Trusted by
Trusted by
Demand, labor, risk, and supply models that move with events, mobility, economics, and local conditions.
Assistants for ops, real estate, and planning that can answer “what’s happening around this site?” from data, not guesses.
Agents that can reason about markets, neighborhoods, and routes using structured context, not just text.
Simple tools that give regional leaders and store/branch managers real‑world context alongside their KPIs.
One external data layer that supports many use cases instead of a different feed for every project.
You bring the models and use cases. Factori brings consistent real‑world signals.
Each dataset is designed to be easy for data teams to understand and simple to join to your own locations and markets.
Add external signals around stores, branches, hotels, depots, routes, and markets to cut error and explain variance.
Use the same real‑world layer to support forecasting, pricing, site selection, workforce, and supply chain models.
Feed copilots and decision agents with structured location, event, and market context so answers are specific and defensible.
Skip months of sourcing and cleaning external data; start from documented, production‑ready datasets.
Build models on top of concepts everyone understands: traffic, events, nearby businesses, local income, and spend.
What changed around this store that might explain last week’s drop?
Which new sites look most like our top performers in the real world?
If we open a depot here, how does that affect service levels and cost?
Where should we test this promo next, based on demand and competition?
What’s the risk profile of this branch or region compared to similar areas?
Heads of Data, AI, and ML Platforms
Applied ML, forecasting, and data science teams
Product teams building internal AI tools and copilots
Analytics engineers and data teams who own the warehouse/lakehouse
Strategy, ops, and finance teams who rely on AI‑powered decisionsFor example: demand forecasting, site selection, workforce planning, or a planning copilot for operators.
Start with 2–4 that clearly match your use case—for example, Mobility + Events + Economic for demand, or Places + People + Business for site planning.
Compare your current approach to “current + Factori data,” and review impact on accuracy, explanations, and user trust.
Factori gives AI models, copilots, and agents real-world context from mobility, places, events, economics, market demand, and local conditions.
Internal data shows what happened inside the business. Factori adds outside-in signals that explain what is changing in the real world.
Factori supports forecasting models, location-aware copilots, geo-aware LLM apps, field decision tools, and reusable feature pipelines.
No. Factori enriches your existing warehouse, lakehouse, models, agents, and applications with clean, ready-to-join external features.
Factori adds structured signals around stores, routes, depots, markets, and trade areas to help models reduce error and explain variance.
Yes. Factori links model outputs to understandable drivers like traffic, events, nearby businesses, income, spend, and local demand.
Factori removes months of sourcing, cleaning, and normalizing external data so AI teams can test real-world features faster.
Start with one flagship use case, add 2–4 relevant Factori datasets, and compare accuracy, explanations, and user trust before and after.
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