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.
We forecast revenue by segment, product line and channel using bookings, open deals, seasonality and wider economic drivers. It produces a base, upside and downside range each period so leadership plans on a range, not a single number.
For project-based businesses we predict which jobs will overrun budget or schedule, using early progress metrics, change orders, resource loading and historical outcomes of similar projects. It flags at-risk projects while corrective action is still cheap.
We forecast the components of working capital — receivables, payables and inventory — and their net position over the coming quarters. It shows how growth, seasonality and terms changes will consume or release cash.
We forecast required staffing by role, day and hour from predicted demand, service-time standards and coverage rules. Deployed against your scheduling data, it converts a demand curve into a headcount-by-hour requirement each site needs.
We predict attendance for each scheduled class or session from bookings, weather, historical show rates and instructor and time-slot effects. It informs which classes to run, resize or promote.
We forecast seasonal peaks in labour demand months ahead and produce a hiring-and-ramp plan that accounts for recruitment and training lead times. It tells you when to start hiring, not just how many people you'll eventually need.
For businesses unsure how customers react to price changes. We measure how sensitive demand is for each product from your past price changes, separating out promotions and seasons, and show where you can raise prices and where you cannot.
For businesses with limited capacity or stock, like rooms, seats or seasonal goods. Our pricing engine recommends prices in real time from demand, remaining capacity, competitor prices and sensitivity, always within the floors and ceilings you set.
We estimate customer willingness-to-pay and optimal price tiers from purchase data, quote acceptance, and where relevant conjoint-style surveys. It informs packaging and tier design so you price where demand actually sits.
It monitors competitor pricing from official APIs, licensed price feeds or sites that permit automated access, and predicts how your volume and margin respond to their moves and to your counter-moves. It recommends whether to match, hold or diverge on each item based on measured cross-effects.
We train a model that scores each upcoming appointment for no-show probability using booking lead time, history, reminders, weather and slot type. Deployed into your scheduling flow, it drives targeted reminders and smart overbooking.
We predict which reservations are likely to cancel and forecast net expected arrivals per date, using lead time, rate type, source channel and history. Revenue managers plan overbooking and staffing against expected true occupancy.
For businesses deciding how much to spend winning customers. We predict each customer's future value from their purchase history, so marketing can set budgets and focus on the most valuable groups.
We predict long-term customer value from only the first days or first order, so you can identify future high-value customers before they've spent much. It transfers patterns from mature cohorts onto brand-new customers.
We predict when each customer is next likely to buy and what, from inter-purchase intervals, category cadence and lifecycle stage. It powers well-timed replenishment reminders and next-best-offer campaigns.
For businesses whose sales team must decide which accounts to call. We predict the size and margin of each customer's next order and combine it with how likely they are to buy now, ranking accounts by the value of contacting them today.
We predict which free-trial or freemium users will convert to paid, from onboarding actions, feature usage depth and engagement velocity. It flags high-intent trials for sales touch and low-intent ones for nudge automation.
We forecast electricity and gas consumption at building and portfolio level from meter history, weather, occupancy and operating schedules. Deployed with live weather and calendar feeds, it projects the load curve hours to days ahead.
We predict the timing and magnitude of peak energy demand events that drive demand charges, and recommend load-shifting or curtailment actions ahead of each. It targets the specific intervals that dominate your bill.
We forecast expected water, gas and electricity usage per site and flag when actual consumption diverges — catching leaks, faults and waste. It separates weather-normal variation from genuine anomalies.
We forecast charging demand across your sites and connectors by time of day from session history, local traffic, events and weather. It informs capacity planning, dynamic pricing and load management for charging operators and fleets.
We predict customer wait times in real time from current queue state, staffing, service-time distributions and arrival forecasts. Deployed to your app or displays, it gives customers accurate estimates and managers early congestion warnings.
We predict accurate delivery and service-arrival times from route, traffic, historical leg durations, stop counts and driver patterns. It replaces flat time-window promises with data-driven ETAs and flags jobs likely to run late.
We predict how long each job or appointment will actually take from job type, customer, technician, parts and history, replacing flat booking slots that cause overruns and idle gaps. It feeds tighter, more accurate scheduling.