qofi — what we do

services

From board-level strategy to custom agents, the infrastructure to run them, and the platform they reason over.

in-perimeter deploymentclient data ≠ training datatenant isolationfull audit trailencrypted at rest and in transitno model training on client dataSSO and role-based accessregional data residency

what we do

Four engagements, each one deployed inside the institution rather than sold as a license.

AI Business Strategy

Board-level vision, AI education for leaders, ROI models, and a roadmap matched to the institution’s ambitions — in as little as two weeks.

RoadmapsROI modelsLeadership educationGovernance

Custom AI Solutions

Agents built to open new opportunities and take out cost — integrated into the firm’s environment, typically in production in under twelve weeks.

Workflow agentsDocument generationAgent templatesDesk supervision

Enterprise AI Infrastructure

AI architecture, evaluations capability, agentic frameworks, and knowledge pipelines — the technology and skills to scale AI across the business.

AI architectureEvaluationsAgentic frameworksKnowledge pipelines

Qofi Origin Platform

One engine across the institution — a living context graph of documents, deals, and decisions that every agent reasons over, permissioned down to the individual.

Context graphProvenancePermissioningIn-perimeter

More deal capacity without adding headcount

Agents run first-pass diligence from source documents to IC memo, so analysts spend their time forming the view rather than assembling it.

Covenant terms, exposure and counterparty history are pulled from the institution’s own filings and reconciled into a single reviewable position.

Recurring reporting drafts itself from the systems of record, with every figure traceable back to the document it came from.

A context graph over past deals, memos and decisions, so precedent surfaces at the moment the desk needs it.

DEAL MEMO

✓ Conviction, faster

Repetitive work shifted to agents

how we work

The same sequence on every engagement, whichever service it starts as.

Detailed Project Scoping

We forecast the build before it starts.

Scoping is its own phase. We map the workflow, the data behind it, and the decision it feeds, then commit to timelines, cost, and the impact we expect — so the work is judged against a number agreed up front.

Custom-Built AI Software

Production systems, not demos.

We build workflow automations and product features to production standard, inside the institution’s environment and its existing stack, rather than handing over a prototype that someone else has to make real.

Evals

Metrics that guide the build and prove it works.

Before deployment we assemble ground-truth data and evaluation harnesses that measure accuracy throughout development, so performance is known rather than asserted when the system reaches the desk.

Deep Collaboration

Your domain knowledge, our AI expertise.

We sit with the desk that owns the outcome. The institution brings the judgment that took decades to build; we bring the systems, and neither of us could produce the result alone.

Deployment and Handoff

Reliable systems, put into production.

We ship code, documentation, and walkthroughs, and stay through rollout — playbooks, training, and enablement — until the team owns and maintains the system without us in the room.

Ongoing Enablement

Desks that keep pace after we leave.

Hands-on programs bring teams up to speed on frontier models, code agents, and the workflows they change, so capability compounds inside the institution instead of leaving with the vendor.

where should we start?

Look at what we have shipped, or bring us the problem you have been circling.

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