John Hoctor's Newton Research Deploys Media AI Agents That Stay With Your Data

The REVisionists: How John Hoctor's Newton Research Is Deploying an Army of Data Science AI Agents

Media has a math problem. Data volume grew. Decision windows shrank. Teams still run on limited analyst capacity. John Hoctor's Newton Research is answering with a platform of domain-trained AI agents that act like embedded data scientists across the media workflow.

These are not general chatbots. They are specialized agents trained on marketing science methods, media datasets, and the decision logic that practitioners use every day. As Hoctor puts it, "Our moat is specialization."

What Newton Is

Newton is a coordinated team of AI agents that plug into planning, targeting, buying, optimization, and reporting. Each agent is trained to handle specific analytical tasks that marketers repeat across campaigns.

Think of it as a method library with execution built in. Ask for an incrementality test, a media mix model, or in-flight optimization, and the agent knows the steps, assumptions, and data inputs-without brittle, one-off code.

• Planning: audience sizing, budget allocation, scenario modeling
• Experimentation: geo-tests, holdouts, lift studies
• Measurement: MMM, MTA-aware reporting, reach/frequency analytics
• Optimization: pacing, supply rebalancing, creative rotations
• Reporting: automated QA, anomaly detection, executive summaries

Where It Came From

Hoctor and co-founder Matt bring decades in TV measurement, including their time at Data Plus Math. The recurring pattern they saw: every company wants more analysts and cleaner, faster analytics. AI made it possible to productize that capacity and make rigorous methods accessible inside daily workflows.

Why Specialization Beats General Purpose

General LLMs like Gemini, ChatGPT, or Claude are broad. Ask them for MMM or a production-ready geo-test plan and you often get fragile code or vague steps. Newton focuses on media analytics only, with agents encoded to reflect real campaign constraints and edge cases.

Result: output that teams can use immediately-methods with defaults, diagnostics, and guardrails that match how practitioners actually work.

How Newton Learns

Newton is continuously trained with a "media analytics handbook" approach: named methodologies, worked examples, parameter ranges, failure modes, and evaluation criteria. When you request an incrementality design, the agent maps the request to vetted patterns and chooses the right method for the context.

Customers can add their own notebooks, scripts, and SOPs. The agents then "think like the team," not like a general chatbot.

Data Governance by Design

Newton runs as a containerized application where the data already lives. Zero data movement outside the customer's environment. Private training stays local and never feeds Newton's global models.

• Security: respects existing controls and access policies
• Compliance: no cross-tenant learning or sharing
• Reproducibility: versioned methods and datasets

Productivity Without Replacement

Hoctor is clear: Newton augments people. Teams that tried generic tools found they didn't behave like their analysts. Newton's agents are trained for media science, so practitioners spend less time debugging ad-hoc code and more time on experiment design, interpretation, and action.

From Reporting to Prediction in TV

Traditional measurement looks backward. Newton moves it forward. Agents run predictive modeling while campaigns are in flight, feeding insights directly into pacing, supply selection, and creative mix.

Teams shift from end-of-campaign recaps to continuous decisions. The effect is tighter feedback loops and less wasted spend.

Cooperation of Bots, Not a Battle

Interoperability matters. Hoctor points to emerging protocols like the Model Context Protocol (MCP) for agent-to-agent collaboration. If implemented well, specialists can coordinate without recreating walled gardens.

Learn more about MCP

What This Means for Scientists and Researchers

If your work touches media analytics, Newton's approach maps neatly to good science: defined methods, version control, peer-reviewed patterns, and measurable outcomes. The practical move is to formalize your team's tacit knowledge into agent-executable playbooks.

• Catalog your top 10 recurring decisions (e.g., budget shifts, frequency caps, geo tests).
• Standardize the methods, inputs, and diagnostics for each decision.
• Containerize data access and enforce role-based permissions.
• Add an evaluation harness: backtests, counterfactual checks, and error budgets.
• Instrument in-flight learning with clear override rules for humans.

Key Takeaways

• Specialization is the moat: agents trained on media science outperform general chatbots for analytics.
• On-prem deployment with zero data movement is now table stakes for enterprise adoption.
• Predictive, in-flight decisioning will replace retrospective reporting as the core value of measurement.
• Open protocols can let agents cooperate across tools without central gatekeepers.

If your team is leveling up skills for agent-assisted analytics and experiment design, consider structured paths that blend statistics, causal inference, and applied AI. A focused option: AI certification for data analysis.

Hoctor sums it up well: "Customers aren't handing over data-they're training an extension of their team." For media scientists, that means your methods scale, your standards hold, and your impact shows up in live decisions-not just in the deck.