Three companies show how innovation can take shape well beyond the startup stage: Rippling is applying AI to business administration, Seekr is building an enterprise AI platform for consequential deployments, and Ultrasound AI is using ultrasound images to estimate pregnancy due dates. They are examples selected for Fast Company’s 2026 Next Big Things in Tech coverage—not evidence that established companies generally innovate as quickly as startups.
What makes these companies notable?
The examples span different fields and kinds of work: business operations, enterprise AI infrastructure, and clinical ultrasound analysis. Their common thread is an attempt to apply technology to specific, practical needs rather than simply introduce a new consumer gadget.
Fast Company’s feature, by Ross Rubin and published October 6, 2026, presents the companies as honorees. Its descriptions offer a snapshot of their products and aims, not a head-to-head evaluation of performance or proof that their approaches outperform alternatives.
How Rippling is applying AI to business operations
Fast Company describes Rippling as a platform that streamlines administrative work across human resources, IT, and finance. Its example of innovation is AI that can draw on a company’s own data structures, context, and controls. In principle, that context can help an AI response reflect the organization’s actual business state when assisting with administrative tasks.
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The value of this approach depends on the relevance and quality of the company information available to the system. Fast Company’s description is not an independent product test, so it should be read as an account of Rippling’s approach rather than verified evidence of how well it works in practice.
How SeekrFlow targets enterprise and government AI
Fast Company describes SeekrFlow as an end-to-end platform for creating, deploying, and managing large language models using an organization’s own data. It positions the platform for enterprise, government, defense, and regulated-industry settings, with an emphasis on data quality and oversight.
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The feature also reports that Seekr partnered with General Dynamics Information Technology on agentic AI solutions for federal agencies and that SeekrFlow is available on AWS GovCloud. Those are product and deployment claims reported in the article; organizations considering a deployment should confirm current availability and suitability for their requirements directly.
How Ultrasound AI estimates a pregnancy due date
Fast Company says Ultrasound AI’s Delivery Date AI (DDAI) analyzes a standard ultrasound image to predict a pregnancy delivery date in less than a minute, rather than relying on manual formula measurements. A due-date estimate can inform clinical planning, including decisions about the timing of an induction or cesarean and preparation for a potentially high-risk birth.
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The feature reports that Ultrasound AI received FDA De Novo clearance in February 2026. That regulatory milestone is reported by Fast Company; the article’s account alone does not establish the precise clearance status or intended-use wording. DDAI should be understood here as a clinical technology described in the feature, not a consumer pregnancy tool or a substitute for clinical judgment.
How the three examples differ
| Company or product | Who it is for | Input or context described | Task it supports |
|---|---|---|---|
| Rippling | Business operations teams and employees | Company data structures, context, and controls | AI-assisted administrative work in HR, IT, and finance |
| SeekrFlow | Enterprise, government, defense, and regulated-industry organizations | An organization’s own data, with attention to data quality and oversight | Creating, deploying, and managing large language models |
| Ultrasound AI’s DDAI | Clinical users involved in pregnancy care | A standard ultrasound image | Estimating a pregnancy delivery date |
These are distinctions drawn from Fast Company’s descriptions, not measured performance comparisons. The products address different users, inputs, and decisions, so the feature does not establish which is more innovative or effective.
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What this says—and does not say—about established-company innovation
The three examples suggest that established companies can pursue new applications in areas where they already have, or are building, specialized products and capabilities. Rippling’s approach centers on business-specific context; SeekrFlow is framed around organizational data and controlled deployment; Ultrasound AI applies image analysis to a clinical estimate.
They do not show that established firms as a group match startup speed, risk tolerance, or outcomes. They are selected award examples, and the feature does not provide comparative testing against startups or competitors. Their significance is narrower: innovation can mean applying AI to a defined operational or clinical problem, not only launching a new consumer product.
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