01 — Where I work
I focus on operational problems where failure is expensive.
Not automation for its own sake. I work on systems where fragmented data, weak controls, repetitive finance work or unreliable AI decisions affect revenue, cash, risk or operating capacity directly.
Revenue leakage
Detect underbilling, incorrect discounts, missed usage charges and entitlement mismatches — revenue that should have been collected and wasn't.
Payment reconciliation
Unify Stripe, PayPal, bank transfers and other processors into one auditable ledger with automated matching and exception handling.
Failed-payment recovery
Classify failures by cause, orchestrate recovery logic per failure type, and surface only the cases that genuinely need a human.
Cash visibility
Turn fragmented revenue, expense, receivable and payment data into a cash position and a forecast the finance team can actually operate on.
Credit & risk operations
Auditable workflows across ingestion, decision logic, scoring, review, monitoring and exception management.
Production AI
AI systems with evaluation, structured outputs, human review, observability — and model adaptation only where it measurably improves the outcome.
02 — Selected work
Selected engineering work
Systems designed and built around financial operations, payments, risk, data infrastructure and AI evaluation. Each case study states its status plainly — production, validated prototype, benchmark or reference implementation.
Multi-Processor Payment Reconciliation
A reconciliation architecture that normalises transactions, settlements, refunds, fees and payment events from multiple processors into a single auditable ledger.
Failed Payment Recovery Engine
An event-driven system that handles failed recurring payments according to failure type, instead of applying one retry strategy to every transaction.
90-Day Cash Flow Engine
Operational financial data combined into forward-looking cash visibility, scenario analysis and threshold-based risk alerts.
AI Evaluation Harness
Evaluation infrastructure for testing model behaviour against reproducible datasets and explicit decision thresholds — before anything reaches production.
03 — How I work
From expensive workflow to operating system.
- 01
Diagnose
Understand the workflow, the business objective, the operating constraints and the financial impact.
- 02
Architect
Design the data model, integrations, business logic, AI components, interfaces and control boundaries.
- 03
Build
Implement with code, APIs, databases, workflow orchestration and model infrastructure where it's warranted.
- 04
Evaluate
Test happy paths, edge cases, financial logic, AI behaviour, security boundaries and failure scenarios.
- 05
Deploy
Staging into production with monitoring, logging, rollback mechanisms and operational controls.
- 06
Improve
Measure what changed, find the weak points, and keep raising reliability, automation rate and business impact.
04 — Engineering principles
Production systems need more than a successful demo.
These are the properties I hold a system to before it touches money, customers or a credit decision.
Reliability
Retries, idempotency, timeout handling, explicit error states and recovery paths.
Auditability
Financial and AI decisions should be inspectable long after they happen.
Observability
Structured logging, error tracking, health metrics, latency and operating-cost visibility.
Human control
Automation should know when to stop and escalate rather than make uncertain high-impact calls.
Security
Least privilege, access controls, webhook verification, secrets management, careful data handling.
Ownership
Clients should understand and be able to operate the systems they depend on.
Evaluation
AI is measured against explicit datasets and acceptance criteria — not because a demo looked impressive.
Business problem → system → architecture → engineering decisions → technology. In that order.
05 — Model adaptation
Fine-tuning is a tool, not the strategy.
Most AI problems do not require training another model. I work up the ladder and stop at the first rung that meets the requirement.
Adaptation earns its place when there is repeatable domain behaviour, usable data, and a measurable quality target the base system cannot hit economically or consistently.
Every adaptation project ships with: baseline · held-out eval set · acceptance metric · failure analysis · cost and latency comparison · rollback path.
06 — Engagements
Ways to work with me
From diagnosing one costly operational problem to owning the architecture and continued evolution of the system.
Finance & AI Operations Diagnostic
Seven business days to map the workflow, quantify the financial impact, and decide what should and should not be automated.
$3,500Production Systems Sprint
A defined operational problem becomes a working system: architecture, implementation, integrations, testing, deployment and handover.
$12K–$25KFractional AI & Finance Automation Lead
Ongoing technical ownership — roadmap, architecture, implementation, controls, evaluation and ROI reporting.
$7,500/month · 3-month minimumOptimization & Reliability
For existing automation or AI infrastructure that needs to become dependable: monitoring, failure handling, performance and cost.
$4,000/monthDomain Model Fine-Tuning & Evaluation
Dataset preparation through supervised fine-tuning, evaluation, benchmarking, model card, deployment and rollback.
$12K–$25KSee scope, deliverables and how engagements start
All services →07 — Fit
Built for companies where operational complexity has become expensive.
Usually a strong fit
- Multiple operating systems and APIs, with meaningful transaction volume
- Finance or operations teams doing repetitive manual work
- Fragmented data, revenue leakage or weak cash visibility
- Growing reconciliation complexity or manual risk review
- A measurable business problem with a named owner inside the company
Probably not the right fit
- You need a $500 Zapier workflow
- There's no measurable operational problem, or nobody owns it internally
- The objective changes every week
- AI is the goal rather than the means
- You want someone to quietly maintain undocumented automations forever
08 — Writing
I publish the engineering work.
IntelligenceOS documents what I'm building, evaluating and learning across production AI, financial systems, model adaptation and architecture.
Read IntelligenceOS →09 — Common questions
What people ask before the first call.
Short, direct answers. If your question isn't here, it is a good first question for the discovery call.
What does Ugo Chukwu do?
Ugo Chukwu is an AI and financial systems engineer based in Dubai, UAE. He designs, builds and operates production AI and financial systems for fintech and B2B SaaS companies — payment reconciliation, failed-payment recovery, revenue leakage detection, cash flow forecasting, credit and risk automation, and AI model evaluation and adaptation.
What is a Fractional AI & Finance Automation Lead?
It is ongoing technical ownership rather than a one-off project. Ugo owns the automation roadmap, AI and finance systems architecture, integration strategy, implementation, reliability and controls, model evaluation, vendor coordination, and measurement and ROI reporting — typically two days a week on a prepaid retainer with a three-month minimum.
How much does an engagement cost?
A Finance & AI Operations Diagnostic is $3,500 fixed over seven business days. A Production Systems Sprint is $12,000–$25,000, scoped per system. The Fractional AI & Finance Automation Lead retainer is $7,500 per month with a three-month minimum. An Optimization & Reliability retainer is $4,000 per month. Domain Model Fine-Tuning & Evaluation is $12,000–$25,000.
How does an engagement start?
With a 30-minute discovery call, then usually a paid diagnostic that maps the workflow end to end and quantifies the financial impact. The diagnostic produces a value case with numbers — and sometimes the honest answer is that building is not worth it. Only then does a sprint or retainer begin.
Which companies is this a good fit for?
Fintech, B2B SaaS, payments, embedded finance, marketplaces and subscription businesses with meaningful transaction volume, multiple operating systems and APIs, and a named internal owner for a measurable operational problem — reconciliation backlog, failed-payment loss, revenue leakage, weak cash visibility or manual risk review.
Does Ugo Chukwu do model fine-tuning?
Yes, but only when it beats simpler approaches on an agreed evaluation set. The scope is supervised task-specific training, LoRA/QLoRA fine-tuning, embedding and reranker tuning, dataset curation, experiment tracking, evaluation, serving and monitoring — not frontier-model pretraining. Every adaptation project ships with a baseline, a held-out eval set, an acceptance metric, a cost and latency comparison and a rollback path.
Where is Ugo Chukwu based and who does he work with?
He is based in Dubai, United Arab Emirates, and works with clients across EMEA, the United States and remotely worldwide.