Brighthive - Your AI-Powered Data Team 📈

Plus: Co-Founder & CEO Suzanne on how Brighthive’s 0007 Agents are reshaping enterprise data workflows...

March 04, 2025

CV Deep Dive

Today, we’re talking with Suzanne El-Moursi, Co-Founder & CEO of Brighthive.

Brighthive is building an AI-powered data team in a box, designed to help organizations unlock the full potential of their data without requiring a deep bench of engineers or data scientists. By combining six core AI agents with a reasoning-driven orchestration layer, Brighthive automates everything from data ingestion and governance to analytics and visualization. Whether it’s helping state governments make smarter policy decisions or enabling mid-sized businesses to tap into insights previously out of reach, Brighthive aims to make enterprise-grade data intelligence accessible to everyone.

Since its founding, Brighthive has empowered a range of customers—including government agencies, manufacturers, higher education institutions/universities, supply chain and energy companies—to make faster, more informed decisions. Its latest product, Brightbot Studio, allows non-technical teams to build their own AI agents, turning data consumers into data creators and redefining how organizations interact with information.

In this conversation, Suzanne and Matt share the origins of Brighthive, how reasoning AI is reshaping enterprise data workflows, and why joy in work might become the most important KPI of the AI era.

Let’s dive in ⚡️

Read time: 8 mins

Our Chat with Suzanne 💬

Suzanne - welcome to Cerebral Valley! First off, introduce yourself and give us a bit of background on you and Brighthive. What led you to co-found Brighthive?

Brighthive is headquartered in Chicago, and I’m the co-founder and CEO alongside Matt Gee, our CTO. What got me excited about this opportunity was stepping back and realizing that, despite all the technological advancements we’ve made—from electricity to the internet, the miniaturization of computers, the app economy, EVs, and even Mars exploration—the fundamental problem of data remains unsolved. Every modern technology generates massive amounts of data, yet we are nowhere near a reality where people start their day making data-informed decisions. 

The vision Matt and I share is that anyone—regardless of their role—should be able to access and use data before making decisions, recommendations, or taking action. The problem is accessibility. The technical on-ramp for data—cleaning it, making it usable, and structuring it in a way that teams can actually work with—is still incredibly difficult, even with unlimited resources. That’s what got me excited when I met Matt. Coming from software and him from data, we both saw that even with all these innovations, data fluency and maturity within organizations rank incredibly low. The question became: can software solve this? Can AI solve this? Given that we generate more data than we can process daily, it’s a problem that needs fixing. 

For my co-founder Matt Gee, it’s always been about bringing high-capacity data capabilities to organizations that don’t have the resources to hire a full-stack data team. Matt is an impact entrepreneur and deeply cares about the use of data for impact and social good, solving societies hardest problems from a data point of view. He ran Data Science for Social Good for years, and saw firsthand that while PhD-level data scientists were optimizing ad clicks, organizations with massive challenges had almost no access to that talent. Less than 1% of organizations can afford a full data team—data engineers, analysts, governance experts—so the big question became, can we automate certain workflows? Could we make it low-code and lower the barrier to entry?

Our mission for why we are applying agentic AI in this way and building Brighthive is because we want to transform work in all companies. We see a future where every day work becomes data informed work. We want to make everyone in any organization, regardless of their role and technical skills, become equipped to be a data analyst and is fully capable to answer the first question, “what does the data say?” on their own. Brighthive will change people’s feelings of hesitation, and anxiety around working with data, to feeling a sense of delight and greater confidence.

We got lucky that two major shifts happened around the same time. First, the data stack matured. It became more programmable and composable—you no longer needed to rely on drag-and-drop interfaces for visualization. You could implement headless BI, compose data contracts with open standards, and define data quality rules with frameworks like Great Expectations. As the stack evolved, it became possible to program against not just data, but its entire orchestration.

The second shift was the rapid improvement of LLMs. We saw the trajectory and took a bet: if these models continued improving, we could build AI agents capable of handling complex data tasks. If LLMs got good enough at reasoning, and we paired them with a composable data infrastructure, we could give every organization a data team—AI agents that could perform data governance, engineering, and analysis at a level that would bring everyone up.

That was the bet we placed. We spent years working on the composability layer without knowing if LLMs would improve fast enough. Fortunately, they did. We hit a stroke of luck last year as people started realizing that reasoning at scale was becoming mathematically possible. As entrepreneurs, you don’t get many moments where you’re solving a difficult problem with massive benefits for humanity—and now that it’s technically possible, the challenge is just about execution and moving fast.

How would you describe Brighthive to the uninitiated developer or AI team?

In an AI-enabled era, Brighthive is a system of seven AI agents that work through the entire data management and analysis workflow. No matter where you sit in an organization—whether it’s supply chain, customer success, or engineering—you have a complete end-to-end platform at your fingertips. It takes in all the data assets you want, and the agents process them from raw inputs all the way to actionable insights.

We’re handling everything—spreadsheets, unstructured data, structured data, flat files—whatever the input, our agents do the heavy lifting to translate data into insight. We’re making substance out of the mass. Our key metric is accelerating time to insight. No longer do strategy, marketing, or sales teams need to wait on the data team to crunch numbers and generate reports. Of course, official reports will still be issued, but now anyone in the enterprise can load data into the Brighthive platform  and trust that Brighthive’s agents will handle everything—cleansing, governance, pipeline creation—and deliver visualized insights instantly.

That’s incredibly powerful because it shifts how business teams operate. Instead of waiting for reports, they can see insights in real-time and have more informed, data-driven conversations. It’s essentially an AI data team in a box. You can plug it into your existing data warehouse and hit go.

Who are Brighthive’s core users today? Talk us through which companies or roles are finding the most value in Brighthive’s product. 

Matt and I come at the why we should solve this problem from two different perspectives. Matt’s career path is among data engineers and data scientists—his community, his tribe. These are highly skilled, fluent, and capable professionals. But for me this is much much bigger. There’s another group that benefits just as much from what we’re building: the data consumer. That’s everyone else. Anyone who isn’t fluent in modern data tools, who doesn’t know their way around the data stack, and who isn’t sure where to even begin when trying to use the data their organization generates. I spent 16 years of my career before becoming an entrepreneur in 2012 working in multiple large enterprises. IBM, GE HEALTHCARE, HSBC, and as a technology consultant with clients such as Bank of America, State Farm, Sprint, Target to name a few.

The big corporate culture is bloated with siloed functions and specialized teams, each possessing rich data assets, which they could not unlock for insights easily and on their own. I witnessed so much workflow and process orchestration happen in absence of insights and decisions made not always evidenced from data. I want to change this and see a future where we apply this POWERFUL agentic AI frontier to make everyone, in any role, become their own data analyst and steward. Whichever way you look at the why, Matt and I have deep conviction that we all win because humanity becomes much more data informed.

The modern data stack is both a blessing and a curse. It’s powerful, but it’s complicated. There’s a tool or a platform for every small innovation in managing data, which leaves business teams—strategy, marketing, sales—completely lost when it comes to accessing insights. Our go-to-market strategy targets organizations where data is abundant, but data maturity is low. Some of our early users include companies where the entire data team consists of a CIO and a single analyst. These organizations are rich in data but haven’t built a fully managed data stack.

From our perspective, the middle market is a massive opportunity. In the U.S. alone, there are 35,000 organizations with between 500 and 5,000 employees, representing a $4.6 billion market. These companies need data capabilities, but they don’t have the resources for full-scale data teams. Brighthive gives them an end-to-end AI-powered data team at a fraction of the cost of a single data engineer, while also democratizing data across the organization.

How are you measuring the impact you’re having on your earliest customers? What signs are you seeing that signal Brighthive is adding value within their data workflows?

Our early customers have been organizations with large data volumes but little capacity to actually use it. State governments are a perfect example. The State of Virginia is one of our customers, and they had a massive amount of public data but, at the time, only one person in the Chief Data Officer’s office. They serve millions of people, but their internal capacity was minimal. Brighthive allows organizations like that to activate their data, making it usable for decision-making without requiring a massive in-house team.

The same is true for mid-sized manufacturers. Many of them have highly instrumented facilities, collecting huge amounts of data on production, supply chain, and performance. But their core business isn’t data—it’s manufacturing. Unlike large financial institutions, they don’t have in-house data teams, yet they often generate as much data as a major bank. We see a huge opportunity in helping them tap into those insights without needing to build out an entire data department.

What excites us most is unlocking immediate value in industries that have traditionally been left behind in the data revolution. Instead of waiting for their infrastructure to catch up, these companies can start extracting insights today.

Walk us through Brighthive’s product. What use-case should enterprises experiment with first, and how easy is it for them to get started? 

When you log into Brighthive, the first thing you do is connect it to your existing data infrastructure. Whether you have a data warehouse, an ETL tool like Airbyte or Fivetran, or you’re using DBT for data versioning, Brighthive is designed to integrate seamlessly with your existing stack. For organizations that don’t have a fully built-out data environment, we also offer a managed version that provides a best-in-class data stack out of the box. Many of our customers start with their data sitting in a basic Postgres database, and they need a way to plug into a more performant, composable system.

From there, the home base presents the entire data workflow in a clean interface with intuitive buttons and a chat prompt for engaging with our main AI supervisor agent, Brightbot. Brightbot is the central interface, and you can start by simply asking, “Tell me something interesting about my data.” Under the hood, Brightbot functions as a supervisor agent that delegates tasks to six specialized sub-agents, each responsible for different aspects of the data workflow.

When a user asks an open-ended query, one of the first agents Brightbot engages is the data strategy agent. This agent takes in the business context of the user by building an organization-specific knowledge graph—a structured understanding of the company’s key strategic priorities. It pulls from sources like strategy documents, websites, pitch decks, and board reports to develop a narrative about what’s important for different business units.

Once the strategy agent determines the five most important focus areas for the business, Brightbot hands off the query to the exploratory analytics agent. This agent scans the data catalog, retrieves relevant data based on the strategic context, and performs queries against the warehouse. It then generates a Jupyter notebook with exploratory analytics tailored to the user's query.

For example, the exploratory analytics agent might identify an interesting trend—such as a spike in new users on Tuesdays and Thursdays. It performs additional queries on the user logs table, identifies patterns, and ultimately determines that there is a meaningful insight to report. It then sends its findings back to Brightbot, which summarizes the results:

"We found three key trends in your user data. One of the most notable insights is a spike in new users on Tuesdays and Thursdays. You might want to investigate what’s driving this pattern."

A user might then realize, "That’s when we send out our biweekly newsletter." They can continue the conversation by asking Brightbot to generate a visualization of the trend. Brightbot then delegates the request to the data visualization agent, which generates a fully rendered, shareable dashboard using Evidence.dev, an open-source, headless BI solution.

The user can go back and forth, refining the visualizations, and once they’re satisfied, they can generate a shareable Evidence dashboard to send to their team. Instead of waiting weeks for an analyst to dig through the data—or worse, never uncovering the insight at all—this entire process happens in a matter of minutes.

For teams without dedicated data analysts, the time to insight could previously have been infinite—because they never would have caught the pattern. Even for teams with a single overworked analyst juggling multiple tasks, this kind of discovery might have taken two weeks. With Brighthive, it happens almost instantly, making powerful data insights accessible to any team, regardless of their technical background.

Which existing use-case for Brighthive has surprised or delighted you the most? 

One of the most impactful features we’ve added to our product is Brightbot Studio, which allows users to customize and extend our six core agents for specific business needs. Once Brighthive has processed and structured the data, teams can create their own specialized AI agents to interact with different departments—whether it’s sales, operations, or customer support.

A recent customer ran an internal hackathon to showcase this. They took 40 non-technical employees—no engineers, no data scientists—and gave them access to Brightbot Studio. With their company’s data now AI-ready, they broke into seven teams and built custom AI agents tailored to their workflows. At the end, they held a show-and-tell to present what they created, and the results were remarkable. Employees who previously had no direct access to data insights were now empowered to build their own AI-driven tools simply by providing prompts to Brightbot.

This kind of hands-on experience is what turns AI from a niche tool used by a few gatekeepers into something that democratizes access to insight and automation across the entire organization. Instead of us needing to sell them on AI’s potential, these teams became internal champions for change, excited about how they could transform their own workflows.

It’s similar to what Steve Jobs did with the iPhone—no one asked for it, but once they saw it in action, they realized it was exactly what they needed. The same thing happens every time we introduce Brighthive. No one comes to us saying, "Do you have a data team in a box with seven AI agents?" That’s not how people think. But when they load their data, step into the sandbox, and see what’s possible, they instantly become believers.

Even traditional product demos don’t fully capture the power of this transformation. People need to experience it themselves—to go from feeling like the “data outsider” in their company to being the person who discovers and shares game-changing insights.

We think this isn’t just about efficiency—it’s about redefining joy in work. People will start loving their jobs in a way they haven’t before because they’re empowered with data, without fear or intimidation. Instead of avoiding data work, they’ll embrace it as a way to shine, impress their teams, and make smarter decisions.

Ultimately, this shift will become a new key performance indicator (KPI)—one that measures how much people actually enjoy their work because AI is helping them do it better. I am starting a new barometer around measuring the delight and joy in our work. As we get everyone discovers the art of the possible with Brighthive at the core of their data informed work, we will be measuring everyday work being a greater source of pride for each employee.

How do you see Brighthive evolving over the next 6-12 months? Any specific developments that your users/customers should be excited about? 

Our primary focus over the next six months is increasing the reasoning capabilities of our agents against core benchmarks. The workflow we described earlier is only possible because of reasoning agents—AI systems capable of internal and external research, decision-making, and insight generation. This is a significant leap from traditional automation because these agents aren’t just executing pre-defined tasks; they’re exploring open-ended questions, a challenge known in AI research as open-endedness. OpenAI has an entire team focused on this, and much of the progress in reasoning agents is being driven by advancements in this field.

The key challenge is teaching AI how to explore a space of possibilities, filter relevant insights, and determine what’s most important. Last year, our agents struggled with this—they could execute queries but failed to surface novel insights. The difference between traditional AI tools and true reasoning agents is that data work isn’t a simple input-output problem. It’s an iterative process: you take business context, test a hypothesis, get feedback, adjust, and repeat. Unlike generating code or writing text—tasks that earlier AI systems handled well—data workflows require constant reasoning at every step.

Over the next six months, we aim to demonstrate that every improvement in reasoning agents directly translates into better performance on internal and external benchmarks for complex data tasks, like data governance. This is an incredibly intricate problem that, frankly, was impossible to tackle effectively last year. But with recent advances, our models can now handle it with much greater fidelity.

Given the excitement around AI Agents, how does this factor into your product vision for Brighthive? 

Each of our six core agents operates as its own multi-agent system, using multiple models for different tasks. For example, our data engineering agent relies on a specialized LLM trained for high-fidelity SQL generation, while a separate reasoning agent decides how to structure an entire DBT project. We’re constantly testing and optimizing these models, comparing performance against the latest releases from groups like O1 and Deep SEQ R1. Given the current pace of AI advancements, new reasoning models are improving almost weekly, and we are integrating those improvements in real time.

To put this in perspective, each Brighthive agent is essentially an AI system in itself. If you compare our approach to competitors like Number Station, which focuses primarily on analytics and querying, the difference becomes clear. Brighthive isn’t just about querying data—it’s about end-to-end data intelligence. Our agents don’t just generate reports; they build, analyze, and apply reasoning across entire workflows.

This wasn’t even possible a year ago. In February last year, no one was even talking about AI agents in the way they are today. We took a risk by betting that reasoning models would rapidly improve, and that bet has paid off. At the time, the technology wasn’t good enough to roll out to customers—it wasn’t accurate or reliable enough for high-stakes business decisions. But we knew that reasoning would improve, so we built against that future.

Now, that future is here. And at the rate things are progressing, we’re not talking about next year—we’re talking about next quarter.

Lastly, tell us a bit about the team at Brighthive. Are you hiring, and what do you look for in prospective members joining Brighthive? 

Our team of thirteen (13) is incredibly global and diverse, with members from Singapore, Egypt, Pakistan, Costa Rica, India, and more. They’re not just based in Chicago—where our headquarters is located—but spread all over the world, helping us build this. The diversity of thought and cultural perspectives they bring is invaluable. Diversity of thought creates the alpha. This Brighthive represents the best of what the U.S. has to offer while also empowering young engineers in other parts of the world where technology isn’t typically being built.

There’s a level of hunger, commitment, and conviction in our team that’s truly exciting. Because we operate as a global Brighthive, our team members can contribute from wherever they are. When we come online in the afternoon, it’s 3 PM in Cairo, and they’re just starting their day. They work late into the night, solving problems while we sleep. We wake up to new solutions and fresh perspectives. That constant cycle of innovation and dedication is what makes this team so unique.

It’s an amazing time to be a builder. Some days, it feels like we’ve been given magic powers. Of course, it’s not magic—it’s just math—but the whole team feels that sense of delight and pride. We’re building something that changes the way people work, and that impact is tangible.

Culture isn’t something you dictate—it develops over time and is deeply influenced by leadership. The joy in what we’re building is real, and you can’t overstate the power of that. What’s special is that we’re not forcing a joyful culture—it’s something the team brings on their own. I have always been deeply committed to an important belief of mine; if a member of any team I lead has to leave, they must leave bigger than how they started with us. I say this to every new employee on their first day. They always pause and they process that. They get excited but you can tell they are uncertain. At every chance I get, I will Sleck them a messaging sharing with the growth that I am observing for each one of them and for the team as a whole. They are so deep in the work that they don’t see it the way that I do from where I sit. This all builds trust, authenticity, commitment and bonds us tightly to achieve our mission. We are transforming work to become data informed work for everyone.

We see it in how our engineers pair up, how ideas flow across teams, and how every day they log in with more ideas than we can even execute. They’ve created a natural, organic connection among themselves, with young engineers collaborating across time zones, working together across thousands of miles in a way that is additive to each other’s work. In our headquarters, you will find a Chicago based engineers working on a zoom call for 2-3 hours with another engineer in Cairo when it’s past 11:00 p.m. in there. This is something we don’t ask for and are so humbled to witness. The deep level of commitment to the mission. That kind of ownership and passion is rare. They’re not just doing this because we’re a seed-stage Brighthive trying to build a venture-scale business. They’re doing it because they believe the world needs this. Of course, they hope it becomes a sustainable, investable Brighthive, but at its core, this is about solving a real problem that matters. 

And yes we are most certainly hiring across all the functions! This year is an incredible growth year for us as it’s been known to the year of the agents. We don’t only look at technical fit (engineering or otherwise, everyone has a technical fit to the role). We have learned the importance of cultural fit. We’re looking for passionate builders. Those who have an intrinsic hunger to learning, who think about this work as their legacy and their best days. Where they believe in the mission of transforming work to become bring us joy and be a greater source of pride; propelling humanity to be much more informed; changing how our future can look because the societies and the systems around us are shaped differently simply by making data easy to understand and data work enjoyable.

Joining us to build Brighthive is about an impact for all. Our board has some of the top investors in the country who care that their investment is delivering impact to humanity. They back us up in our belief that we can apply AI in an incredibly powerful way to advance people individually and collectively to a brand new world of possibilities.

Conclusion

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