Every company today is drowning in the same kind of noise. Emails pile up by the hundreds. Meeting transcripts stack up faster than anyone can review them. Messages arrive across a dozen different tools, each one demanding a decision. Somewhere inside that flood sits the handful of items that actually move a business forward, but finding them usually means reading everything first. Most teams never get that far. They react to whatever lands last instead of what matters most, and the gap between the two keeps growing as the volume of information keeps climbing.

Matt Francis is the CEO & Co-Founder of Herd AI, Inc, a company built to sort through exactly that kind of overload. A lifelong technologist who grew up taking electronics apart just to see how they worked, Matt spent years watching executives collect information without ever building a system to act on it.

In this episode of Lead with AI, Dr. Tamara Nall speaks with Matt Francis about how Herd AI, Inc decides what deserves a team's attention, why the platform intentionally stops calling its own AI model once it trusts its own judgment, and what it looks like when an entire company works from the same set of ranked priorities instead of a hundred separate inboxes.

What Happens When Information Outpaces Attention?  

Matt Francis calls it a herd problem before he calls it a technology problem. Companies are getting thousands of signals a day: emails, meetings, messages, and transcripts, and none of it arrives pre-sorted by importance. Before Herd AI, Inc, most of that information landed in a folder, got summarized by a chatbot, and then sat there, unused.

Herd AI, Inc takes a different approach. The platform pulls in a company's emails, meetings, and messages automatically and scores them against its stated objectives. A signal that touches a strategic priority rises to the top. A routine update stays out of the way. Matt frames the whole system around a simple idea: the herd is the company, and everyone in it should be working from the same understanding of what matters right now, not fragments of it scattered across a dozen inboxes.

Teaching an AI When to Speak Up and When to Stay Quiet  

Most AI products call a large language model every time they process a piece of information, and that adds up fast in both time and cost. Herd AI, Inc works differently. When new signals come in, the platform's learning engine scores them with AI at first. Users respond with feedback, marking results as accurate or not, and once the system reaches a high enough confidence level on a given type of task, it stops calling the model altogether.

The AI only comes back if the confidence score starts to slip. At that point, Herd AI, Inc leans on the model again until accuracy climbs back to where it needs to be, then steps back once more. Matt Francis describes it less like a tool and more like a colleague, one the team argues with, corrects, and occasionally gets frustrated with, the same way they would with any coworker learning a new role.

The result is a system built for both performance and cost, not capability alone.

The Executive Who Got His Evenings Back  

One Herd AI, Inc customer came to the platform with a familiar complaint. Emails kept arriving well past five in the afternoon, and by the time he cleared his inbox it was often seven or eight at night. Real work waited until everyone else had gone home.

Herd AI, Inc sorted his incoming information against the company's actual objectives and surfaced only what mattered that day.

He was getting his evenings back.

Within weeks, he was finishing his most important work by early afternoon and using the rest of his day for the kind of strategic thinking that late nights never left room for. Matt Francis points to that shift, not as a productivity trick, but as proof that focus is something a system can actually restore.

Fairness Has to Be Engineered, Not Assumed  

Herd AI, Inc touches sensitive territory by design. It reads a company's emails, meetings, and messages and decides what gets priority, which makes fairness a real engineering problem, not a marketing line.

Matt Francis says the answer starts with strict prompt engineering built for repeatable, consistent outcomes, paired with a continuous stream of user feedback. Every customer can see and edit the prompts driving their own instance of the platform, adjusting them until the system treats every employee's signals the same way regardless of role or seniority.

That work does not stop once a system goes live. Frontier AI models change constantly, and Herd AI, Inc's team monitors those shifts to make sure the underlying models it depends on stay fair as they evolve. Matt describes it as an ongoing responsibility rather than a settled achievement.

Throwing Away the Old Playbook  

Matt Francis did not always build this way. Earlier in his career, building software meant handing a design off to a development team and waiting, often for months, only to find the finished product rarely matched the original plan.

Herd AI, Inc's own development looks nothing like that anymore. Matt says the tools his team relies on now handle both the data model and the application interface together, so a change in one no longer breaks the other. The legacy code from his earlier ventures could not make the jump. His team scrapped it entirely and started over, a decision Matt calls difficult but necessary once he saw what an AI-native build process could actually do.

A Platform Other Companies Can Build On  

Ask Matt Francis where Herd AI, Inc is headed and he brings up a problem most AI conversations skip entirely: every company racing to build its own version of the same infrastructure, multiplying data centers and the environmental cost that comes with them.

His answer is not more building. It is more sharing. Matt wants Herd AI, Inc to become a reusable foundation by 2030, a base that other companies build on top of instead of starting from zero every time. Fewer duplicated systems, he argues, benefits everyone, including the environment absorbing the cost of all that redundant infrastructure.

It is a quieter kind of ambition than most AI roadmaps promise: build the foundation once, well, so more companies can use it.

For leaders buried in emails, meetings, and messages with no system to sort through any of it, Herd AI, Inc is live at getherd.ai. Matt Francis and his team offer a live walkthrough of the platform for anyone ready to see what a shared, ranked view of company priorities looks like in practice.

For more conversations with the founders and leaders building the next generation of AI, subscribe to Lead with AI on Apple Podcasts, Spotify, YouTube, or wherever you listen.

Quick Answers  

What is Herd AI, Inc? Herd AI, Inc is an AI-native platform that reads a company's emails, meeting transcripts, and messages, then ranks them against the company's stated objectives so teams know what to focus on first.

How does Herd AI, Inc reduce AI costs? The platform's learning engine scores incoming information with AI until it reaches a high confidence level on a given task, then stops calling the underlying model for routine items. If accuracy drops, the AI comes back online until confidence is restored.

Who is Herd AI, Inc built for? Herd AI, Inc works best for CEOs, COOs, and CROs at companies ready to change how their teams operate, regardless of company size. Matt Francis says the deciding factor is mindset, not headcount.

Is Herd AI, Inc secure? Herd AI, Inc recently completed SOC 2 Type II compliance, a certification Matt Francis says required significant work from the team.

Who is Matt Francis? Matt Francis is the CEO & Co-Founder of Herd AI, Inc. A lifelong technologist, he built the company after watching executives get buried in emails, meetings, and transcripts with no system to prioritize any of it.

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