Our Story

Walk into any successful enterprise and you'll find the same thing: layers of specialists. Financial analysts building models and stress-testing assumptions. Legal teams reviewing contracts clause by clause. Compliance officers tracking regulatory frameworks across jurisdictions. Research teams synthesizing market intelligence into actionable insight.

Each person brings years of training and domain-specific intuition to their work. They don't just have information. They have judgment refined through repeated exposure to the patterns of their field.

Now walk into a growing mid-market company. You'll find talented people, but they're stretched. The CFO is also handling investor relations. The operations lead is managing HR. A single in-house counsel is covering everything from employment law to commercial contracts. Everyone is doing three jobs because hiring dedicated specialists for each function isn't realistic yet.

This gap between the expertise organizations need and the expertise they can access shapes almost every business decision. It determines which opportunities get pursued and which get passed over. It influences the quality of analysis behind major investments. It affects how quickly companies can respond when industries shift.

We started Cyrenza because we believed this gap was solvable. Not by replacing human expertise, but by making specialist-level AI available within the specific industries where that expertise matters most.

The question wasn't whether AI could perform business tasks. By 2024, that was settled. Large language models could write, analyze, summarize, and reason. The real question was whether AI could work the way professionals actually work within their specific industries.

A good financial analyst doesn't just run numbers. They know which assumptions matter most for a particular deal type. They understand how different stakeholders will read the same projections. They've seen enough transactions to sense when something doesn't add up, even before they can prove it.

A good contract attorney doesn't just spot legal issues in the abstract. They understand the business context of the deal. They know which terms are worth fighting for in a software license versus a construction agreement. They anticipate how a clause might play out three years from now in a dispute no one is planning for.

This kind of expertise isn't just about information. It's about pattern recognition accumulated over years of practice within a single domain. It's about judgment refined through repeated exposure to real situations in a specific field.

Could AI develop something similar? We thought so. But only if we built it differently than the generalist tools the market was producing.

Most AI tools are generalists. They're designed to handle any question about any topic with equal confidence. Ask about tax implications, they'll give you an answer. Ask about lease terms, they'll give you an answer. The problem is that the answers have no depth. They read like someone looked something up rather than someone who actually practices within the field.

We took the opposite approach. Instead of one AI that does everything adequately, we built many AIs that each do one thing exceptionally well within a defined professional domain. We call them Knowledge Workers.

Each Knowledge Worker is designed for a specific professional function within one of eight industry verticals. A financial analyst that understands how to read balance sheets, build projections, and stress-test assumptions according to the standards of that field. A contract reviewer that knows what terms create risk in different deal types and how to flag issues that aren't obvious to non-specialists. A compliance specialist that tracks the regulatory frameworks relevant to a particular industry and can assess where an organization stands.

We've built eighty of these specialists across finance, legal, residential real estate, commercial real estate, insurance, consulting, marketing, and business operations. Each one understands the vocabulary, standards, and patterns that matter within their vertical. They're not generalists pretending to have expertise. They're specialists that work within the domains they were built for.

You open Cyrenza and start a conversation. You explain what you're working on. Maybe you need to analyze a potential acquisition, review a set of vendor contracts, or understand how a new regulation affects your operations within a specific industry.

A Knowledge Worker picks up the conversation. Not a generic assistant, but the specialist whose domain matches your work. If they need more context, they ask. If you've uploaded relevant documents to your Vault, they reference them. Then they get to work.

The Vault is where your documents live. Financial statements, contracts, market research, internal memos. Whatever context is relevant to your professional work. You organize it however makes sense for your team. When Knowledge Workers need to reference something, they pull from what you've provided rather than guessing or generating information that sounds plausible but isn't grounded.

As Knowledge Workers complete tasks, the outputs are saved to your Records. An analysis becomes an artifact you can reference later. Sources are tracked and linked. If you asked for a contract review last month and need to revisit it, you'll find it organized and accessible, not buried in a chat history.

The whole system is designed around how professional work actually happens within each industry. You have a conversation with a specialist. Work gets done at the depth the domain requires. Outputs are preserved. Context accumulates over time rather than starting from scratch with every session.

When we say a Knowledge Worker specializes in finance or legal or real estate, we mean something specific about the depth at which they operate within that vertical.

A financial Knowledge Worker understands that a DCF model is only as good as its assumptions. They know how to stress-test projections and identify which variables have the most impact on a particular deal type. They can read a set of financial statements and flag anomalies that deserve investigation. They understand the difference between GAAP and IFRS and why it matters for cross-border analysis within the finance vertical.

A legal Knowledge Worker knows how to parse contract language and identify terms that create risk within the context of a specific transaction. They understand that an indemnification clause in a software license operates differently from one in a construction agreement. They can flag non-standard terms that might require negotiation and explain why they matter in the context of that particular deal type.

A real estate Knowledge Worker understands lease structures, cap rate analysis, and property valuation methods as they're actually applied within the industry. They know the difference between triple-net and gross leases and what that means for a specific investment thesis. They can analyze rent rolls and identify occupancy trends that affect asset value.

This isn't about having access to more information. It's about understanding how professionals within each field think about problems. The same set of facts looks different to a lawyer than to a financial analyst. We built Knowledge Workers that reflect those differences because the differences are what make expertise valuable.

We're deliberate about how we position what Cyrenza does. Knowledge Workers are not replacements for human professionals. They're specialists that extend what human teams can accomplish within their industries.

A Knowledge Worker can analyze a contract and flag potential issues. The decision about whether to push back on a term, how hard to negotiate, and what tradeoffs to accept. That still requires human judgment informed by organizational context, relationships, and priorities that no AI can fully understand.

This isn't false modesty. It's an accurate description of what the technology can and cannot do today. AI is exceptionally good at processing information, identifying patterns, and applying frameworks consistently within a defined domain. It's less capable at navigating ambiguity, understanding organizational politics, or making judgment calls where the answer depends on values and priorities rather than analysis.

We designed Knowledge Workers with this boundary in mind. They show their reasoning. They cite their sources. When they're uncertain, they say so. They're built to support human decision-making within each vertical, not to bypass it.

Cyrenza is an early-stage company building something we believe will matter for every industry we serve. We're not a finished product. We're a team that ships improvements every week based on what we learn from professionals actually using the platform within their specific verticals.

Some of our most useful features came from watching how people work within their industries and noticing friction we hadn't anticipated. The platform today is meaningfully better than it was six months ago, and it will be meaningfully better six months from now.

We work closely with customers in each vertical because that's how we figure out what to build next. If you're evaluating whether Cyrenza could help with the professional work you're already doing within your industry, the best way to find out is to try it. We'd rather show you than convince you.

We believe the organizations that thrive over the next decade will be those that figure out how to work with AI effectively within their industries. Not by automating everything, and not by ignoring it, but by finding the right division of labor between human judgment and machine capability within each professional domain.

We believe access to specialist expertise shouldn't depend on how many people you can afford to hire. A ten-person firm should have access to the same quality of financial analysis, legal review, or market research as a Fortune 500 company. The barrier should be whether you know what your industry needs, not whether you have headcount.

We believe AI should be transparent about what it's doing and honest about its limitations. Technology that asks to be trusted blindly isn't trustworthy. Technology that shows its work and invites scrutiny is.

That's what we're building. That's why Cyrenza exists.