Paai uses Bohmian to catch analytical failures before production

Paai is building an agentic operating system for partnership teams. Its platform uses data and AI to help organizations discover, prioritize, and act on partnership opportunities. In a product like that, a small error in filtering, scoring, ranking, or a data transformation can change which partners the system recommends.

Paai already had the standard safeguards: engineering review, automated tests, and AI-assisted development tools. But those safeguards can still miss a particularly important class of error: code that runs, passes its checks, and reads as reasonable, but doesn’t do what the analysis requires.

Ultimately, the Paai team learned they didn't need a tool adding to the deluge of generic code diffs and comments. They needed an independent answer that honed in on the most essential question:

Does this change actually do what we intended?

Paai brought in Bohmian to catch that.

The gap: code that runs but does the wrong thing

Tests and code review can tell you a great deal about whether code works. They are less reliable at checking whether it matches the analytical intent behind it, especially when that intent isn’t fully encoded in tests or requirements.

Bohmian’s Data & ML verifier reviews changes for exactly this kind of failure:

• Filtering and population construction that doesn’t match the intended selection
• Data, label, and temporal leakage
• Evaluation designs that don’t measure what the team believes they measure
• Feature, aggregation, and join logic that silently changes the grain or meaning of the data
• Analytical assumptions that don’t hold, and nondeterminism that makes results hard to reproduce

A refinement step that didn’t actually refine

candidate set

refine

recommend

refinement bypassbroader candidates are still reaching ranking

Bohmian · verify against intent

verified path

One change to Paai’s partner-discovery logic was meant to take an initial candidate set and narrow it against additional criteria, and only this narrowed set was supposed to continue through the workflow.

As written, however, the code could let the broader, unrefined set continue downstream without the intended filtering applied.

Nothing crashed. The code was valid, and the failure was easy to miss in ordinary review.

Bohmian flagged the issue in review, and Paai fixed the logic before merge.

From pilot to ongoing use

Paai started with a pilot on real, production-bound changes. During the pilot, Bohmian surfaced multiple issues in data and partner-discovery logic changes that Paai was then able to correct before merging. Now Bohmian has expanded beyond the pilot to being a key part of Paai's change-management process.

What Bohmian adds

• Independence. Verification is a separate step from code generation, with its own requirements, evidence, and review process.
• Verification against intent. Bohmian checks more than whether code executes. It checks whether the code produces the behavior the analysis or product requires.
• Specialist depth alongside existing tools. Bohmian adds to tests, human review, and general-purpose AI coding tools rather than replacing them.
• Earlier detection. Issues surface in the pull request, before they reach production systems or downstream decisions.

About Paai

Paai is an agentic operating system for partnership teams. Its platform uses data-driven AI workflows to help organizations discover partners, run partnership motions, and measure outcomes.

About Bohmian

Bohmian is the independent assurance layer for AI-driven work.

Its specialist verifiers check work against the requirements that matter in each domain: Data & ML, backend systems, application security, and optimization, with contracts and finance in preview.

Bohmian Intent turns an organization’s existing context into verifiable requirements. Wyrd, Bohmian’s open-source infrastructure, keeps those requirements, verdicts, and evidence portable and under the customer’s control.