Demand Forecasting on Lightspeed, for technology
Ward reads sales transactions, inventory counts, customer profiles from Lightspeed Retail, runs demand on it, and sends the technology read each day.
How a Head of IT runs demand off Lightspeed Retail
Demand Forecasting. Ward combines historical patterns, weather data, local events, and economic signals to forecast demand at the store-SKU-day level.
The business wants AI. You sign off on the architecture. Ward filters to what a Head of IT can act on and drops the rest.
What Ward does with that: Ward builds store-level demand models incorporating seasonality, weather forecasts, promotional calendars, local events, and macroeconomic indicators.
Setup: Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access.
Key capabilities
- Weather-driven adjustment
- Event and holiday modeling
- Automatic reorder point recalculation
- Store-SKU-day level precision
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
Reporting
Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.
| Model | Horizon | MAPE |
|---|---|---|
holt_winters | 4wk | 4.1% |
arima_sarimax | 13wk | 8.9% |
gbm_demand | 1wk | 2.1% |
bayes_hier | new store | 11.4% |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"inventory_shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Why this combination
is its own problem.
Demand Forecasting needs sales transactions and inventory counts at minimum. Lightspeed Retail carries both, at the grain the model needs. That is the whole integration story: no middleware, no staging warehouse, no custom extract.
What Ward has eyes on.
The connection to Lightspeed Retail is read-only and runs on your schedule. Ward ingests sales transactions, inventory counts, and the rest of the feed, then combines it with external context your POS does not carry: weather, local events, competitor pricing.
The demand model runs every morning, not on a reporting calendar. It spots the pattern, accounts for what caused it, and attaches a recommended move before the number reaches a review deck.
What the first 90 days
actually look like.
-
01
Week 1: connect
Read-only credentials to Lightspeed Retail. Ward reads sales transactions, inventory counts, customer profiles and starts building baselines. First findings land inside 48 hours, before baselines are stable, so you can see the shape of the output early.
-
02
Weeks 2 to 3: calibration
Baselines stabilize per store and per category. Ward stops flagging normal variance and starts flagging exceptions. This is the window where the false positive rate drops sharply.
-
03
Weeks 4 to 12: operating rhythm
Cards arrive every morning and get triaged like any other queue. Most teams find the volume settles into something one person clears in ten minutes. What matters is the action rate, not the alert count.
How Ward connects to Lightspeed Retail
Ward integrates with Lightspeed Retail POS for specialty and boutique retailers. In-store transaction data powers customer behavior and assortment insight cards.
Setup: Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access.
Data Ward reads from Lightspeed
Impact metrics with Lightspeed
Data lake enrichment
Ward enriches Lightspeed data with: Sales transactions, Customer profiles, Weather & events, Competitor data, Demographic data
The business wants AI. You sign off on the architecture.
- ×Business sponsor already chose the vendor. You inherit the security review
- ×Every AI vendor wants write access and a copy of the production data
- ×Model lock-in means rewriting the stack when GPT or Claude moves again
- ×Audit trail is an afterthought. Compliance has nothing to pull on
- ×Data lake project keeps getting bumped for the next thing the business wants
- ✓Federated query: data stays in your warehouse. No copies, no shadow lake
- ✓Read-only credentials. Cedar policies enforce least-privilege per agent
- ✓LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys
- ✓Every query, every model, every source logged. SIEM-ready audit output
- ✓VPC peering, PrivateLink, SOC 2 II. Your security review is short
74% of enterprise AI projects stall before production. Integration debt and security review are the top two reasons. Source: Gartner
Frequently asked questions
Ward connects via Lightspeed Retail API. OAuth-based authentication. Read-only access. Data points include: Sales transactions, Inventory counts, Customer profiles, Purchase orders, Employee performance.
The business wants AI. You sign off on the architecture. Ward solves this with automated insight cards: Federated query: data stays in your warehouse. No copies, no shadow lake. Read-only credentials. Cedar policies enforce least-privilege per agent. LLM-agnostic. Anthropic, OpenAI, Gemini, Ollama. Bring your own keys.
First insight cards arrive within 48 hours of data connection. Ward needs approximately 2 weeks to establish stable baselines for your specific operation.
No. Ward sits on top of your existing stack. It is the proactive intelligence layer that watches your data continuously and delivers insight cards, so your team acts on findings instead of hunting for them.
Related solutions
See what demand problems Ward catches.
Root causes, not just alerts. See it on your data.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.