A small firm of veteran, battle-hardened engineers. Your AI operating partner for healthcare, government, accounting, and finance teams.
For decades, software has been built for the “average customer”. But no customer is average.
The cost of building software is plummeting. You shouldn't be forced to adapt to a SaaS product, your tools should adapt to you.
At the same time, the stakes are skyrocketing. Your competition is fierce. Adversaries are using powerful AI to search your systems for vulnerabilities and customer data to steal. Different companies release new models constantly.
Anyone can vibe-code prototypes that appear to work. But running them securely in production, on real workloads, while handling the edge cases your team handles every day and maintaining them over time...that's a whole different ballgame.
Agents that know your accounts, your counterparties, and your approval chain, wired into the software your team already opens every morning. Each one scoped to a specific decision your team already makes. We have the judgment to know when to use AI where it adds value and software where it doesn't.
Deal screening, due diligence, approval routing, internal research.
Reconciliations, document processing, claims work, reporting pipelines. Automation built around the reality of multi-entity finance: the failure modes a real human would catch, and human-in-the-loop kickbacks for the cases that need them.
Bank & ledger reconciliation, invoice operations, claims, close cycle.
We build data infrastructure from scratch or work with what you've already got: spreadsheets, PDFs, bank feeds, vendor systems. Either way, your data becomes something agents and analysts can actually query, extended where useful with public and partner data we maintain across our engagements.
ETL, warehousing, document extraction, public & partner data.
Audits, monitoring, and compliance work for software your team runs, whether written in-house, by vendors, or by AI tools. We find where it leaks, fails under load, or won't pass a SOC 2 review, and build the controls that keep watching after.
Pre-launch reviews, AI agent audits, SOC 2 readiness, anomaly detection.
We also interview engineering candidates for teams driving their own hiring. You bring the pipeline; we read the technical signal.
Mad Data Labs is a focused R&D practice, run by an ex-AWS engineer with over a decade of experience building production AI and data infrastructure at scale for Fortune 50 companies, startups, SMBs, and government agencies in highly regulated industries.
Our background spans building production ML workflows, training models, document processing, data pipelines, applied research, and scaling engineering teams.
A paragraph or two on what you're trying to build, by when, and your budget, is more useful than a formal RFP.