AI Research Laboratory

Where Science
Meets Scale

Aluminar Research builds the intelligence layer for modern marketing — applying advanced data science and agentic AI to turn budget decisions from guesswork into science.

Data ScienceFoundation
Agentic IntelligenceOrchestration
Adaptive SystemsLearning Loop

Grounded in causal measurement at a leading global tech platform, go-to-market practice across enterprise and startups, and a history of innovative product development (past work: SXSW Innovation Award finalist)

Bayesian InferenceMonte CarloCausal InferenceMulti-Armed BanditsAgentic AI
In Production

Research, Applied

Our methodologies are deployed in production systems serving clients from early-stage startups to Fortune 150 enterprises. These systems score, orchestrate, and measure marketing campaigns across channels — with credible intervals on every decision.

The research areas below represent the technical foundations behind these platforms. Each discipline reinforces the others, creating a self-improving cycle that strengthens with every campaign.

Measurement

Cross-channel incrementality frameworks that provide independent, unified views of campaign impact — replacing self-reported platform metrics.

Orchestration

Agentic systems that handle campaign workflows autonomously — from brief generation to compliance — with human approval at every decision point.

Optimization

Adaptive scoring and budget allocation that learns from outcomes, narrowing uncertainty bounds and improving recommendations over time.

Research Focus

Core Research

Three converging disciplines that create compounding IP. Measurement informs agents, agents generate decisions, decisions produce outcomes that strengthen measurement — a self-reinforcing cycle.

01

Data Science

Every Decision Carries a Confidence Interval

Instead of single-number estimates, we produce full probability distributions — so you know not just what happened, but how confident you should be. Bayesian inference, Monte Carlo sampling, and causal inference methods propagate uncertainty through every layer, from creator scoring to budget allocation.

Bayesian inference with hierarchical priors
Monte Carlo sampling for uncertainty quantification
Causal inference for incremental measurement
Multi-armed bandits for exploration vs. exploitation
Portfolio optimization with diversity constraints
Multi-dimensional scoring with confidence propagation
02

Agentic Intelligence

Autonomous Systems with Human in the Loop

AI that acts, but never without guardrails. A coordinated network of domain-specialist agents handles briefs, discovery, compliance, and budgets — each with defined tool registries, safety constraints, and budget caps. A multi-stage quality protocol with independent judge panels catches what single models miss. Every verdict still requires human approval.

Domain-specialist agent network with coordinator
Multi-stage quality protocol with independent review
Autonomous compliance remediation
Guardrailed autonomy with human-in-the-loop
Persistent agent memory across campaign runs
Circuit breakers and adaptive retry logic
03

Adaptive Decision Systems

Every Campaign Makes the Next One Smarter

Every decision — scoring, routing, recommendation — is logged with its full context. Outcomes feed back into prior distributions automatically, tightening confidence curves and improving accuracy over time. As campaigns accumulate, uncertainty bounds narrow and subsequent decisions tend to improve.

Decision → Outcome → Prior Update cycle
Automatic outcome labeling from real results
Adaptive learning rates with drift guardrails
Confidence curves that compound over time
Our Approach

Technical Innovations

The technical approaches behind our production systems — measurement frameworks, scoring pipelines, agent architectures, and learning mechanisms.

Unified Marketing Framework

Ground-truth measurement and in-flight signals — in one framework.

True incrementality is the only honest measure of marketing performance — but it's a lagging indicator. You can't wait weeks to learn what's working. UMF solves this tension: it roots everything in causal, ground-truth measurement while simultaneously generating real-time performance signals you can act on today. One framework that tells you both where spend actually drove results and where to move budgets right now.

Ground Truth (MMM)

Causal

Causal incrementality measurement calibrated by real experiments. The slow, honest answer to "what actually worked?" — with credible intervals.

In-Flight Signals (MTA)

Real-time

Real-time performance indicators informed by ground-truth relationships. The fast, actionable answer to "what should we do right now?"

Bayesian Integration

Predictive

A Bayesian layer fuses both signals — producing in-flight predictions of eventual incrementality. You get the speed of MTA with the honesty of MMM, in real time.