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Seekr CEO Pat Condo on Building AI That You Can Trust
“ Trust the input and defend the output.” – Seekr chairman and CEO Pat Condo joined iHeartMedia’s CEOs You Should Know for a conversation about why explainability has moved from a nice-to-have to a purchasing requirement in regulated and mission-critical industries. The full episode runs about 31 minutes. A summary of the main points is below.
What Seekr does
Seekr is the leader in explainable, defensible AI built for critical decisions in environments that demand accuracy and accountability. Seekr’s flagship platform, SeekrFlowTM, lets customers build right-sized, domain-specific models trained on their own data, while ensuring every output is explainable and defensible.
Explainability is a purchasing requirement, not a feature
Condo’s framing is that the open questions slowing enterprise AI deployment — hallucination, bias, inaccuracy, exposure of personal information, cyber risk — are not abstract concerns. They surface as specific decisions a company has to justify to a customer, an auditor, or a regulator.
He gives two examples from regulated commercial work. A health insurer denies a drug claim. A bank denies a loan application. In both cases someone will ask how the decision was reached, and the answer has to hold up under scrutiny.
“The same reason the government needs explainability is the same reason a health insurance company would.”
Seekr’s customer base spans commercial and government organizations, and that’s not by coincidence – the need for explainability exists across both.
Provenance, lineage, and intent
The technical core of the conversation centers on Seekr’s three-layer attribution framework for explainability: context attribution, data attribution, and model attribution. They each answer separate questions, but together give the customer a true understanding of how a model arrived at an output.
Context attribution addresses the question: which parts of the runtime input actually mattered? Data attribution traces which training or fine-tuning data most influenced a specific output. Lastly, model attribution answers the question: how did the model’s internal structure produce this?
“Provenance, lineage, and intent are the three key things when you’re building an AI model.”
Condo’s point is that individual pieces of this are available elsewhere. Carrying all three through a single system, so that any output can be traced end-to-end, is what makes an answer defensible in a legal, financial, or operational review.
Sovereign AI and the regulatory pull
Internationally, procurement conversations increasingly center on sovereign AI: keeping data, models, and confidential systems under national control, with explainability attached as a condition rather than an add-on. Domestically, recent executive orders addressing AI governance, guardrails, and explainability point in the same direction. Condo’s view is that these are converging on the same set of technical requirements, and that attribution is how you satisfy them.
Where SeekrFlow runs
SeekrFlow ensures AI sovereignty, model security, and flexibility across hardware- and cloud-agnostic deployments. Cloud, on-premises, edge, or air-gapped environments where a persistent network connection is not available. This deployment flexibility is important for regulated industries, which weigh data residency heavily in AI deployment decisions.
The future of AI
Asked about the next decade, Condo describes a growing market for autonomous reasoning systems operating in environments where a human cannot be present or connected — remote industrial sites, subsea, and eventually space. That work requires models that interpret images, video, and audio and drive physical actuators, not models that only produce text. Each deployment needs its own model, built and constrained for the job.
He also pushed back on how AI gets discussed publicly.
“People give it a personality, where they say it’s good or evil. And it’s not either of those.”
His view is that AI is a technology like electricity or nuclear power—something humanity learns to harness, with responsibility resting on those who deploy it.
Pat Condo’s Career
Condo’s career started at Northrop Grumman, where he participated in the MX Missile Program and the Space Shuttle Program—two of the largest programs in U.S. space and missile systems technology history. This is where his interest in navigation began. Search technology followed, and he has spent more than three decades as a technology and business leader, guiding companies across AI, search, software, and defense & intelligence. Pat was the CEO of NASDAQ listed companies Excalibur Technologies and Convera Corporation. In partnership with Allen & Company LLC, Excalibur exited to Intel and Convera sold its operations to Microsoft and Lockheed Martin.
Seekr came out of research his team started in the mid-2010s into how information moves through news and social channels, and how much of what people read is shaped by influence operations and incentive structures rather than accuracy. That question – Can you trust what went in, and can you defend what came out? – is the same one the platform answers now for enterprise and government customers.
See how SeekrFlow builds AI you can Trust →
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