SmartInferAbout SmartInfer
SmartInfer exists to make consequential decisions and actions measurably better.
Why specialist AI systems
High-value problems can justify more than a general-purpose model. When the cost of error is large, there is economic room to build a system specifically for the work.
SmartInfer combines domain-specific science, purpose-built agent infrastructure, and a trust layer to create AI specialists.
The system depends on the problem. Some specialists require custom execution, tools or controls. Some benefit from smaller specialised models when they improve cost, control or task performance. Their consequential outputs and actions must be checkable through evidence, constraints, tests or verification appropriate to the work.
The platform provides common foundations without forcing every specialist into the same architecture.
Further reading
The Missing Layer in Agentic AI → AI’s Edison Moment: The Moat Is the Grid, Not the Spark →
How we choose problems
We start with the economics of the problem, not the technology.
- Where is meaningful economic or operational value being lost?
- Which decision or action determines that outcome?
- Can a suitable AI system materially improve it?
- How will we know that it did?
We pursue a direction when there is a specific user, a plausible intervention, and a credible way to measure the result.
Why retail first
Retail provides a recurring scoreboard. Spend, revenue, promotions, discovery behaviour and customer response can be observed repeatedly, making it possible to test whether a system actually improves outcomes.
SmartInfer’s first commercial work is therefore in retail, beginning with marketing allocation through Galileo and extending to product discovery and product discoverability.
Founder
Anjan Goswami
Founder, SmartInfer
Anjan has spent two decades building and leading production search, ranking, machine-learning, and AI systems across Walmart, eBay, Adobe, Salesforce, Amazon, and Microsoft, most recently leading AI for PowerPoint Copilot.
He holds a PhD in computer science from UC Davis and an M.Tech from IIT Kanpur.
SmartInfer grew from a recurring lesson across that work: a more sophisticated model is often not sufficient by itself. The decision, its measurement, and the surrounding system determine whether technical capability becomes useful.