The architect designs.
The AI executes. The team supports.
One integrated lead designs every engagement. A proprietary AI execution layer handles the technical work at scale. A Cebu-based team handles the human-required execution. Fixed-price where scope allows. No discovery-deck theater.
Why an Integrated Lead Matters
AI implementations in finance require coherent design across six layers. Most organizations hire specialists for each layer; the integration gaps between them are where 95% of enterprise AI deployments fail. Filling each gap takes three or four specialists at most consultancies, assuming a subject-matter expert with the right depth even exists. Proforma is one of the only firms built around an architect with expertise across all six layers.
Our delivery model is built around one integrated lead, an AI execution layer, and a Cebu-based team for the human-required work. Fixed-price where scope allows. No discovery-deck theater. The architect designs and owns the design through delivery.
The model is the answer to a specific industry problem: most enterprise implementations fail at the integration points because no single person holds the design across financial systems, AI engineering, and finance transformation simultaneously. The work that survives in production is led by someone who does.
The Six Layers
Every layer has to be designed coherently with the others. A specialist who owns one layer cannot formulate the question intelligently without the other five. One architect balances decisions and educates the organization with all six in mind simultaneously.
Data Architecture
The core framework revolves around ensuring the right data architecture is in place. A scalable framework for dynamic context assembly across financial systems is what keeps agents stable. Without it, agents become brittle and prone to failure when relying on LLM reasoning alone.
Correctly designed data architecture establishes governance and safety, and firewalls key production systems from the catastrophic failures common to most autonomous systems.
Governance
Autonomous AI agents cannot be governed the way traditional software is governed. System prompts, rules, tools, and instructions are suggestions to the model, not enforceable constraints. Proper governance relies on deterministic, non-autonomous design at the orchestration layer, with LLM-driven reasoning scoped to the narrow decisions the deterministic logic cannot make.
Governance becomes a design decision rather than a control layer. The agent's scope is bounded structurally, not by hoping the model behaves.
Agent Design
Best-in-class architecture and modern agent design, with the substance documented openly on our research page. No fluff, just raw intellectual property. We deploy agent functionality fast by evaluating reasoning, not just results.
Three disciplines hold the design together: scope-limiting to reduce hallucination, system prompts that enforce behavior, and a clear line between deterministic orchestration and agentic reasoning.
Process Design
Agentic workflows can be bolted onto existing business processes during a pilot or MVP. But as agent usage scales, processes have to be re-architected with AI in mind. Governance, management reviews, and individual task assignments all get rethought, including which actions happen on which business day.
How will the close and planning cycles change? Where do feedback loops belong? Most of the business value lives in how agents change the business calendar itself.
Proforma authors or refines business processes from the core activity level down to the individual task, so the surrounding workflow is stable and the AI-centric layer behaves deterministically in production.
What Deployment Has to Get Right
A production-grade AI system has to work technically and make economic sense. The two are inseparable: operational effectiveness without economic discipline produces systems that work but cannot be afforded.
Adapting Existing Financial Systems
EPM, ERP, and CRM a few tools that must evolve to support agent-driven strategies. As organizations move from driver-based planning and automated consolidation tools to modern AI-aware toolsets, finance and operations have a defining role: identifying how AI-driven forecasting can produce faster scenario modeling that remains grounded in operational reality.
An AI-generated forecast is only valuable if its insights map to controllable elements of operations. Predictive planning still lags across the finance world because most predictive models have not yet been rooted in operational reality.
Economics
Fully dynamic architectures are expensive. The cost difference between truly autonomous AI and stable AI-driven automation can reach 100x.
We engineer the trade-off between latency, token usage, and autonomy so each solution produces measurable ROI. Observability and cost reporting are part of the production system from day one, not an afterthought.
The Delivery Model
AI implementations for a Finance organization requires coherent design across all six layers. Most organizations hire specialists for each layer. The integration gaps between those specialists are where 95% of enterprise AI deployments fail, according to the MIT NANDA Project’s State of AI in Business research.
The combination of disciplines required to design across all six simultaneously is statistically rare. The architect is what that combination looks like when it exists, and the firm is structured around it rather than around its absence.
The Architect
Data. Process. Modeling. Technical delivery. One person oversees all elements.
The AI Execution Layer
A proprietary framework we built and refined across engagements: agent orchestration, dynamic context assembly, semantic resolution, and deterministic scaffolding. The result of years of building and breaking custom AI systems until each design principle became self-evident.
The Cebu Delivery Team
The most talented resources based in Cebu City, Philippines. We recruit and screen for aptitude and intelligence, because AI multiplies both. Every team member is trained deeply in AI engineering and Oracle EPM, so the firm's delivery quality holds across each layer of every engagement.
Modernize for the next decade, not the last one.
Fixed-price where scope allows. Engagements run 11 to 42 weeks depending on shape. The first conversation is free; the architect tells you honestly which shape fits your actual situation, even when the budget conversation is uncomfortable.