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Seekr’s Explainability-First Approach Earns Notable Challenger Status from GAI Insights
Enterprise and federal AI buyers are looking past model capability alone to the infrastructure required to explain, audit, and control agents in production.
GAI Insights named Seekr a Notable Challenger in its 2026 Corporate Buyers’ Guide, highlighting our year-over-year momentum across both strategic value and market readiness, as well as our explainability-first approach to AI.
This validates one of our core product beliefs: explainability belongs in the infrastructure layer.
Agents are here. Trust is the next test.
With agent adoption already underway, what remains unresolved is whether organizations can deploy agents in production with the control, evidence, and auditability their workflows require.
Agents retrieve information, call tools, follow instructions, and produce outputs that can influence real decisions. With each step, the risk of error compounds, and so does the need for explainability. Organizations need to know what shaped the result, where it can be trusted, and where it needs to be challenged.
Explainability belongs in the infrastructure layer
A model that performs well in a demo is not the same as an AI system that can be governed in production. Enterprise teams need to trace outputs back to the context, data, tools, and model behavior that influenced them. They need evidence they can inspect, challenge, and use to improve the system over time.
That is the foundation of SeekrFlow™. SeekrFlow gives organizations a complete, secure operating system for training, validating, deploying, and scaling trusted generative AI agents and applications. Explainability is built into how teams operate the system, adding trust and transparency from development to deployment and beyond.
This matters because agentic AI does not fail in abstract ways. It fails inside retrieval, policy, tool use, data pipelines, and operating environments. Without visibility into those layers, teams are left guessing where the failure happened and how to fix it.
AI evaluation has to match the environment
SeekrGuard extends that foundation into model and agent evaluation. Instead of relying only on generic benchmarks, organizations can evaluate AI systems against their own business-specific and mission-specific criteria before deployment.
This is especially important in enterprise, government, defense, and regulated environments where auditability is part of the deployment requirement. Teams need AI they can explain to technical reviewers, business owners, procurement teams, regulators, and mission stakeholders.
Trusted AI means deployment control
Deployment control is part of that equation, too. Seekr supports SaaS, private cloud, on-premises, edge, and air-gapped environments so organizations can deploy trusted AI where their data, security, and operational requirements demand it.
The new standard for enterprise AI
GAI Insights’ recognition of Seekr reinforces what many AI buyers are already making clear: agent infrastructure has to be explainable, auditable, and defensible from the start.
We’re proud to continue building AI infrastructure organizations can explain and defend in real-world deployments.
Read the full press release from GAI Insights here.
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