data-fit LLC · Decision Intelligence Consulting

AI adoption is a
leadership decision,
not a technology project.

Finance leadership and AI fluency are no longer
separate job descriptions.

Successful digital transformation
demands Alignment,
Ownership, and Agility.

The organizations winning with AI didn't buy better technology.
They made better decisions.

data-fit advises the leaders who make it succeed.

At the intersection of Finance, Strategy, and Technology — data-fit delivers senior-grade counsel that enables digital transformation and AI as genuine differentiators of business success.

Active Certifications
11 certifications · 6 platforms
IBM watsonx (3)
watsonx Orchestrate Practitioner Advanced
watsonx.governance Practitioner Advanced
watsonx.ai Data Science & MLOps Practitioner Advanced
AWS (3)
AWS Generative AI Applications Specialization
AWS Certified Machine Learning – Specialty
AWS Certified Cloud Practitioner
Microsoft Azure (1)
Microsoft Certified: Azure Data Scientist Associate
Data Platforms (2)
Snowflake Generative AI Specialization
Mastering Azure Databricks for Data Engineers (Packt)
Stanford University (2)
Machine Learning Specialization (Stanford University / DeepLearning.AI)
AI in Healthcare Specialization (Stanford University)
IBM Silver Business Partner
Snowflake Registered Partner
Google Cloud ® Partner Network · Registered

IBM, the IBM logo and IBM Partner Plus are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. Snowflake is a trademark of Snowflake Inc. Google Cloud is a trademark of Google LLC.

Four disciplines. One decision layer.

Winning with AI requires more than technology — it demands strategic execution, cloud maturity, portfolio discipline, and financial rigor. data-fit operates at that convergence.

01 — Agentic AI
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Autonomous Decision Systems

Multi-agent AI systems that monitor business performance, surface anomalies, synthesize cross-functional signals, and deliver structured recommendations — with human oversight built in by design, not added as an afterthought when the auditors ask.

Multi-Agent Generative AI LLM Engineering HITL ReAct
02 — Multi-Cloud & Hybrid Architecture
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Cloud-Native Data & Analytics

End-to-end data engineering across hybrid and multi-cloud estates — from streaming pipelines to data science platforms — with FinOps governance to keep cloud spend aligned to business value, not vendor preference.

Multi-Cloud Hybrid Architecture Streaming Data Science FinOps
03 — Strategic Portfolio Management
🗂️

Strategy Execution at Scale

Connecting organizational strategy to program execution — aligning investment decisions, managing capacity, and governing delivery through Agile and SAFe frameworks at enterprise scale, with OKRs that drive accountability rather than reporting theater.

Strategy Execution Program Governance Agile SAFe Value Streams
04 — Finance & Strategy
📊

Financial Intelligence & Advisory

Senior-grade financial counsel that transforms raw data into boardroom-ready intelligence — from 3-statement modeling and DCF valuation to AI-augmented CFO narratives and M&A advisory. The financial rigor most technology consultancies cannot provide.

FP&A DCF M&A Scenario Planning CFO Advisory

The advisory gap most practices cannot close.

Most technology consultancies lack financial depth. Most finance advisors lack technical execution capability. The organizations that fail at AI and digital transformation rarely fail at the technology — they fail at the intersection where financial accountability, technical architecture, and strategic execution have to work together. That intersection is where data-fit operates.

Domain foundation

Finance-native problem framing

Every engagement begins with the business problem and its financial consequence — restatement exposure, fraud loss, control failure cost, cloud spend misalignment. The technology architecture follows from that framing. The platform is always the conclusion of the analysis, never the starting assumption.

Regulatory discipline

Every claim citable at the primary source

No assertion without a named reference. PCAOB inspection findings. FBI IC3 loss data. FinCEN advisories. NACHA operating rules. ACFE occupational fraud statistics. The same sources your audit committee, BSA officer, and board already reference — applied to the design of every detection scenario and every detection signal.

Platform agnostic

Broad expertise. Zero vendor lock-in.

Certified across AWS, Azure, GCP, IBM watsonx, Snowflake, and Databricks — not as a preferred-vendor list, but as a depth-of-expertise inventory. Every engagement selects the platform that fits the problem and the existing infrastructure, not the other way around.

Build standard

Production-pattern throughout

Medallion schema. Idempotency keys on every event. Dead-letter queues with exponential backoff. LangSmith observability on every agent call. Full audit trail on every decision. These are the standards applied to every engagement — demonstrated in running systems any architect or auditor can interrogate directly.

Two problems. Two running systems.

Each project begins with a documented business problem grounded in primary regulatory and statistical sources. Each delivers a production-pattern system — ML pipeline, multi-agent investigation layer, human-in-the-loop escalation controls, and a complete audit trail. The technology serves the problem. The problem is always defined first.

In Development
Payment Risk Intelligence · Mid-Market Banks · FinTech Operators · Corporate Treasury

RiskPulse

Real-Time Fraud Detection Across Every US Payment Rail
The problem

US cybercrime losses reached $16.6 billion in 2024 — a 33% increase year-over-year and the highest annual figure since IC3's founding — with business email compromise alone accounting for $2.77 billion across 21,442 complaints.FBI IC3 Annual Report, 2024 The structural failure is architectural: ACH, Fedwire, and FedNow each operate under incompatible settlement timing, reversal rights, and fraud exposure profiles. Point solutions address one rail. Fraud actors exploit all three simultaneously, across the gaps between them.

The methodology

Every fraud scenario is defined against a named primary source before a single model is trained. BEC wire diversion maps to FinCEN FIN-2019-A005. BSA structuring maps to 31 USC 5324. APP scams map to CFPB Circular 2022-04. The synthetic transaction generator is parameterized against the Federal Reserve Payments Study, NACHA ACH fraud reports, and FBI IC3 loss distributions — the same sources your BSA officer and audit committee already reference. No public dataset covers all three rails simultaneously at the granularity required for rail-calibrated detection. Building from regulatory statistics is not a compromise. It is domain discipline.

The system

Eight fraud scenarios covering the complete material US payment fraud surface. XGBoost risk scoring with a 15-feature vector calibrated to US payment rail semantics. A three-agent LangGraph pipeline — Triage, Investigation, Escalation — with HITL interrupt on Critical-tier alerts and LangSmith observability on every agent call. Every alert delivers the full agent reasoning chain and a structured investigation memo. A risk score without a traceable rationale is not an actionable finding for a fraud operations team.

Architecture — Layer Map
⚙️
Synthetic Transaction Generator
Fed Payments Study · NACHA · FBI IC3 distributions
🧠
XGBoost + SMOTE Risk Scoring
15-feature vector · rail-calibrated thresholds
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LangGraph Agent Pipeline
Triage → Investigation → Escalation
👤
HITL Escalation — Critical Tier
LangGraph interrupt · full audit trail
Live dashboard screenshot — riskpulse.data-fit.com
Vendor-Agnostic 8 Fraud Scenarios LangSmith Observability HITL — Critical Tier
Gate 11 Complete — Deploying
Financial Controls Intelligence · CFO · Controller · Chief Audit Executive · CIO

FinGuard Core

Continuous GL Controls Monitoring for SOX 404 Compliance
The problem

The PCAOB reported a 35% audit deficiency rate in 2023 — the highest in a decade — with internal control over financial reporting the leading deficiency category for the third consecutive year.PCAOB Annual Report, 2023 The ACFE estimates organizations lose 5% of annual revenue to occupational fraud, with financial statement fraud producing a median detection lag of 16 months — more than a full fiscal year after the control failure began.ACFE Report to the Nations, 2024 The detection gap is timing, not coverage. Most SOX 404 controls identify exceptions after the period closes — when remediation is reactive, restatement risk is already present, and the audit committee conversation is harder than it needed to be.

The methodology

Every control scenario is grounded in a named COSO principle and regulatory citation before the ML layer is specified. Segregation of duties violations map to COSO Control Environment and PCAOB AS 2201 §28. Period-end manual journal entries map to SEC Staff Accounting Bulletin 99. System account postings map to PCAOB AS 2201 IT General Controls. This is a governance-aligned detection framework — not a threshold-violation exercise. Materiality is assessed per exception against configurable dollar thresholds that reflect your business reality, not a system default set at implementation and never revisited.

The system

Seven control scenarios covering the complete SOX 404 exception surface. FLAML-selected ML classification with a three-agent LangGraph investigation pipeline — Classification, Materiality, Escalation — with HITL interrupt on Material-tier exceptions. Every exception produces a structured memo: materiality rating, COSO principle, regulatory citation, and recommended disposition. Every decision is logged to a full audit trail before the period closes — available for external auditor review and PCAOB inspection without additional documentation effort at period-end.

Architecture — Layer Map
📋
Synthetic ERP Transaction Stream
COSO/ACFE-calibrated · 7 control scenarios
🧠
FLAML AutoML Classification
Idempotent GL event processing · medallion schema
🔀
LangGraph Agent Pipeline
Classification → Materiality → Escalation
📊
CFO Dashboard + Audit Trail
Real-time · PCAOB-ready documentation
CFO dashboard screenshot — finguard.data-fit.com
Vendor-Agnostic 7 Control Scenarios LangSmith Observability PCAOB-Ready Audit Trail

Methodology that matches the problem, not the preference.

Most firms force a single delivery model onto every engagement. data-fit applies SDLC discipline where contracts and stability matter — and Agile iteration where intelligence needs to emerge from data. The distinction is not stylistic. It is structural.

"We don't simplify complexity. We make it navigable."
01

Problem-first architecture

Every engagement begins with the business problem and its financial consequence. Data schemas, pipeline architecture, and observability design are specified upfront against those requirements — never against a preferred platform. Platform selection is always the conclusion of the analysis.

02

Regulatory grounding — no claims without sources

Every scenario, threshold, and detection signal is citable at the primary source level — the same sources your audit committee, BSA officer, and board already reference. No assertion without a named reference. No number without provenance.

03

Observability as a first-class citizen

Every system ships with full agent observability, distributed traces, and audit logs — LangSmith on every agent call. What you cannot measure, you cannot improve. And you cannot defend to regulators, auditors, or an audit committee.

04

Human-in-the-loop by design

Autonomous agents that act without guardrails are a liability in regulated industries. HITL controls are architected at the workflow level — as LangGraph interrupt nodes on Critical and Material-tier events — not added as an afterthought when the auditors ask.

Core Expertise
Finance & Strategy
FP&A DCF M&A CFO Advisory IBP
Cloud Data Engineering
AWS Azure GCP Snowflake Databricks
AI / ML Engineering
LLM Platforms IBM watsonx LangGraph XGBoost
Agentic AI Systems
ReAct HITL Multi-Agent Guardrails

Where rare expertise converges.

Most AI initiatives fail not because of bad technology — but because leadership lacked the right counsel at the moment of decision. data-fit exists to close that gap.

The practice sits at the intersection of Finance, Strategy, and Technology — combining deep financial expertise across corporate FP&A, channel economics, and long-range planning with multi-cloud engineering breadth and production-grade AI execution. That combination — finance-native problem framing with technical delivery capability — is the advisory gap most practices cannot close.

Registered in Texas. Serving organizations where digital transformation and AI are strategic priorities, not IT projects.

The right advice at the moment of decision.

Whether you're evaluating an AI initiative, a financial controls modernization, a data transformation, or a payment risk challenge — the conversation starts here. Every initial discussion is structured around your specific problem, your existing infrastructure, and your organization's definition of success.

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Consulting Engagement

Finance, data engineering, AI strategy, or controls modernization

🎯

Request a Live Demo

See RiskPulse or FinGuard Core running against your scenario type

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Partnership or Role

Senior collaboration or advisory opportunity

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