PSV was founded by a team with credentials that are rare in applied AI consulting. The AWS AI Research Award recognized contributions to machine learning infrastructure; NSF-funded research produced peer-reviewed publications on physics-informed neural networks — a class of methods that embed domain knowledge directly into model architecture to improve reliability on sparse, noisy data.
That research foundation shapes how we approach production systems. We understand not just what a model predicts, but why — and we design accordingly, with explicit uncertainty handling, principled guardrails, and the kind of audit trails that hold up under scrutiny.
We deploy purpose-built AI systems that reason through real operational workflows, integrate with your existing stack, and produce outputs your team can audit and trust. Our focus is on operations where correctness and reliability matter: deal screening, compliance workflows, invoicing, scheduling, financial reconciliation, and back-office execution. We don’t build demos or proof-of-concepts that expire. We build systems intended to run in production for years, and we remain accountable for their performance.
