AI systems that upgrade how you forecast, plan, and move inventory.

TAI Analytics blends AI, planning expertise, and retail data engineering to build decision tools that actually ship.

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Where we usually start

End-to-end AI consulting, focused on outcomes.

We work with a small number of teams at a time, helping them move from “we know we need AI” to concrete systems in production that change how planning and decisions happen.

Forecasting & demand systems

Build or upgrade forecasting models for items, locations, and channels. Tie them into planning tools so allocation, buys, and inventory flow are driven by real demand signals.

  • Promotion & uplift forecasting
  • Store / DC / online forecasts
  • New item launch behaviour

Planning engines & automation

Turn tribal logic into a clear engine: units, buys, allocations, and flows that planners can review, override, and trust – with full traceability.

  • Buy & allocation engines
  • Flow & replenishment logic
  • Scenario planning sandboxes

Executive analytics & AI assistants

Put one clean version of the truth in front of leadership, then layer AI on top to answer questions, surface anomalies, and tell the story in plain language.

  • Metric layer & KPI dashboards
  • LLM assistants on governed data
  • Ad-hoc analysis on demand

Recent work

Examples of the kind of work we ship.

Details are anonymized, but the constraints, stakes, and impact are real.

Planning engine · Apparel

Pre-season planning & allocation engine

Built an engine that turned manual Excel logic into a parameterized system: demand-driven buys, pack logic, and DC flows planners could review and override.

Impact: cut planning cycle time from weeks to days.

Forecasting · Grocery

Promotion & price-elasticity forecasting

Modelled promo uplift and cannibalization for key categories, feeding optimized forecasts into existing replenishment & allocation processes.

Impact: reduced stockouts and over-buys in promo weeks.

Analytics · LLM

Executive analytics & AI insights layer

Designed a metric layer and dashboards on the enterprise warehouse, then added an LLM-powered assistant to answer questions using governed, audited data.

Impact: reporting time reduced from days to minutes.