OPCO.AI

Creators of patent-pending INTELLIGENCE LAYER™

Intelligence architecture for institutional finance.Built by OPCO.AI.

An invitation to look closer.

Proprietary · U.S. PATENT PENDING 63/909,042

01 / The question

If we were rebuilding the intelligence architecture of a financial institution for the AI era, without replacing existing infrastructure, what would that architecture look like?

Consider the architecture

A different starting point.

OPCO.AI / First Principles

Keep the infrastructure. Reimagine the architecture.

STITCH, NOT SWITCH.™

02 / Our architecture

We are builders and innovators, creators of

Intelligence
Layer™

Proprietary, patent-pending architecture for institutional finance and capital markets.

Bridging legacy Wall Street systems to foundational AI models, with institutional context, judgment and controls built in.

STITCH, NOT SWITCH.™

Explore INTELLIGENCE LAYER™

Institutional intelligence, built in.

03 / The enterprise, connected

Proprietary enterprise intelligence

Intelligence is our product. The enterprise is our focus.

Others build agents around tasks. We build intelligence around your enterprise.

Our proprietary INTELLIGENCE LAYER connects domain intelligence across Investment & Portfolio, Operations, Risk, Finance, Compliance and Governance—bringing shared context to decisions and action.

Discover our patent-pending architecture

Context
Spine

Foundational
AI models
Domain Intelligence Systems. Connected institutionally.Inside the architecture

04 / INTELLIGENCE LAYER™

Architecture for
institutional intelligence.

Your firm controls its context: data definitions, policies, limits, relationships and authority.

Intelligence Layer assembles only the context each task needs, sends it to the model within your firm’s controls, and checks the output before it becomes action.

Systems of record stay authoritative. Models stay bounded and replaceable.

Your firm’s context

  • Data definitions
  • Policies
  • Limits
  • Relationships
  • Authority

Authoritative systems of record

INTELLIGENCE LAYER

Institutional context.
Within your firm’s controls.

Model boundary

AI models

Bounded · Replaceable
Only the context
the task needs
Output returns
for checking

Policy-approved action

Output checked before action

Not a model. Not an agent.

Intelligence Inside.

Discover our patent-pending architecture

Questions

  1. Q.01Why can’t firms simply connect directly to foundational AI models?

    Foundational AI models provide broad compute capability. Connecting internal systems of record directly to AI models surfaces the firm’s proprietary data, research and judgment to the model. OPCO.AI’s Intelligence Layer is the barrier between the firm’s confidential systems of record and the models. The layer sends only what a task needs and keeps the rest under the firm’s control.

  2. Q.02What is Intelligence Layer?

    Intelligence Layer is the architecture between a firm’s systems of record and foundational AI models. It qualifies the firm’s data, assembles institutional context, enforces a model boundary, and returns validated output for policy-approved action.

  3. Q.03What is proprietary about OPCO.AI’s architecture?

    The architecture is novel and PATENT PENDING (U.S. 63/909,042). It joins data qualification, institutional context, a model boundary and policy-approved action in one governed design, developed by OPCO.AI. Intelligence Layer is a trademark of OPCO.AI, Inc.

  4. Q.04What has OPCO.AI already built?

    OPCO.AI has built 12 Domain Intelligence Systems with 147 Capability Modules, covering Investment, Treasury, Risk, Operations, Compliance, Finance and more.

    Built on the same architecture, BLUEGANGES.AI has 3 productized systems for Private Capital, with 55 Capability Modules: TURBINE for Data Centers, Power and Compute; DELTA for Private Markets; and VOYAGER for Insurance.

05 / STITCH, NOT SWITCH.™

STITCH, NOT SWITCH.™

Keep existing systems. Add intelligence across them.

Node Calculator

Two ways to connect the same systems.
12systems

4 systems24 systems

Point-to-point integration

66integrations · one per system pair

Every system pair requires its own contract, schedule, retry policy and failure handling.

One Intelligence Layer

12governed adapters · one per system

Each system connects once through a versioned governed adapter contract to Intelligence Layer.

5.5×fewer integration contracts: 66 point-to-point versus 12 adapters

n(n−1)/2 ÷ n = (n−1)/2

Illustrative topology comparison.

06 / Domain Intelligence™

Domain Intelligence™

One architecture. 12 institutional domains.

A developed starting point, based on first principles, for enterprise AI applications.

Discover First Principles
Domain Intelligence
Systems
12
Capability
modules
147

Each Target, a system. Each dot, a Capability Module. Select a system to explore its scope.

01 / 12

Investment & Portfolio

CONVEXITY

Research, portfolio construction, allocation and performance attribution.

14 Capability Modules

Different domains require different context.

Domain Intelligence Systems apply the Intelligence Layer approach to the language, relationships and decision context of a specific function.

Explore Domain Intelligence Domain Intelligence in practice

07 / In practice

Convexity

Investment & Portfolio

Investment Cube

Six things that matter across the book, in one view: performance, market news, research, limits, liquidity and the investment committee docket.

Read the six sample faces

Surface 01

Performance this month

+0.81%

+$200mm month to date on the $24.8bn book

HF Series A +$80mm leads · Credit Fund B −$10mm, only fund down

Surface 02

Market moves and news

108 headlines

in 48 hours on the sectors the book holds

108 headlines in 48 hours · 37 listed

Surface 03

Research and ideas

+410 bps

Credit Opportunity A over the 10.5% required return

50 ideas · 5 approved · 5 at IC prep · 6 in diligence · 28 passed on

Surface 04

Limits on the watchlist

18.7%

Meridian Health, share of Credit Fund B NAV, against the 15% cap

5 positions on watch, $795mm · 1 in breach · 2 inside 25% of a limit

Surface 05

Cash and liquidity

12.4%

cash and near-cash, % of NAV, against the 8% reserve

Low 9.1% on Oct. 27 · under redemption stress, $446mm below the reserve by Nov. 8

Surface 06

Investment committee

5 memos

$585mm proposed for the Oct. 23 IC meeting

2 held on a red sign-off · 4 linked trades go as one item

From context to prediction

08 / Predictive Intelligence

Predictive
Intelligence™

Everyone wants prediction.
It requires architecture, context and reasoning first.

A forecast is only as good as what sits beneath it: connected systems of record, qualified data, and reasoning that explains the present with evidence.

OPCO.AI builds in that order. Intelligence Layer first. Then Level 1: Domain Intelligence Systems that explain what is happening and why. Then Level 2: forecasts of margin calls, liquidity and limit use, each with its confidence, checked against the outcome.

A margin-call forecast, connected to its context.Illustrative example · Sample data

Firm context

VAULTTreasury & Margin
LATTICERisk
SPANNEROperations

Intelligence Layer

Connected domain context.

Margin calls, next 10 days

$46–67mm · 80% range

High confidence
An illustrative margin-call forecast and its observed outcomeSample data. The initial range is 46 to 67 million dollars, with an 80 percent range and high confidence. The observed 10-day outcome is 56 million dollars. The illustrated recalibrated range is 50 to 63 million dollars.705540$mmToday+10 days
Forecast rangeCentral forecastObserved outcome

Treasury, risk and operations contribute firm context.

Each forecast shows its confidence, its drivers and its data, and is checked against what actually happens. Sample data.

09 / Application Layer

Our proprietary architecture supports overarching Domain Intelligence Systems & Applications.

Discover our patent-pending architecture From architecture to products

Full AI stack

  1. Domain Intelligence Systems Applications

    OPCO.AIServices

    12 Domain Intelligence Systems147 Capability Modules

    BLUEGANGES.AIProducts

    3 productized Domain Intelligence Systems55 Capability Modules

  2. OPCO.AI

    Intelligence Layer

    U.S. Patent Pending 63/909,042

  3. AI models

    Foundational AI models + specialized models
    Replaceable · firm context stays governed

  4. Cloud / Data Centers

    Client-controlled runtime

  5. Chips / Computing

    Compute infrastructure

  6. Energy

    Power generation + delivery

10 / BLUEGANGES.AI

Sister company

From architecture
to productized intelligence.

BLUEGANGES.AI

BLUEGANGES.AI, OPCO.AI’s sister company, builds 3 products for private capital, with 55 Capability Modules, on the same Intelligence Layer architecture.

11 / Experience

30+ years of
domain experience.

Banking & hedge funds
Investment banking and hedge funds · buy side and sell side
FICC
Fixed income, currencies and commodities · capital markets sales and trading analytics
Rishi Bali in front of the New York Stock Exchange.

Forbes Article

From Goldman To AI: How Rishi Bali Is Building Wall Street’s Transformation Layer With OPCO.AI

Sindhya Valloppillil · Sept. 23, 2025

Read the article
UBS episode card: AI and Family Offices—Building the intelligence layer, with Mark Tepsich and Rishi Bali.

UBS On-Air Podcast

AI and Family Offices — building the intelligence layer

Family Office Solutions Podcast · Dec. 11, 2025

Listen on Apple Podcasts
Intelligence Inside

12 / Intelligence Inside

Intelligence
Inside.

Start a conversation

Stay connected.

Be the first to hear as we continue innovating.

hi@opco.ai

OPCO.AI First Principles

The thinking behind the architecture

First
Principles.

  1. Existing financial infrastructure is not disappearing.

    Financial institutions have invested enormous amounts of capital, time and institutional knowledge in their existing technology environments. Portfolio management systems, accounting platforms, risk engines, treasury systems, trading platforms, data warehouses, CRM systems, document repositories and internally developed applications each perform functions that remain necessary.

    Artificial intelligence does not make those systems irrelevant. Nor can institutions practically replace decades of infrastructure before capturing the benefits of AI.

    This creates an important architectural constraint:

    The AI architecture of the future must work with the infrastructure institutions already own.

    The objective therefore should not be to create another isolated system that requires the institution to migrate its data, replace existing workflows or establish yet another source of truth. The more important opportunity is to create intelligence across the infrastructure that already exists.

  2. Data is not intelligence.

    Financial institutions possess enormous quantities of data. But possessing data and understanding what it means are fundamentally different things.

    A position stored in one system has relationships to financing terms stored somewhere else. Those financing terms affect liquidity. Liquidity affects portfolio construction. Portfolio construction affects risk. Risk may affect a covenant, policy limit or investment decision.

    The economic reality exists across these systems simultaneously even when the underlying technology does not represent it that way.

    Intelligence therefore requires more than retrieving information. It requires understanding relationships, context and meaning across information.

    This distinction becomes particularly important with AI. Giving a foundational AI model access to larger quantities of disconnected data does not automatically give it institutional understanding.

    The architecture must establish what the information represents, how different pieces relate to one another, which sources are authoritative, what has changed and what context matters to the question being asked.

  3. Foundational AI models do not know the enterprise.

    Foundational AI models represent an extraordinary expansion in generalized machine reasoning. But generalized intelligence and institutional intelligence are not the same thing.

    A model does not inherently know which portfolio a position belongs to. It does not know which version of NAV is authoritative. It does not know the terms of a firm’s prime brokerage agreements. It does not know the institution’s accounting policies, investment mandates, counterparty relationships, approval hierarchy or control framework. It does not know which information it is permitted to access. And it does not inherently know the history or current state of the enterprise.

    This means that the principal challenge of enterprise AI is not simply obtaining access to increasingly capable models. The deeper challenge is creating the architecture through which those models can reason within the specific context of an institution.

  4. Financial intelligence is domain-specific.

    There is no single generic definition of intelligence inside a financial institution. The information necessary to understand treasury is different from the information required to understand accounting. Portfolio risk requires different context from regulatory compliance.

    Collateral management, fund structuring, investor relations, operations, market making and governance and audit each involve their own:

    • data
    • terminology
    • relationships
    • workflows
    • policies
    • calculations
    • decision structures
    • exceptions
    • controls

    A generic AI interface may be capable of answering questions across these areas. But institutional-grade intelligence requires deeper domain context.

    At the same time, these domains cannot remain isolated. A change in one part of the institution can affect another. Treasury can affect investment decisions. Investment decisions affect risk. Risk can affect financing. Financing can affect accounting. Accounting can affect investor reporting.

    The architecture therefore has to accomplish two things simultaneously:

    Understand each domain deeply. Connect intelligence across domains.

  5. Enterprise AI must be governed at the architectural level.

    Financial institutions cannot treat governance as something applied after an AI system produces an answer.

    The architecture itself must determine:

    • what information a model can access;
    • what information can leave the institution;
    • which model may be used;
    • what context is provided;
    • whether sensitive information is redacted;
    • whether data is retained;
    • whether a model is appropriate for the requested computation;
    • what source generated an output;
    • how an inference can be traced;
    • whether owner approval is required;
    • and what actions an AI application is permitted to take.

    These are not simply compliance questions. They are architectural questions.

    A financial institution cannot separate intelligence from control because the reliability of the intelligence depends partly upon the controls governing how it was produced.

  6. AI applications should be built on context, not connected to it afterward.

    Much of the first generation of enterprise AI has focused on individual applications, copilots and agents.

    But an application that independently reconstructs enterprise context every time it performs a task creates a structural limitation. Each application develops its own integrations. Its own interpretation of data. Its own business logic. Its own permissions. Its own understanding of state. Its own governance framework.

    The result risks creating a new generation of fragmentation above the fragmentation that already exists below it.

    A more effective architecture becomes possible if enterprise context is established first. Applications can then operate on top of shared intelligence.

  7. The purpose of enterprise AI is not more automation. It is better decision-making.

    Automation has enormous value. Many processes inside financial institutions should become faster, less manual and more efficient. But automation and intelligence should not be confused.

    Automating an existing task answers:

    Can a machine perform this activity?

    Intelligence asks a different set of questions:

    • What is happening?
    • Why is it happening?
    • What information matters?
    • How does it relate to everything else we know?
    • What requires attention?
    • What decision does this information support?

    The distinction matters because the greatest economic value of AI inside financial institutions may ultimately come not from reproducing human tasks more cheaply, but from improving the institution’s capacity to understand itself, its portfolios, its counterparties, its risks and its opportunities.

OPCO.AI / Intelligence Inside.Return to OPCO.AI
OPCO.AI Intellectual Property

Original work. Built from first principles.

Intellectual
property.

  1. Architecture.
  2. Domain understanding.
  3. Prediction.

OPCO.AI develops proprietary intelligence architecture for institutional finance and capital markets. Our work connects the infrastructure firms already own, builds the context each domain requires, and carries that understanding into governed prediction.

Patent portfolio — Three areas of invention

Built in the order below.

Existing systems of record.Kept in place.

Select an invention to explore the work and its formal title.

01 / The architectural foundation

Keep the infrastructure.
Create intelligence across it.

An architecture between institutional systems of record and AI models. INTELLIGENCE LAYER qualifies data, assembles relevant institutional context and governs model access, with checked output supporting policy-approved action.

Explore INTELLIGENCE LAYER
Formal patent title & application

INTELLIGENCE LAYER PLATFORM FOR INVESTMENT SYSTEMS

U.S. Application No. 63/909,042 · Patent pending

The foundation for the domain and predictive intelligence that follow.

The sequence matters

Prediction begins
with what the
institution knows.

Architecture creates context.
Context creates domain understanding.
Domain understanding grounds prediction.

In OPCO.AI’s architecture, these are connected steps. Governed predictive intelligence builds on domain understanding; domain understanding builds on qualified institutional context. The INTELLIGENCE LAYER provides that foundation across the systems a firm already owns.

Discover the architecture

Trademarks / Architecture, approach and identity

The names behind the work.

Five marks.
One focus: Intelligence.

01 /STITCH, NOT SWITCH.™Connect existing infrastructure.
Add intelligence across it.

Our architectural approach preserves existing systems of record and the functions they perform. A shared intelligence architecture creates connections across that infrastructure without requiring wholesale replacement.

Explore the approach
02 /INTELLIGENCE LAYER™Institutional context between
systems of record and AI models.

The architecture through which qualified information and relevant context reach models within a firm’s controls, and model output returns for checking before action.

Explore INTELLIGENCE LAYER
03 /DOMAIN INTELLIGENCE™Intelligence organized around
each institutional function.

Domain Intelligence Systems bring the shared architecture into functions such as Investment & Portfolio, Treasury, Risk, Operations and Finance, supported by the Capability Modules each function requires.

Explore the domain systems
04 /PREDICTIVE INTELLIGENCE™Context, reasoning and calibration
applied to what may happen next.

Our approach connects domain reasoning with forecasts, scenarios and uncertainty, with observed outcomes informing calibration. It forms the predictive stage of OPCO.AI’s architecture.

Explore PREDICTIVE INTELLIGENCE
05 /OPCO.AI™Intelligence architecture and proprietary
builds for institutional finance.

The OPCO.AI brand identifies the team developing this architecture and applying it to the institutional systems, relationships and decisions of finance and capital markets.

Explore OPCO.AI

OPCO.AI / The work of the lab

Architecture first.
Intelligence inside.

Our lab focuses on the connective architecture, domain context and governed reasoning that institutional intelligence requires. We develop proprietary builds around existing financial infrastructure and the people who remain accountable for its decisions.

Explore our architecture
OPCO.AI Access terminal

Explore the work

Access
terminal.

OPCO.AI

DOMAIN INTELLIGENCE SYSTEMS

12
Systems
147
Capability Modules

Systems open on theintelligencelayer.org after sign-in.

BLUEGANGES.AI

Productized DOMAIN INTELLIGENCE SYSTEMS

3
Systems
55
Capability Modules

Product sites require sign-in.

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