USE CASE · FINANCE

Built for the operators generic finance tech was never built to serve.

Independent RIAs, boutique wealth managers, family offices, emerging fund managers, private credit shops, and vertically integrated capital operators all make the same caliber of decisions as the largest institutions, without the research desks, technology budgets, or infrastructure built to support them. Oz builds the systems that let them operate at that level.

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The way capital gets managed has changed. The tooling still assumes it hasn't.

Wealth management, capital allocation, and investment operations used to be defined by access. Access to research, to deal flow, to information asymmetry, to the relationships that closed a fund. Institutions had it. Everyone else waited for it to trickle down. That model is ending.

Today an independent RIA, a solo family office CIO, or an emerging fund manager runs on a stack that was assembled from tools built for a different scale of operation. A portfolio management system built for a wirehouse advisor with 400 households, not an allocator running concentrated positions across custodians. A CRM designed for insurance salespeople, not for tracking sub-doc status across 60 LPs in a first fund. A research process that means reading the same public filings and sell-side notes everyone else is reading, at the same time, with the same conclusions. Meanwhile, the institutional side has spent the last five years quietly wiring proprietary intelligence layers underneath everything they do — internal LLMs trained on their own memos, automated monitoring across their entire portfolio, systematic pattern recognition across decades of deal history. The gap is not access anymore. The gap is infrastructure.

The future of independent capital is going to look nothing like its past. The operators who dominate the next decade will not be the ones who buy the same third-party research everyone else buys or subscribe to the same alt-data feeds. They will be the ones who build proprietary intelligence into their own operation — models that know their portfolio, agents that watch their positions, systems that generate their memos and reports in their voice, on infrastructure they control, with client data and investment thesis material that never touches a shared vendor environment. The tooling to do this used to require an institutional balance sheet. It does not anymore. Independent operators can now run analytical infrastructure that was, until recently, only available to firms with hundreds of employees and a nine-figure technology budget. Oz is how you build it.

CAPABILITIES

Systems designed around how your shop actually invests.

Every Oz engagement starts with how your shop already operates — the specific mandate you run, the diligence process that defines your edge, the client communication cadences that keep capital in the door, the internal handoffs, and the exceptions. There are no templates. There are no forced workflows. There are no guardrails on what can be built. If you can describe it, Oz can build it, deploy it on infrastructure you control, and hand it back to you to run. The examples below are patterns we have built and can build again. They are not the ceiling.

  1. Portfolio monitoring and alert agents that read your positions in real time.

    Agents connected to your custodian, prime broker, or internal position ledger, monitoring every holding against the specific risk thresholds, concentration limits, and mandate constraints that define your strategy. Alerts fire when a position drifts outside guardrails, when correlation breaks down, when a name in the portfolio hits news that matters, or when a covenant on a private position gets tripped. Not generic market alerts. Position- specific intelligence, calibrated to how your shop thinks about risk.

  2. Diligence agents that read filings, transcripts, and internal memos against your investment framework.

    Agents trained on your firm's investment philosophy, prior memos, and pattern of decisions, deployed inside your own environment. Feed them a 10-K, an earnings call, a data room, or a competitor's public filings, and get analysis grounded in how your team already thinks about opportunity and risk. Not a generic summary. A first-pass diligence memo written in your firm's voice, flagging the specific angles your process cares about.

  3. LP reporting, client communication, and capital call automation.

    Agents that pull from your portfolio, transaction history, capital deployment, and performance data, and generate quarterly LP letters, capital call notices, tax packages, ad-hoc client reporting, and internal partner updates in your firm's format and voice. Reporting cycles that used to consume a partner's month move to review-and-send. Client communication that used to slip into "we'll get to it next week" becomes a system that runs on its own schedule.

  4. Research infrastructure that turns your own body of work into a searchable, queryable knowledge base.

    Every prior memo, every diligence file, every rejected deal, every post-mortem, every earnings model, every internal thesis document — indexed, embedded, and queryable through natural language, deployed inside your own perimeter. When a new opportunity walks in, your team can instantly surface every prior analog you've ever looked at, every reason you passed or invested, every framework you've applied to a similar situation. Institutional memory that used to leave when a partner did stays inside the firm.

  5. Private AI, deployed on infrastructure you control.

    This is the architecture that separates Oz from every generic AI vendor selling into wealth management, fund operations, and capital allocation. Every model, every agent, every workflow can be deployed on the firm's own infrastructure — on- premise servers, a private cloud tenant, or a dedicated VPC the firm controls end-to-end. Options include self-hosted open-weight models (Llama, Mistral, and finance-tuned variants) served through vLLM or Ollama, or serverless GPU on Replicate, RunPod, or AWS with proper isolation. Retrieval- augmented generation against the firm's own memos, models, prior transactions, and internal research, so responses stay grounded in your actual investment framework and your actual portfolio, not the vendor's training set. Every request logged, every model version pinned, every data flow audited. Client account data, investment thesis material, deal terms, and proprietary research never leave the firm. There is no shared tenant. There is no third-party vendor training on your positions, your process, or your edge. The AI that runs your shop belongs to your shop, sits inside your walls, and answers only to you. This matters because your edge as an independent operator is not the models you use — those are commoditizing every quarter. Your edge is the specific way your firm thinks, the specific patterns you have learned to recognize, the specific relationships you have built. A shared-tenant AI service that reserves the right to review or train on your inputs is a slow leak of the only thing that actually differentiates you. With Oz, that leak does not exist.

These are patterns. What Oz builds for your shop depends entirely on your shop. That is the point.

WHAT'S POSSIBLE

Institutional-grade

infrastructure, on an independent operator's balance sheet

The systems Oz builds for finance operations are deployed on the firm's own infrastructure by default. Portfolio data, client accounts, investment thesis material, and proprietary research never touch a shared vendor environment. Every model, every agent, every retrieval index sits inside the firm's own perimeter. The analytical and operational infrastructure that used to require a wirehouse balance sheet is now available to independent operators who care about controlling their own edge. Named engagements will follow as clients consent to be referenced. The scoping call is where we walk through what a build for your shop would actually look like.

Every Oz engagement is different. What we build for a finance operation depends entirely on the operation. The scoping call is where we figure that out.

See what Oz would build for your shop.

The first call is diagnostic, not a pitch. We walk through how your shop actually operates, where the analytical and operational leverage lives, and what a system designed around your mandate and your process could look like. If Oz is not the right fit, we tell you. If it is, you leave the call knowing exactly what a build would include and what it would cost.

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