Qofi Origin Platform

Agentic AI, Deployed Inside the Institution

Origin is the platform to build, ground, evaluate, and run agents on real finance workflows, inside the firm's perimeter, against the institutional standard.

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The Platform

One Platform, Four Layers

01 / WORKFLOW AGENTS

Agents That Run Real Deal Workflows

Compose agents that execute end-to-end finance work, diligence, modeling, memo drafting, screening, built from templates encoded with how the institution actually operates, not generic chat.

Agent TemplatesGenerate DocumentsHuman-in-the-Loop
Diligence AgentRunning
01Pull filings & data room
02Build the model
03Draft the IC memoREVIEW
04Route for sign-off
02 / CONTEXT GRAPH

Every Agent, Grounded in the Firm's Knowledge

The Context Graph turns the institution's documents, deals, and systems into a living, governed knowledge layer. Agents reason over the firm's own context, not the open web, so outputs are grounded, current, and citable.

Knowledge HubsCitationsVersioned & Governed
03 / EVALUATION & GUARDRAILS

Measured Against the Institutional Standard

Every agent is graded by operator-built rubrics before it ever touches production, then bounded by guardrails that keep it inside policy. The firm sees exactly where an agent is reliable, and where a human stays in the loop.

Operator RubricsPolicy GuardrailsAudit Trail
Eval ReportPASS
Accuracy
Grounding
Coverage
Policy
Within policy · cleared for production
04 / INTEGRATIONS & MCP

Connected to the Systems the Firm Already Runs

Origin speaks to the institution's data and tools through governed connectors and the Model Context Protocol, all inside the perimeter. Agents act across the firm's stack without data ever leaving it.

MCPIn-PerimeterGoverned Connectors
Use Cases

Put Agents to Work

No matter the desk, and no matter the task, Origin frees the institution’s teams to focus on the work that moves the firm forward. A sample of what agents handle across the firm.

01 / RESEARCH

Stay Ahead of the Market, Hands-Free

Agents watch the tape, the filings, and the desk’s own models, surfacing what matters before the open.

Alerts when analyst ratings or price targets shift
Auto-generated earnings preview summaries
Custom screeners for sector and alternative analytics
Skills that parse research models and align them to internal consensus
How It Works

From Connection to Production, in Five Steps

Qofi’s forward-deployed team stands Origin up inside the institution, connected, grounded, and governed from day one.

01
Connect

Governed connectors and MCP link Origin to the firm’s systems, inside the perimeter, scoped by the institution.

02
Ground

The Context Graph structures the firm’s knowledge into a governed layer every agent reasons over.

03
Build

Compose workflow agents from finance-native templates, tuned to how the institution runs each task.

04
Evaluate

Operator rubrics grade every agent and set guardrails before anything reaches production.

05
Deploy & Monitor

Agents run in production with continuous monitoring, a full audit trail, and a human in the loop where it matters.

The Challenge

Why Agentic AI Stalls in Financial Institutions

Most agents fail in finance not because the model is weak, but because it has no grounding, no ground truth, and no place to run safely. Origin closes all three gaps, so agents do work the institution can actually trust.

Generic Agents Don't Know Finance

Off-the-shelf agents miss the conventions, controls, and judgment a deal workflow assumes, and produce work no desk would sign off on.

No Ground Truth to Measure Against

Without operator-graded evaluation, there is no way to know whether an agent is right, only that it answered. That is unacceptable in finance.

Data Can't Leave the Perimeter

The data agents need is exactly the data the firm cannot send to a third-party endpoint. Most platforms ask institutions to choose between capability and control.

Case Study

Our First Customer Was Ourselves

Before deploying Origin inside financial institutions, Qofi ran it on its own operation, grounding agents in the firm's own context and turning them loose on real workflows. Within weeks, they were indispensable.

3xOutput per operator with agents in the loop
60%Less time spent on routine review
40%Faster turnaround on internal workflows
24/7Continuous agent coverage across operations
2xThroughput per quarter, without new headcount
5→3Operators to run the same volume of work

Put Agentic AI to Work Inside the Firm

See Origin run a real finance workflow, grounded, evaluated, and governed, inside your perimeter.

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