises like Intuit and Unity. At Unity he served as SVP of Engineering for Ad Platforms and led the division from $137M to over $600M in revenue over 4 years. He successfully deployed deep learning and reinforcement learning systems that achieved up to 30% performance improvement per deployment, before being promoted to GM of Unity Gaming Services, overseeing backend services for games and helping lead the company through its IPO. Yet he’s also hands-on-keyboard, applying AI-coding best practices at 70+ hours/week since joining Datalinx.
- Nicole Ferragonio (Chief Product Officer) brings a strong background in product, AdTech, and consulting, including eight years at Amazon. During her Amazon tenure, she worked extensively in advertising, focusing on clean room, measurement, advertising, and payment products, including the popular Amazon Marketing Cloud. She managed a 55-person team and helped scale Amazon's professional services arm to support customers with data integration challenges. Her experience at Amazon gave her direct insight into the core problem Datalinx addresses - she consistently saw that customers couldn't bring clean data to Amazon's platforms, with data preparation being the biggest barrier.
- Alek Liskov (Chief AI Officer) built Mailchimp’s AI strategy and roadmap, and led the development of Intuit’s internal Customer Data Platform and Revenue Intelligence AI product, scaling to 200K weekly active users. Prior to that, Alek was a product and data science leader at Verizon, building the company’s data and AI platform and leading the development of the Personalization AI platform.
Our Investment
In addition to our capital investment, High Alpha is backing the Datalinx team with our signature Studio Services, snapping in our team of product design, brand design, marketing, finance, accounting, recruiting, HR, and legal pros, who have helped launch dozens of companies, to endow Datalinx with an unfair advantage.
Looking Ahead
Datalinx is positioned to become the essential data utility that makes enterprise AI actually work. With a world-class team, validated customer demand, and a massive market opportunity, we’re confident they’ll help enterprises unlock the true potential of their data investments.
The AI revolution isn’t just about better algorithms — it’s about making data AI-ready. Datalinx is building the foundation that makes that transformation possible. And we’re thrilled to join them on the journey.
