Back to Blogs
By Architecture, Not by Assertion
Why Seekr made the 2026 CB Insights AI 100 — and what the list says about where AI is going.
Seekr was just named to the 2026 CB Insights AI 100 — the tenth annual ranking of the 100 most promising private artificial intelligence companies in the world. CB Insights selected the AI 100 from a pool of more than 40,000 companies, using a methodology that weighs commercial traction, deal activity, partnerships, hiring momentum, and proprietary scoring including the Mosaic Score and Commercial Maturity index. Within that methodology, Seekr ranks in the top 1% of all private AI companies CB Insights tracks for company health and growth potential.
The AI 100 is a leading indicator, not a lagging one. Across the last five cohorts, AI 100 winners have exited at 3.2x the rate of comparable AI companies — 84% of those exits through acquisition — and have closed their next funding round a median 198 days sooner than peers.
Read the full press release HERE.
The market signal underneath the list
Beyond ranking individual companies, the AI 100 functions as a read on where AI itself is moving. CB Insights flagged three structural shifts in this year’s cohort. Two of them cut directly to why Seekr exists.
AI agents are running enterprise workflows — and they need their own rulebook
CB Insights describes a “Know Your Agent” stack now forming for autonomous agents in the enterprise. The agents on this year’s list are executing multi-step work in production — more than a million SOC investigations completed at one company, 1.2 million financial crime cases at another — without human sign-off on each step. That scale surfaces a question the industry is just beginning to answer: agents act on enterprise and government systems, but they aren’t employees, service accounts, or traditional software. They have no persistent identity, no verifiable owner, no scoped authority, and no audit trail tied to a principal. A growing cohort of companies is now building the security and observability layer that wraps around models and agents to close that gap. SeekrFlow™ is one of them — and SeekrGuard, our evaluation suite, was purpose-built to verify model accuracy and produce the attribution and audit trails that enterprise and government deployments demand.
Vertical AI winners are defined by their data, not their sector
CB Insights identified three patterns shared by the most durable vertical AI businesses: companies building proprietary models on non-textual data, companies whose deep workflow embedding creates switching costs, and companies whose access to rare datasets becomes the moat itself.
Both shifts point at the same unanswered question: in a world where AI agents act on consequential systems, how does anyone know the AI will do exactly what they intend?
That’s the question SeekrFlow was built to answer. It enables organizations to build domain-specific large language models, vision language models, and AI agents on their own data, deployed in their own environments, without surrendering custody. For a federal agency, a financial institution, a telecommunications operator, or a critical infrastructure provider, that’s not a feature — it’s the precondition for adopting AI at all.
The five disciplines of trustworthy AI
Trust in an AI system isn’t a feeling. It’s the result of knowing the system will do exactly what you intend, and being able to prove it at every step. That requires discipline at every stage:
- Orchestration. Controlling what the system does — the level of determinism in the workflow, the right human-in-the-loop interaction model, the boundaries inside which the AI is allowed to act.
- Observability. Seeing what the system did. Every reasoning step, every tool call, every output, captured with enough metadata to answer questions later.
- Explainability. Understanding why the system did it. Tracing every output back to the training data, context, and model behavior that produced it.
- Contestability. Acting on what you’ve learned. Correcting outputs, retraining models, escalating to humans, or stopping deployments — the institutional ability to push back on the system.
- Evaluations. Verifying it worked. Continuous testing and scoring against accuracy, bias, reliability, and mission risk — before, during, and after deployment.
Any one of these without the others is theater, not trust. Observability without orchestration tells you the system did something you couldn’t control. Explainability without contestability tells you why the AI broke a rule you can’t enforce. Evaluations without observability tell you the model passed a benchmark in a lab.
Together, the five disciplines are how AI gets built for environments where the answer to “did it work?” has to hold up in front of a regulator, a board, or a mission owner.
What the AI 100 is — and what it isn’t
The AI 100 is a checkpoint, not a destination. The customers and missions Seekr supports — defense, financial services, telecommunications, critical infrastructure, among others — cannot afford AI they can’t audit, defend, or explain. As enterprise and government adoption of AI agents accelerates across edge, on-premises, and sovereign deployments, the demand for AI that is explainable and defensible by architecture, not by assertion will only grow.
Book a consultation with an AI expert
We’re here to discuss your priorities and challenges, walk through a live demo, and explore how Seekr can help.
Let’s connect
Accelerate your path to AI impact
Book a consultation with an AI expert. We’re here to help you speed up your time to AI ROI.
Request a demo
