Who Is Alex Reichenbach?
Alex Reichenbach didn't set out to build a data company. He set out to help a family friend. The friend ran a small bank that couldn't keep up with deal flow — their data engineering was, in Alex's words, "terrible." Alex was working as a robotics engineer at the time. So he did robotics during the day and helped the bank at night. The first thing he built was an automated tracker that scanned millions of websites for portfolio company information. It took him a month and a half.
Years later, he rebuilt the same tracker on Structify, the platform he co-founded. It took less than a couple of minutes.
That gap — month and a half versus a couple of minutes — is the founding story of Structify, the AI data team for every enterprise. It's also the gap Alex spends his days thinking about. Not as a metric, but as evidence that something fundamental has shifted in what a single person can do with data. Structify raised $4.1M from Bain Capital and 8VC to build the infrastructure that makes that shift available to anyone in an organization, not just the data team.
"I want everyone to feel like they have the power of the Citadel data team behind them."
The Archetype: The Magician
The Magician
The Creator
Tests & Allies
Alex's vision isn't a better tool. It's transformation. The Magician archetype is about taking elite capability and making it accessible — not by simplifying it, but by re-engineering who's allowed to use it. When Alex says he wants every operator in an organization to have "the power of the Citadel data team behind them," that's classic Magician territory. The thing that used to require a quant team and a budget now lives in a Slack channel.
The Magician's signature move is also Alex's signature insight: the biggest blocker to AI adoption isn't the technology — it's people's belief about what they're allowed to do. "The biggest thing that is preventing more people from becoming users is just their self-belief that they can do more than what they have been trained to do in the past." A Magician changes what people believe is possible, and the rest follows.
His secondary archetype is The Creator. Alex is a builder before he's anything else. He demos by building, teaches by building, sells by building. Mid-interview, he pulled up a Senator voting pipeline he had built that morning "just thinking out loud" — running it live on screen to show what was possible. The Creator wants to make something that didn't exist; the Magician wants to change what others can do. Alex is both, but the Magician leads — the building is in service of the transformation.
"It's easier to teach someone who knows the business processes how to data engineer than it is to teach a data engineer the business processes."
The Hero Match
Daedalus
Daedalus is the master craftsman of Greek mythology — the engineer who built the wings that gave humans flight. He's not a king or commander. He's the working builder whose distinguishing trait is making tools that extend what humans can do. The wings are the perfect parallel: take an ability normally reserved for the few — flight, in the myth; data analysis, in Alex's case — and engineer it into something a person can use.
The other thing about Daedalus is his obsession with accuracy. He warned Icarus about the wax: fly too close to the sun and the bond fails. That's the same energy as Alex's "if our pipelines are 2% more accurate than our competitors, it makes any bank that doesn't use us negligent." The craftsman knows where the failure mode lives. He builds for the wax to hold.
And Daedalus made the labyrinth — a complex deterministic structure that ran the same way every time. Alex's architectural call to separate the LLM-as-strategist from the deterministic code-as-execution is the same instinct. Intricate underneath; repeatable on top. That's the Daedalus signature: complexity that obeys.
Doc Brown — Back to the Future (1985)
Pre-DeLorean Doc Brown is the version that maps. He's in his garage, surrounded by tools, building things at full speed and explaining them with too much energy. Strong opinions. Respect for accuracy ("88 miles per hour, exactly"). The work itself is the payoff. When Marty arrives, Doc doesn't pitch — he runs the experiment and narrates what's happening as it happens.
That's exactly what Alex does. He pulled up the Senator voting pipeline mid-interview and walked through what the agent was doing while it ran. He demoed the Slack/HubSpot integration on screen. He's the inventor in the workshop, not the executive in the conference room.
"I just spend maybe two minutes thinking about this. I processed multiple hundreds of thousands of bills and just answered a research question that I would've not had otherwise."
The Story Behind Structify
The Founder's Journey ↔ The Company's Journey
Computer vision researcher → Yale single-cell RNA lab → robotics engineer at Magician → night-job data builder for a family friend's bank → realized the gap between mature data tools and brand-new AI capabilities → committed to Structify full-time → two years in, $4.1M raised, finding the real ICP.
A side project automating portfolio tracking for one bank → a generation platform that writes code to query any data source → a Slack-first interface that puts a "data team" inside any company → an architectural commitment to separating LLM strategy from deterministic execution → now expanding into legacy industries that need a data team but can't build one.
The same archetype drives both: The Magician who watched a single person grind for a month and a half to do something that should take minutes, and decided that gap was the whole problem worth solving.
How Alex Leads
Alex leads with architecture, not story. He'll state the structural distinction first — "code is deterministic, agents are probabilistic" or "we separate interpretation from execution" — and then ground it with a story. The principle comes first; the narrative serves it. That's the opposite of how most pitches work, and it's a tell that he trusts the architecture before he trusts the marketing.
His decision-making style is sole on vision, collaborative on execution. He owns founding-era calls in first person — "I built that," "I decided to do this," "I realized selling to data teams was wrong." But once Structify becomes the protagonist, he shifts to "we": "we put a lot of work into accuracy," "we're a generation platform." The pronoun split is clean and not performative — it marks where authorship ends and team execution begins.
He's also comfortable holding strong positions and pushing back. When Angelina coined "vibe data engineering" mid-interview, Alex pushed back: "I think that we dislike trying to fit into the vibe toolings because we put a lot of effort into our accuracy." That kind of disagreement isn't hostile — it's the calm of a founder who's already worked through the trade-off and made his call.
Founder Superpowers
Building Live in the Conversation
Alex doesn't pitch — he opens his laptop and shows. Mid-interview he pulled up a Senator voting analysis pipeline he'd built that morning, demoed the Slack integration with a one-shot HubSpot lead-enrichment prompt, and walked through the SEC EDGAR pipeline running on screen. The throwaway tone is the signal — he's running these queries casually because the tool is real, and that's harder to fake than any pitch deck.
Refusing the Obvious Customer
Alex saw the wrong sales path early and corrected. The natural buyer for a data tool is the head of data — and Alex spent enough time there to learn it doesn't work for him. "We tried to sell to heads of data and I realized that was a terrible sales process for us." His current ICP is COOs, PMs, and operators feeling the pain of data-team backlogs. The discipline is in the update — most founders cling to the obvious customer because the org chart says they should. Alex looked at the data and said no.
Separating Interpretation from Execution
The architectural call that defines Structify: the LLM strategizes and writes the code; the deterministic Polars-based pipeline executes. "It's not a model making the decision." That move gives Structify a defensible edge where pure agent-loop tools can't compete. Multi-agent systems compound errors; deterministic pipelines don't. Alex didn't bolt this on later — he designed the company around it.
What It's Like to Work with Alex
Working with Alex means being in a room where architecture comes first. He'll state the structural distinction before he'll tell you the story, and he expects you to follow the principle into the example. That's not gatekeeping — it's how he thinks. If you want to debate something with him, bring the architectural argument, not the vibes.
He's high-energy when he's showing something he built and measured when he's listening. He doesn't talk over questions; he lets them land. He's comfortable disagreeing — even with the person interviewing him about his own product — because he respects the disagreement enough to engage with it honestly. He'll say "I'm gonna say something that you might disagree with" and then say it.
He owns his calls. When something is his decision, he says "I." When the team executes, he says "we." That clarity makes him easy to read. You always know what's his to defend and what's the team's to ship.
He also cares about user dignity in a quiet way. The recurring theme of "people have been trained to think they can't touch data" isn't just a product positioning point — he means it. The founder of a data tool company genuinely believes the bottleneck to adoption is not the tool but the user's permission to use it. That belief shows up in product decisions: lead with the Slack interface, not the workflow builder. Make the foreign language work. Ask follow-ups in plain English. The product is shaped by who it's trying to reach.
"The biggest thing that is preventing more people from becoming users is just their self-belief that they can do more than what they have been trained to do in the past."
Why This Matters (For You)
If You're an Operator Backlogged by Your Data Team
You're the customer Alex thinks about. The COO whose finance team builds hundreds of Excel sheets and prays the formulas hold. The head of rev ops who waits weeks for a one-off enrichment query. The PM whose ticket has been in the data team queue for two quarters. Alex's position is that the bottleneck isn't your data team's competence — it's that you've been trained to think you have to wait. With the right tool, the M&A doc-scanning that used to take days happens in minutes. The portfolio company tracking that took a month and a half takes seconds. The question Alex would ask you: what request have you stopped making because it always gets shoved to next quarter? Start there. The math has changed.
If You're an Engineer Building AI Agents
Alex's architectural call is worth studying: separate interpretation from execution. The LLM strategizes and writes the code; deterministic infrastructure runs it. That separation is what gives Structify accuracy where pure multi-agent loops compound errors at 5% per step. The deeper lesson is about where to put the LLM in your system. Most builders treat the LLM as the executor — every step is an agent decision. Alex treats it as the planner — the agent figures out the strategy, then code carries it out. Ask yourself: what's the part of my system where I need determinism, and am I letting an LLM make that decision when code could?
If You're Early in Your Career
Alex's path looks haphazard from the outside: computer vision research, single-cell RNA lab, robotics, side project at night, founder. None of it was a planned ladder. What it was, was learning what it feels like when a problem hurts more than it should. The family-friend bank pain wasn't market research — it was a real friend with a real problem. The fact that Alex was working as a robotics engineer at the time didn't disqualify him from solving it. The lesson: build for someone you actually know, on the problem you actually feel. That's the founding story. It tends to be unfakeable, and the catharsis when you fix it is the engine that gets you through year two.
If You're Considering Joining Structify
Structify is for people who care about getting the architecture right. Alex leads through conviction, not consensus, and the conversations he has with his team are about which approach is correct, not which approach is comfortable. The technical bar is high — you'll be working on agent systems, deterministic execution layers, and the metadata layer that lets agents query across snowflake tables, dropboxes, APIs, and PDFs. Precision matters. Demos matter. There's a strong cultural bias toward showing instead of pitching. If you're the kind of engineer who would rather build the working version than write the spec, you'd probably fit.
Go Deeper
The full conversation with Alex Reichenbach is on its way. Check out other episodes in the meantime.
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