Know what you are likely to sell next, so you can order and staff for it.
We train a multi-horizon, SKU-and-location-level forecasting model on your full sales history, calendar, promotions and price changes, then deploy it as a service that emits daily, weekly and monthly demand curves with prediction intervals. It backtests against your own held-out history so you see real accuracy before you trust it.
For products with zero sales history, we build a model that predicts first-season demand from attributes, pricing, comparable-item analogues and early pre-order signals. It transfers patterns learned across your existing catalogue onto the new item so launches are planned on evidence, not optimism.
We fuse your sales history with historical and forecast weather (temperature, rain, wind, heat indices) to model how conditions move demand for weather-sensitive lines. Deployed with a live weather feed, it turns the next 14 days of forecast into a demand adjustment per product and location.
We model visitor and footfall patterns for each of your locations using historical counts, day-of-week, seasonality, local events and weather. It outputs hour-by-hour traffic predictions so you can align opening hours, staffing and stock to when people actually walk in.
We forecast unit sales of each menu item per service period from your POS history, weather, day-type and events, so kitchens prep to demand. It flags items likely to sell out and those heading for waste before the shift starts.
We build a model that quantifies how nearby events — concerts, matches, conferences, holidays, school terms — shift demand at each of your locations. A live events calendar feeds the model so you get surge and lull alerts weeks ahead.
We decompose every product's sales into trend, multiple seasonal cycles and residual, then serve a clean baseline demand signal stripped of noise and one-off spikes. This gives planning teams a stable view of underlying demand and where it is genuinely accelerating or decaying.
We train a churn model on your subscriber or customer history — usage, billing, support tickets, engagement — that scores every account's probability of leaving in the next 30/60/90 days. It surfaces the specific risk drivers per account so retention teams act on causes, not just scores.
We combine demand forecasts with lead times, order costs, minimum order quantities and service-level targets to compute the optimal order quantity and timing for every SKU. Deployed against your inventory feed, it emits a daily buy list that hits your target availability at minimum holding cost.
We calculate SKU-specific safety stock from measured demand variability and lead-time variability at your chosen service level, rather than a blanket rule of thumb. It continuously re-tunes buffers as volatility shifts so you carry protection only where it's earned.
For shelf-life-limited goods, we forecast demand at daily granularity and optimise ordering against expiry dates to minimise both spoilage and stockouts. It recommends markdown timing to clear stock before it perishes.
We model your central warehouse and local sites as one network and recommend how to position and rebalance stock across echelons to meet local demand at minimum total inventory. It flags transfers that pre-empt local stockouts using site-level forecasts.
For intermittent, lumpy spare-parts demand we use methods built for sparse series (Croston-type and hierarchical models) to predict when and how many parts you'll need. It sizes stock to hit uptime targets without hoarding slow movers.
We continuously score each SKU-location for probability of stocking out before the next delivery, combining current on-hand, in-transit, forecasted demand and supplier reliability. It raises alerts early enough to expedite or substitute.
We predict per-location sell-through for each product and size/variant to recommend which items and how deep to carry at each site. It trims tail SKUs that don't earn shelf space and deepens winners before they stock out.
For businesses whose equipment breakdowns stop work. We train a model on your machine sensor data, usage and maintenance logs to flag machines heading toward failure, giving a ranked maintenance list.
We estimate the remaining useful life of critical components from degradation trajectories in your sensor and usage data, producing a survival curve per asset. It tells you not just that failure is coming but roughly when, with a confidence band.
We model breakdown risk for each vehicle in your fleet using telematics, mileage, fault codes, age and service history. Deployed as a service, it prioritises which vehicles to service next and predicts which are likely to fail on the road.
We learn the normal operating signature of each machine and detect deviations that precede faults, even for failure modes never seen before. Unsupervised models flag when a machine drifts outside its own learned envelope.
For businesses with heating, cooling and refrigeration across properties. We predict which units are drifting toward failure and estimate the comfort or spoilage risk, so you fix the right ones first.
We forecast warranty claim volume and cost by product cohort and manufacture period, and flag emerging quality issues from early failure patterns. It gives finance a reserve forecast and quality teams an early defect signal.
We forecast expected downtime hours per line and shift from failure-risk models, planned maintenance and historical stoppage patterns. Production planning gets a realistic capacity view instead of assuming 100% uptime.
We build a rolling 13-week and 12-month cash-flow forecast trained on your invoicing, payment behaviour, payroll, recurring costs and seasonality. Deployed against your accounting feed, it projects the daily cash balance with confidence bands and flags shortfall dates ahead.
We predict when each outstanding invoice will actually be paid, per customer, from historical payment behaviour, invoice size, terms and season. It converts your receivables ledger into a dated cash-inflow schedule.