SmartInferProducts
Current product · private pilots
Marketing allocation
Galileo — the Marketing Scientist agent
Decision: How should the next marketing budget be allocated?
Galileo is built around a stricter measurement question: not only whether a model fits historical outcomes, but whether its estimates preserve decision value. It models channel response and uncertainty, checks whether effects can be separated from the available data, and withholds reallocations the evidence cannot support.
An evidence-grounded AI agent lets teams ask the kinds of questions they would put to a statistician or economist—including model-based counterfactual questions about budget allocation—with answers bounded by computed results, uncertainty, and credibility checks.
Channel contributions · response and saturation curves · budget scenarios · confidence estimates · evidence traces
Research benchmark. Across 90 synthetic datasets with known economic ground truth, Galileo 0.2 recorded the lowest median normalised decision regret among five evaluated methods. In paired comparisons, it outperformed Google Meridian on 55 of 90 datasets, with two ties; Meta Robyn on 85 of 90; and PyMC-Marketing on 67 of 90.
Galileo 0.2 is a separate SmartInfer Research estimator. The benchmark supports Galileo’s decision-science direction.
Evaluation design. The benchmark used nine controlled data-generating regimes with ten deterministic seeds per regime. Because the true channel-response curves were known, each recommended allocation could be compared with the oracle allocation. The comparison held observations, train/holdout slices, budgets, constraints, and the allocation procedure constant across methods. Results include regime-level outcomes, bootstrap confidence intervals, and Holm-adjusted paired statistical tests.
Evidence & research
- From Model Fit to Marketing Decisions → Experimental report · 90 synthetic datasets · decision-quality benchmark
- The Confident Wrong Number → Research note · synthetic identifiability demonstration
Continual software engineering
Software engineering
Monel — continual software engineering agent
Objective: Build software from the ground up and become better at engineering it as it evolves.
Monel is an engineering system designed to build new software and improve existing codebases. It explores competing solutions, independently verifies changes, and carries engineering experience forward.
Execution and verification produce a record of attempted changes, accepted results, failures, and repairs. The research goal is to turn accumulated experience into better engineering on the same evolving system.
Repository understanding · solution exploration · independent verification · learning from engineering experience
Research foundations
- Assurance Envelopes for Autonomous Coding Agents ↗ Research paper · selecting accumulated evidence for a later software change
- Early experiments in verified software generation → Experimental report · two early end-to-end systems · bounded verification and repair
In development
Retail specialists
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Shopping Associate AI agent Under development
Objective: How can a retailer help each customer accomplish their shopping objective through conversation and action?
A customer-facing conversation and action engine designed to behave like a human shopping associate within its permitted role. It talks naturally with shoppers, asks clarifying questions, searches the catalogue, and answers product, brand, and policy questions from grounded evidence. It can compare suitable options and use retailer tools to help process orders and returns, while stating when the available evidence cannot support an answer.
Underneath, SmartInfer’s agentic operating-system core manages memory and conversation state, invokes tools, grounds responses in catalogue and policy evidence, and governs permitted actions.
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Product Discoverability Agent Under development
Objective: How should a catalogue or product listing be transformed so search engines, AI shopping systems, and LLM-based interfaces can understand and retrieve its products?
An action agent that accepts a catalogue or individual product listing and returns a cleaned, enriched version. It improves titles, attributes, taxonomy, descriptions, structured metadata, comparison facts, and product relationships so products can be interpreted and retrieved more effectively by AI discovery systems. Outputs can be formatted for straightforward upload to Google, Amazon, and Walmart.
Contact
Work with SmartInfer
Interested in any of these directions? Talk to us.
Specialist-model development is a shared research capability supporting Monel and other SmartInfer systems. Explore specialist-model research →