Your AI team isn't doing AI. They're rebuilding infrastructure.

You know the story. Someone on your team builds a Streamlit chatbot over a weekend. They feed it your research documents, hook it up to OpenAI, and boom – it works.

It actually works. You demo it to leadership. Everyone gets excited.

"This is it. This is our AI system."

Except that it isn’t.

Six months later, you realize that the weekend POC is nothing like what you actually need. Context management doesn't work at scale. The AI hallucinates in production. It can't handle complex workflows. Data security is a nightmare. You don't have audit trails. And your best engineers? They're still in the weeds, wiring APIs, debugging context windows, building data pipelines, handling compliance requirements. They're not doing AI innovation. They're stuck doing foundational infrastructure work that every other firm is also doing.

And guess what?

Our OpenBB Copilot started exactly like that. But that was more than 2 years ago.

Don’t believe me? Look at this demo in December 2023, where we already had an AI chatbot integrated with the workspace that was able to handle structured and unstructured data.

The difference?

We didn't stop there. We spent the next years, and a couple of $M in R&D, learning what it actually takes to go from "fun experiment" to "production system that financial teams can trust".

We learned the hard way what a CTO actually needs when you're operating at scale: sophisticated context management, proper data governance, audit trails, security that doesn't compromise workflow, AI that's genuinely agentic and not just chatty.

The foundation problem

Most teams approach AI the same way: start from zero, wire together APIs, paste some prompt engineering on top, and hope it scales. This works until it doesn't. You're managing context windows, debugging agent hallucinations, building retrieval pipelines, handling data governance, integrating with proprietary systems - essentially rebuilding the same foundational plumbing that hundreds of other firms are also rebuilding in parallel.

It's a massive waste of engineering effort.

Getting from "ChatGPT chatbot" to "production AI system that actually works" takes 24+ months of serious engineering work. Most teams don't have that. More importantly, your best quants and engineers get pulled away from high-value work, actual research, trading strategy, portfolio analysis, to build foundational infrastructure.

That's not an investment; it's opportunity cost.

Every month your top engineers spend on plumbing is a month they're not generating alpha or building your competitive advantage.

I think there's a better way.

What we're offering enterprise clients

For our largest enterprise clients (above a certain seat count or contract size), we're providing source-available access to OpenBB Copilot. This is our AI system - the one we've spent over two years and a couple of $M in R&D building.

You get the code. You get the architecture. You get two years of hard-earned lessons about what actually works in production.

Here's what that means in practice:

1. You skip the 24-month foundational build

Instead of your team spending the next two years reinventing context management, agentic patterns (e.g. RAG), output validation, reasoning, citations, structured with unstructured data, URLs, MCPs, and data governance, they get a proven system from day one.

They can inspect the code, understand the design decisions, and extend it for your specific use cases. That's 24 months of engineering time you just reclaimed.

2. You get a system built for real-world investment workflows

Copilot wasn't designed in a lab - it was built to handle actual problems: connecting to multiple data sources, maintaining context across complex reasoning chains, enforcing data entitlements, handling compliance requirements, producing outputs that fit into real decision-making.

The architecture treats AI as a first-class component of a workflow, not a chatbot afterthought. Features like sophisticated context management (understanding which data sources matter most), dynamic widget parameter modification (automatically adjusting queries based on your needs), step-by-step reasoning visibility (so you can audit how the AI arrived at answers) and citations (even at the document sentence level) make the difference between a toy and a tool you can actually trust.

3. You can customize and own it

Because you have source access, you're not locked into our roadmap or dependent on our support schedule. Your team can extend it, integrate it with proprietary systems, swap out models, add your own data sources – whatever your strategy requires.

You build your secret sauce on top of a solid foundation instead of building the foundation itself.

Damn, you can even rebrand it internally to “Not OpenBB agent”.

4. You're not starting from zero on security and governance

Enterprise AI is risky. Data leaks, compliance violations, audit trails - this stuff matters. Copilot was built with the workspace enterprise requirements from day one: on-prem deployment options, SSO, role-based access control, SOC2 compliance, proper data entitlement enforcement. Your team isn't reinventing the security wheel; they're extending a system that was built to be secure.

5. Out-of-the-box financial environment designed for it to thrive

Here's what most AI companies miss: a copilot is only as good as the environment it operates in. Copilot doesn't sit in isolation. It lives inside OpenBB Workspace, a purpose-built financial analysis platform with sophisticated context management, dynamic widget systems, parameter linking across dashboards, and native integration with your proprietary data sources.

This means Copilot understands the structure of your analysis – which data matters, which widgets are active, what parameters your team is using.

It's not just a chatbot bolted onto a generic system. It's an AI agent that's designed to orchestrate across your entire analytical environment. Your team gets access to the full capability stack: visualization, data integration, workflow automation, and AI reasoning - all built to work together.

Why this is the best bootstrap available

The core insight is simple: you can't innovate on the foundation while you're building it. Your best engineers get stuck doing plumbing. Your timeline stretches. Your cost balloons. And by the time you finish, the market has moved on.

With source-available access to Copilot, your team can start from a foundation that's already been battle-tested by real investors and quants using it in production. They can focus on what makes your firm different: your data, your workflows, your intellectual property - instead of rebuilding what every other firm is also rebuilding.

But there's another advantage: OpenBB continues investing heavily in the foundational layer of Copilot (and workspace).

We're pouring capital into making it better, faster, and smarter - exactly as we have for over two years.

That ongoing investment benefits you directly. Your team gets access to improvements, new capabilities, and deeper financial domain understanding without having to fund the R&D themselves.

And because we're working across multiple firms and use cases, we're continuously learning what financial teams actually need to get done - which means the platform evolves with the industry, not in isolation. For instance, we were the first workspace to support MCP server integration.

This is how AI actually succeeds at investment firms: not by buying a generic tool, not by building from scratch, but by starting with proven infrastructure and layering their own innovation on top.

The firms that move fastest aren't the ones with the biggest budgets for AI, they're the ones that don't waste their budgets on foundational problems that have already been solved.

Source-available Copilot is that solve.

Your team gets over two years of R&D and a production-grade system. They focus on making it uniquely valuable for your investment process.

Everyone wins.

The question isn't whether your firm will use AI - it's whether you'll spend the next 24 months building what already exists, or the next 24 months actually innovating with it.

PS: Last week I shared an open source agent that I built over a weekend, our copilot is that + years of hard work. But if you want to get a glimpse to what you would get, I recommend starting from here: https://didierlopes.beehiiv.com/p/introducing-osor-agent

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