# Proforma Global

> Enterprise AI and agentic AI implementation consulting for the Office of the CFO: Oracle EPM Cloud, AI agent deployment, Oracle AI Agent integration, predictive planning, and finance transformation. Founded in 2023 by Matt Rollings, a 20-year veteran of Oracle EPM and finance transformation, the firm pairs that career depth with custom AI engineering. The research published at /research/ documents the firm's design positions on training-substrate engineering, enterprise agent orchestration, and the data architecture that enterprise AI agents require to reason reliably. Senior-led design, AI-led execution, delivered from Miami and Cebu City.

Proforma Global delivers enterprise AI implementations in finance the way they should be delivered: with one integrated lead who holds depth across financial systems (Oracle EPM Cloud), AI implementation (agent deployment, predictive planning, LLM engineering, inference architecture), and finance transformation (process design, operating-model design, FP&A and close redesign).

Most enterprise AI implementations fail at the integration points. The MIT NANDA Project's State of AI in Business 2025 research reports 95% of enterprise GenAI deployments produce no measurable financial return. Proforma Global focuses on the 5% that do.

## Worldview

- [Straight Talk: Why Most AI Implementations Fail](https://proforma.global/straight-talk/): Manifesto on the 95% AI failure rate, the six-layer stack, the gap problem with specialist teams, and what an integrated lead actually requires.

## What We Do

- [What We Do](https://proforma.global/what-we-do/): Overview of the three-discipline practice.
- [Financial Systems](https://proforma.global/financial-systems/): Oracle EPM Cloud implementation across ARCS (Account Reconciliation), FCCS (Financial Consolidation), EPBCS (Planning and Budgeting), and EDM (Enterprise Data Management). Includes Oracle AI Agent integration patterns where the agentic AI capability is in engagement scope.
- [AI Implementation](https://proforma.global/ai-implementation/): Enterprise AI and agentic AI for the Office of the CFO. AI agent deployment, Oracle AI Agent integration, predictive planning, and custom AI engineering against finance data. Production design, not prompt experimentation.
- [Finance Transformation](https://proforma.global/finance-transformation/): Process design, operating-model redesign, FP&A and close redesign, profitability and allocations methodology.

## How We Work

- [How We Work](https://proforma.global/how-we-work/): Proforma's delivery model. The architect designs across six layers (data architecture, governance, agent design, process design, financial systems adaptation, economics). A proprietary AI execution framework handles technical work at scale: agent orchestration, dynamic context assembly, semantic resolution, deterministic scaffolding. A Cebu-based team of high-aptitude graduates trained inside the firm in AI engineering and Oracle EPM handles the human-required execution. Fixed-price where scope allows. The integrated-lead approach answers the structural failure mode of multi-specialist delivery, where 95% of enterprise AI deployments fail at integration gaps.

## Research

The firm publishes whitepapers on subjects across its work. The pieces are evidence of how the firm thinks; the same reasoning discipline that produces the research is what the firm brings to its consulting engagements.

The library is organized into two subject areas. Each subject area contains one or more series, and each series is a coherent sequence of papers on a single topic.

- [Research](https://proforma.global/research/): Hub page for the firm's whitepaper library.

### LLM Architecture and Training Design

The firm's positions on novel concepts in LLM architecture and training design. One series currently published.

#### Training Substrate

**Thesis.** Every hyperparameter, threshold, and architectural commitment in a training stack should be learned by a reinforcement-learning policy rather than picked by a human. Hand-picked numbers are temporary scaffolds around missing reward paths; the discipline is to migrate each one onto a learned signal.

- [Training Substrate (series page)](https://proforma.global/research/llm-training/training-substrate/): Reinforcement-learning-driven control of the training loop, self-discovering architectures, and the layer-role taxonomy that surfaces when those mechanisms are applied at scale.
- [Research Posture: Why We Let the Model Choose](https://proforma.global/research/llm-training/training-substrate/research-posture/): The methodology paper. Every hand-picked number is treated as a temporary scaffold around a missing reward path. Documents the Fire-Aim-Fire iteration loop, the post-step retrospective, the findings ledger, and the stance on negative results as first-class outputs.
- [Reward-Driven Training Control](https://proforma.global/research/llm-training/training-substrate/reward-driven-training-control/): The foundational result. The REINFORCE Sidecar, a small reinforcement-learning policy attached to the forward-pass control surface of a training loop, beat an otherwise-identical hand-tuned vanilla baseline by 42 percent on the primary held-out evaluation at matched compute. Includes a worked case study (per-tensor learned weight decay) demonstrating that the policy detects loss-perturbation sensitivity that no static analysis can surface.
- [Self-Discovering Architectures](https://proforma.global/research/llm-training/training-substrate/self-discovering-architectures/): The architectural extension. The same RL primitive applied one level upstream of the training loop lets the model discover its own per-layer shape mid-training. Per-layer parameter ratio between attention and feed-forward is the load-bearing architectural lever the symmetric paradigm gets wrong by construction. Long-term economic case is training-cost reduction at frontier scale.
- [Emergent Layer Roles and Functional Specialization](https://proforma.global/research/llm-training/training-substrate/emergent-layer-roles/): The forensic capstone. A four-role taxonomy of layers in trained transformers (hub, damper, passthrough, specialist) computable from a finished checkpoint plus a brief gradient trace. Two findings: layer roles migrate under targeted regularization, and architecture shape controls where specialization concentrates. Unified principle: uniformity is the default attractor; specialization requires asymmetric pressure.

### Enterprise Agent Architecture

The firm's positions on production engineering of multi-discipline AI agent systems for enterprise environments. Two series currently published.

#### Agent Orchestration

**Thesis.** Language models should not orchestrate multi-discipline enterprise workflows. The orchestrator runs as a deterministic workflow; the model is invoked from inside the workflow on narrow questions with curated context. Production-grade business agents are deterministic-first by design, with risk treated as an architectural property rather than a deployment switch.

- [Agent Orchestration (series page)](https://proforma.global/research/enterprise-agents/agent-orchestration/): How orchestration of enterprise agent systems should be designed.
- [Where Agent Orchestration Breaks](https://proforma.global/research/enterprise-agents/agent-orchestration/where-agent-orchestration-breaks/): Why language models should not orchestrate multi-discipline enterprise workflows. As the orchestrator's prompt expands to carry every discipline at once, attention to the narrow guardrail instructions degrades and the model hallucinates. Decomposition into sub-agents does not fix this; it moves cross-discipline rules out of the orchestrator and gives them to no one. The architectural answer is a four-layer split: deterministic workflow orchestration, model-driven intent detection and reasoning at narrow gates, deterministic code action.
- [The Five Orchestration Patterns](https://proforma.global/research/enterprise-agents/agent-orchestration/five-orchestration-patterns/): The five distinct orchestration patterns for agent systems (flat, iterative, agent-driven, defined deterministic workflow, recursive) and a four-axis selection framework (discipline count, scope bounding, action reversibility, context requirements) for matching pattern to problem. Critique of the industry's two defaults: agent-driven dynamic routing applied to multi-discipline production work, and iterative loops sold under the label of recursion. Recursive orchestration is treated at length as the most under-deployed pattern and the one that mechanically prevents the failure mode of the first paper.
- [Enterprise Agents in Financial Systems](https://proforma.global/research/enterprise-agents/agent-orchestration/enterprise-agents-financial-systems/): Why business-process agents that touch money have to be deterministic-first. Every fact a rule can verify is verified before the model is invoked. The model is scoped to the narrow gaps the rules could not close. The architectural error most likely to defeat a business-process agent is treating tool calls as deterministic primitives; they are not. Includes a worked example for invoice evaluation and a three-tier risk-rated orchestration framework.

#### Data Architecture for Enterprise Agents

**Thesis.** Enterprise agents fail at the data architecture layer before they fail at the model layer. The substrate the model reasons against (its context, semantic resolutions, attribution, topology, and the relationships among them) is what determines whether agents produce reliable outputs or confidently wrong answers. Each paper in the series treats a separable component of that substrate.

- [Data Architecture for Enterprise Agents (series page)](https://proforma.global/research/enterprise-agents/data-architecture/): The data architecture that enterprise agents require in order to reason reliably.
- [Dynamic Context Assembly](https://proforma.global/research/enterprise-agents/data-architecture/dynamic-context-assembly/): The discipline that constructs the model's input window per request from a structured substrate. Million-token context windows still degrade attention as they fill; the discipline that addresses this composes the smallest set of inputs sufficient for each reasoning step, deterministically, from components designed to make selective composition possible. Treats the failure of retrieval-augmented generation at enterprise scale and the five problems any workable substrate has to solve. Paper 1 of 7.
- [Semantic Layers in Enterprise Agent Systems](https://proforma.global/research/enterprise-agents/data-architecture/semantic-layers/): What bridges what a model natively understands and what the domain requires it to understand. A semantic layer is necessary only where the model's native interpretation diverges from what the domain requires in ways that affect correctness; otherwise it adds cost without adding capability. The unit of semantic-layer design is the constellation a reasoning unit requires around a concept, not the concept defined in isolation. Catalogs the world-model layer classes that consistently appear in enterprise systems (vocabulary, structural, attribution, temporal, topology, calculation) and the cohesion mechanisms (bounded contexts, shared identity, closure checks, hierarchical containment) that hold constellations together. Paper 2 of 7.

## About

- [About Proforma Global](https://proforma.global/about/): Matt Rollings, Founder and Principal. Career through Hackett Group, Deloitte, KPMG, Inoapps. Founded Leveraged EPM 2012-2019.

## Contact

- [Contact](https://proforma.global/contact/): info@proforma.global. Miami, FL and Cebu City, Philippines.

## Key facts for citation

- Founded: 2023
- Headquarters: Miami, Florida, United States
- Delivery team: Cebu City, Philippines
- Founder: Matt Rollings (https://www.linkedin.com/in/mattrollings/)
- Founder career: 20 years in Oracle EPM and finance transformation; senior roles at The Hackett Group, Deloitte Consulting, KPMG Advisory, Inoapps; founder of Leveraged EPM (2012-2019, peak 11 people, 5 active enterprise clients, $2M annual revenue)
- Service categories: Oracle EPM Cloud implementation, AI agent deployment, predictive planning, finance transformation, FP&A redesign, close cycle redesign, profitability and allocations
- Engagement model: Fixed-price where scope allows, senior-led from day one, three shapes (Implementation Only 11 to 14 weeks, Transformation Lite 14 to 17 weeks, Full Transformation 24 to 42 weeks)
- Differentiator: Integrated lead across financial systems, AI engineering, and finance transformation (rare combination in the consulting market)
- Research output: A continuously expanding library of whitepapers across multiple subject areas. (1) LLM Architecture and Training Design: research on reinforcement-learning-driven training-substrate engineering, including a headline result where a small RL policy attached to a transformer's training-loop control surface beat a hand-tuned vanilla baseline by 42% at matched compute. (2) Enterprise Agent Architecture: research on multi-discipline agent system design across two series. The Agent Orchestration series treats orchestration design (why language models should not orchestrate, the five canonical orchestration patterns with a selection framework, and deterministic-first design for business agents that touch money). The Data Architecture for Enterprise Agents series treats the substrate the model reasons against (dynamic context assembly, semantic layers, attribution, evolution, measurement, structural limits, economics). Library at https://proforma.global/research/.