Every service area below is grounded in The Inductive Enterprise — the seven-part research framework that maps how AI creates measurable value inside real organizations, from business process signal mining through machine learning, text analytics, AI agents, and governance architecture.
Each engagement applies the same principle: structured signal extraction, Bayesian evidence accumulation, and LLM interpretation within a governance framework that keeps human authority intact. The seven areas below map directly to the seven parts of The Inductive Enterprise.
Every business process continuously emits probabilistic information — timing variance, exception queues, narrative commentary, escalation patterns, customer tone shifts. Most organizations treat these as operational noise. We help you treat them as a structured signal surface: an instrument panel that detects instability before it becomes visible in conventional KPI dashboards. This is the foundational reframe that makes every other service area possible.
The first inductive advantage available to most organizations is anomaly triage: detecting payment delays, quality deviations, supply disruptions, and process exceptions earlier by accumulating Bayesian weight-of-evidence rather than waiting for rule-based threshold breaches. Likelihood ratios expressed in decibans are auditable, additive, and update continuously as new evidence arrives. We design and deploy these systems for enterprise process environments.
Predictive classification from enterprise process data: demand forecasting, churn prediction, quality optimization, fraud detection, workforce planning, order lateness prediction. The competitive advantage is not the algorithm — it is the breadth of context fed to it. We design feature engineering strategies that pull from ERP transaction history, sensor data, customer interaction records, maintenance logs, and narrative data to build models that surface feature interactions no analyst specified in advance.
Language is the most ignored data source in most enterprises. Customer communications, project status reports, regulatory filings, competitive intelligence, internal narratives — all carry probabilistic signal about future states. Sentinel demonstrates the method at scale: 185 headline topics, co-integrated with S&P 500 price, producing 93% directional accuracy from text data alone. We apply this architecture to your domain — building custom topic-mining and co-integration systems tailored to your forecasting or risk environment.
Large language models are not productivity tools — they are capital-embedded infrastructure that raises effective output per knowledge worker across multiple workflows simultaneously. AI agents chain prediction, ranking, and bounded action into multi-step processes that operate within explicit governance envelopes. We design and deploy both: LLM integration into knowledge-work processes (drafting, summarizing, classifying, interpreting), and structured AI agent architectures for order-to-cash, procure-to-pay, and project management environments.
Derived from the Mundell comparative statics method, the governance framework identifies five policy instruments executives can directly control: training investment, model retraining frequency, contestability protection, AI deployment speed, and training data quality. We design and install the full governance architecture: mandatory stage gates before production, accountability mapping, delegation boundaries, drift monitoring, override authority, and the AI PMO as the institutional infrastructure through which all of this operates at speed and scale.
When every competitor deploys AI and front-end analysis converges to commodity, the source of competitive advantage shifts to commitment, accountability, and disciplined bold play. Drawn from Dubins-Savage bold-play theory and game-theoretic analysis of AI-saturated markets, this advisory work helps organizations identify where timid play is a dominated strategy, how semantic trail formation shapes the information structure of competition, and how to design governance architecture that makes decisive action sustainable across repeated engagements.
Sentinel, PRIMMS-GPT, and the Headline Intelligence demo are all operational instantiations of the Hybrid Cognition framework — statistical computation first, LLM interpretation second, human authority over every consequential decision.
A weekly S&P 500 forecast driven entirely by textual data. 185 headline topic series co-integrated with price. Bayesian statistical model produces locked quantitative outputs. Claude runs a five-layer Cortical Hierarchy analysis. Published every Monday, free to subscribers. The live demonstration of what narrative intelligence produces when applied rigorously.
A compiled MATLAB application computes Bayesian weight-of-evidence signals from schedule data, Voice of Team sentiment, and project documents. Claude receives those locked signals and runs a five-layer Cortical Hierarchy analysis — producing a sponsor-ready governance document. Six-posture jeopardy classification. Six failure archetypes. Trajectory projections at +2, +4, and +8 weeks. 17+ years of operational deployment history.
A working local retrieval-augmented generation demo over the Sentinel headline corpus — 773,000 vetted headlines spanning August 2022 through October 2025. Ask any question about narrative patterns, framing shifts, or topic coverage. MATLAB built the corpus. FAISS retrieves the evidence. A local LLM answers from only what was retrieved. No cloud API. This is what we build for clients — applied to your internal text data instead.
Whether you need a specific service area, a custom AI system built from the ground up, or a governance architecture for an AI program already underway — John Aaron and colleagues are the right starting point.