Arkham
Builders shape the future. Not AI.
By Hec Monarrez (Co-founder, CTO) & Mau Sepulveda (Co-founder, CEO)
About us.
By winter 2022, Hec and I knew we were about to embark on a lifelong mission.
We’re building Arkham to stand the test of time. We want to be the place where the most hardcore builders — software engineers, data scientists, and designers — come to create exceptional Data & AI products that transform our clients’ operations.
If you’re looking for your next challenge and our mission speaks to you, reach out. We’re building a killer team.
Our founding motivation.
After working in corporations and high-growth startups, we saw that AI’s long-term value wasn’t in isolated models or vertical apps. The real opportunity was in building unified Data & AI Systems at the core of every company.
Maybe it was because we had seen so much inefficiency in the companies we worked for. Maybe it was because we’re drawn to hard problems. But from day one, Hec and I envisioned a system that powers decisions, automates processes, and optimizes operations for every company — think TARS, Jarvis, or Skynet (but the good kind).
But this vision was hard to achieve. Fragmented systems, siloed data, and complex AI tools made it difficult for most companies to adopt even basic AI solutions. In our view, only Palantir and Databricks were truly addressing the Data & AI integration challenge from the right angle. But they catered to large corporations and highly specialized roles.
We set out to change that. We envisioned a future where truly transformational AI was accessible. Where companies across Mexico, the U.S., and the Americas could achieve extreme efficiency and intelligence with their own tailored Data & AI systems.
Two years later, we are well on our way to making our vision a reality. We’ve assembled a hardcore team and built an exceptional product that powers the operations of companies like Circle K, MIP, and Televisa Editorial. Our Data & AI Platform empowers the builders at the core of these operations to unify their company’s data and leverage the best Machine Learning and Generative AI models to solve their hardest operational challenges.
Part 1: The Problem We Solve
Builders shape the future, not AI. But they need the right tools.
Our customers represent the backbone of our economy. They operate in industries such as retail, infrastructure, healthcare and credit. And while they hear every day about the next AI promise — a new chatbot or workflow tool — the reality is that they face much more fundamental challenges.
The builders — software engineers, data analysts and business teams, at the core of our customers’ operations aren’t held back by a lack of AI tools. They are held back by fragmented systems and siloed data, leading to operational inefficiencies: manual processes, limited cost controls, and a lack of real-time insights.
But as they look for solutions, they face an even bigger challenge: A Data & AI industry that’s incredibly complex. The diagram below illustrates this reality: a fragmented ecosystem of specialized tools built for specialized roles. This keeps AI transformation out of reach for companies without a strong Data Engineering and Data Science team.
For most of our customers, solving their challenges would have required an average of five different tools — ranging from Data Ingestion to Model Training and Inference — and at least three specialized roles.
Given these obstacles, it’s no surprise that so few companies have adopted AI at the core of their operations — aside from Microsoft Copilot.
Part 2: Our solution.
First Data, then AI.
This is exactly the problem we solve. Our platform eliminates this complexity by integrating the key tools builders need to unify data, train and deploy Machine Learning models, and implement Gen AI-powered solutions. All in one suite of seamlessly integrated applications.
With Arkham, builders can develop Data & AI systems end-to-end — without the overhead of assembling multiple specialized roles. Our platform provides a robust, scalable foundation, allowing companies to focus on solving problems, not managing infrastructure.
Arkham is built on three core pillars:
- A Robust Managed Infrastructure Layer
Automatically provisions compute, storage, and networking — powering the entire Data & AI lifecycle, from data integration and transformation to model training and inference. - A Highly Scalable Data Platform
At Arkham, we build Lakehouses. The core of our product provides a dedicated Lakehouse for every customer. Our platform embeds industry best practices to ensure scalability and efficiency across the entire data lifecycle. With intuitive applications, it is accessible to builders of all expertise levels. - An AI Platform with Machine Learning and Generative AI Capabilities
- On the Machine Learning front, we enable AutoML techniques alongside traditional training capabilities. Builders can leverage more than 10 model classes to solve problems such as forecasting and anomaly detection. At the core of our framework, we embed versioning, monitoring, and seamless deployment of these models.
- Gen AI is built into the core of our platform. Every Arkham application is powered by model experts that support builders throughout their Data & AI journey while equipping business teams with the tools they need to fully solve their challenges. These range from AI-powered analytics to workflow automation, alerting, and search tools.
The diagram below is an example of our platform at work for a Solar Energy Operator. With Arkham, the tech team can integrate inverters and logger data, detect anomalies in real-time, and simplify asset management processes. All of this — powered by a single, unified platform, without the need for specialized tools or roles.
For us, Arkham is more than just a product. We’ve developed a methodology designed to help our customers build robust, scalable solutions that grow with them over time. Whether your transactional systems change, you acquire a competitor, or you adopt a vertical AI solution, our Data & AI Framework is built to endure and evolve.
In the diagram above, you’ll see three key elements: Ontology, Semantic Layer, and Metric Store. These Arkham applications power our framework, which follows these key steps:
- Unify data following the medallion architecture enabling the Lakehouse to scale.
- Build a Semantic Layer — Define gold datasets as business objects that accurately represent your company’s operations.
- Standardize the metrics that define performance and decision-making across your operations
- Define actions that these objects and metrics can trigger — such as write-backs after an ML model run or automated email alerts based on predefined rules.
The result of this framework is your company’s Ontology — a digital representation of your operations, providing a reliable foundation for deploying AI solutions at scale. The Ontology* enables builders to create a unified system where you gain full visibility and control over your data, ensuring that every AI-driven insight, workflow, and optimization is built on a single source of truth. This is the foundation for a truly scalable, AI-powered operation.
Part 3: What’s next. Our vision and our company.
Contrary to the current narrative, we see a bright future for builders — particularly software engineers. They are in the best position to expand their capabilities beyond application development and take ownership of end-to-end Data & AI systems that will shape the future of their companies.
We believe that a company’s true competitive edge will depend on how well this system is integrated across every function. Companies that effectively leverage their data and configure AI models to enhance their strongest capabilities will win big.
At Arkham, our mission is to make this a reality — empowering every builder across the Americas with powerful, intuitive Data & AI tools to help their companies reach their peak potential.
Because builders shape the future, not AI.
Join us and do the best work of your life.
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Learn about what we do on our YouTube channel:
https://www.youtube.com/@ArkhamTechnologies
(*) Ontology is not a new concept. It has been used across multiple tech domains and adopted by companies like Palantir. We believe it is the key to building robust Data & AI systems.
