Your history
knows the future.
Machine-learning data engineering over your historical costs, orders, quotes, and production metrics — turned into forecasts, dashboards, and analytics your team actually opens.
Illustrative console with sample data. Your dashboards are configured to your metrics, your sources, and your thresholds.
Built on the records
you already keep
Years of costs, quotes, orders, and production numbers are sitting in your systems doing nothing. This is what we build on top of them.
ML data engineering
Historical project costs, quotes, change orders, production metrics, and procurement records — cleaned, deduplicated, joined across systems, and structured into datasets a model can actually learn from.
Demand & cost forecasting
Models trained on your own history to sharpen estimating — what a job should cost, what next quarter's demand looks like, where a quote is off. Delivered as ranges, not false precision.
Vendor & supplier analytics
Pricing trends per vendor, lead-time reliability scoring, and quote-versus-invoice variance — so negotiations start from your data, not their rep's memory.
Inventory forecasting
Reorder points and stockout risk computed from your consumption history and seasonality — carry less capital in stock while running out less often.
Operational dashboards
Live KPIs, exception queues, and aging views that update themselves — built around the questions your Monday meeting actually asks.
Computer vision analytics
Asset utilization, inventory movement, and facility or fleet activity measured from camera feeds you already have. Reviewed and privacy-conscious — scoped to operations, never to watching people.
From raw exports to decisions
The same four moves every engagement. What changes is your data — and how honest it is about itself when we first open it.
Ingest
Spreadsheets, ERP and accounting exports, quoting systems, sensor and camera feeds — in whatever shape they're actually in. History trapped in PDFs? Document intelligence extracts it first.
Engineer
Cleaning, deduplication, joining across systems, and structuring into model-ready datasets. The unglamorous 80% of the work — and where most analytics projects quietly die.
Model
Forecasting, anomaly detection, and scoring — trained on your history, backtested against it, and shipped with error ranges attached.
Deliver
Dashboards, scheduled reports, and alerts in the tools your team already opens. An insight nobody sees didn't happen.
Backtested before you bet on it
Before a model reaches your team, it predicts the past. We hold out recent months of your own history, forecast them blind, and show you exactly how wrong it would have been. If it can't beat your current method, we tell you — and stop there.
In production, forecasts ship as ranges with confidence levels, not single numbers. And decisions above thresholds you set — a large purchase order, a price change — wait for a human sign-off.
- Backtest reportdelivered with every model, on your data
- Drift monitoringwatched in production — see AI ops & security
- Human checkpointsbefore big-dollar decisions — our commitments
$ lx backtest demand --holdout 6mo
window 2025-10 → 2026-03
MAPE 6.8% (current: 11.4%)
p50 range ±4.1%
p90 range ±9.7%
verdict ship, with review gateAsked in almost every consultation
How much historical data do we need?
Do you replace our BI tool?
Where does our data live?
What about privacy with computer vision?
Bring three years of spreadsheets. Leave with a forecast.
One consultation. We look at the records you actually have, tell you what they can predict — and what they can't — and scope the build from there.