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.
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Winning with AI requires more than technology — it demands strategic execution, cloud maturity, portfolio discipline, and financial rigor. data-fit operates at that convergence.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Each project addresses a real enterprise problem — strategic, financial, operational, or regulatory — solved with production-grade architecture and methodology. The underlying data is synthetic, generated programmatically to replicate real-world patterns at enterprise scale. Every number is traceable. Every decision is auditable.
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 with materiality rating, COSO principle, regulatory citation, and recommended disposition. Available for external auditor review before the period closes.
Connects an enterprise's financial targets, quota structure, and compensation plan design into a single calculation engine — surfacing payout exposure at P50, P75, and P90 before period close. Four pre-built scenarios. Dispute governance workflow with SLA management. Executive comp alongside IC comp in one model. The accelerator liability your CFO discovers in January, visible in October.
Eight fraud scenarios covering the complete material US payment fraud surface — ACH, Fedwire, and FedNow simultaneously. XGBoost risk scoring with a 15-feature vector calibrated to US payment rail semantics. Three-agent LangGraph pipeline with HITL interrupt on Critical-tier alerts. Every alert delivers the full agent reasoning chain and a structured investigation memo. No public dataset covers all three rails at this granularity. Building from regulatory statistics is domain discipline.
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.
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.
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.
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.
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.
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.
Every engagement begins with the business problem and its financial consequence. The platform is always the conclusion of the analysis, never the starting assumption. Vendor selection is the last decision in every engagement — not the first. Multi-partner relationships with IBM, Snowflake, Google Cloud, Databricks, AWS, and Azure exist to expand client options, not to pre-determine them.
Registered in Texas. Serving organizations where digital transformation and AI are strategic priorities, not IT projects.
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.
Finance, data engineering, AI strategy, or controls modernization
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