it does.
The institutional knowledge wealth management firms run on, captured and structured so AI can actually use it.
— the problem
Generic models do not understand how your firm operates. They do not know your clients, entities, funds, workflows, people, or how those elements connect across your existing systems.
Client data in one system. Fund documents in another. Communication history somewhere else. No system has the full picture or understands how they connect and neither does your AI.
Most AI tools remember what you've said. They don't know what your firm knows. A conversation log is not the same as reconciled facts, governing rules, or shared context across your firm.
Without firm-wide context and governing rules, AI can't act safely. Wrong clients reached, wrong actions taken, without the approvals or institutional knowledge to know the difference.
— firmOS
FirmOS gives AI tools and agents the context and connectivity to work inside your firm.
It captures what your existing platforms don't:
Not a replacement for the tools and data you run on. The secure layer that helps AI understand how they work together and act within them.
— what firmOS knows
— how FirmOS learns
Specialized agents run continuously in the background, capturing relevant facts and resolving conflicts automatically. Only genuine ambiguity reaches your team. Only verified information makes it in.
Pulls authoritative entity hierarchies and identifiers from your source platforms, diffs against existing information, and writes confident matches automatically. Documents are reviewed for entities and formatting templates, not raw data. Ambiguous ones queue for human review rather than writing silently.
When two sources disagree, agents evaluate and resolve automatically where the answer is clear. Only genuine ambiguity gets surfaced for human review. Most conflicts never reach your team.
Every fact carries a confidence class, assigned at write time based on source, frequency, and how it entered the graph. Each class decays on its own curve. A fact observed once eight months ago is not the same as one confirmed across 47 agent runs this year. When an agent reads a low-trust fact, it treats it differently than one that has been re-observed and confirmed continuously.
During every conversation and on every tool call output, relevant facts are evaluated and captured in real time. Preferences, corrections, patterns mentioned in passing. Designed to capture the knowledge that lives in your team's heads and can only be learned through time and use.
Your knowledge is always accessible in plain English. Review it, correct it, add to it, or ask why the system believes what it believes.
— how it works
— controls
Your firm's knowledge is sensitive. Control who reaches it, how they reach it, and what they can do with it.
— integrations
Purpose-built integrations with your wealthtech platforms and the everyday business tools your firm runs on. Your knowledge stays complete across your entire stack.
Don't see a system you use? Reach out, our team will work with you to build the integration during our white-glove onboarding.
wealth technology
business & productivity
ai model · via MCP
Connect your firm's knowledge to any major model provider via MCP. Your intelligence layer stays portable across every AI tool you use.
— security
Institution-grade security and control, built for regulated environments from day one. Security isn't a feature at SideKar, it's the foundation everything else is built on.
— why we built this
We've seen firsthand how firms run on knowledge that lives in people's heads, tribal memory that walks out the door, and systems that don't talk to each other.
We believe institutional knowledge no longer has to live only in people. It can become the operating layer of the firm itself. A layer that compounds over time, coordinates work across every system, and gives every person and every agent the full context of the firm behind them.
The future of wealth management isn't another application in the stack. It's the system that makes the entire stack work as one.
— the team
Before founding SideKar, Peter served as Innovation Officer at a single-family office, where he led a full-scale modernization of their technology and operations. His background in architecture and industrial design at the University of Pennsylvania informs his belief that firm knowledge should be structured, accessible, and completely effortless for the people using it.
Hunter has a background in finance and computer science from UNC Wilmington. Before co-founding SideKar, he built and deployed cloud-based systems and reporting tools at a nation-wide engineering firm. His ability to build systems that work reliably inside complex organizations shapes how SideKar captures and structures firm knowledge at scale.
Nathan is an investment and operations professional with experience across both the family office and venture capital space. He currently serves as a Director at a single-family office, where he oversees strategy, investments, and operational workflows. His perspective directly informs how SideKar captures the knowledge and context that investment teams actually run on.
— get started
We'll walk through your firm's structure, tools, and how Sidekar fits into the way you work. Then we'll show you the product live.
Request a demo →Book a 30-minute call with our team. We'll walk you through FirmOS and how it can help your firm use AI.
We'll be in touch within one business day to schedule your demo.
The context layer that lets AI agents execute work safely, accurately, and at the speed your clients expect.
