One engagement model for both service lines.
Whether we're forecasting your inventory or your financials, the path is the same: start small, prove value on your own data, and scale only what works — so you see ROI fast and risk stays low.
From first look to measurable ROI — in weeks, not quarters.
The same four-step engagement model powers both service lines. You see real results on your own data early, and only scale what's proven — whether we're forecasting inventory or your financials.
Audit
We start with your data — mapping what you have, surfacing gaps, and pinpointing where AI will move the needle first.
e.g. Inventory: sales & lead-time history. Finance: GL, actuals & drivers.
Prototype
We build a working model on a focused slice of your data, fast — so you see real output against real history, not slideware.
e.g. Inventory: a forecast for one product line. Finance: a cash-flow projection or report.
Deploy
We integrate it into the systems your team already uses — ERP, planning tools, BI, spreadsheets — with no rip-and-replace.
e.g. Delivered where your planners or finance team already work.
Optimize
We monitor accuracy, retrain as conditions change, and expand coverage as the results earn it.
e.g. More SKUs and locations, or more reports and scenarios.
Timelines are typical ranges and vary with data readiness and scope.
What the output actually looks like.
A short demonstration of two example AI agents we've built — shown here running on synthetic data for a fictional company. Not real client data; the numbers are illustrative.
Analytics agent
Answers business-review questions in plain English — revenue drivers, store and regional performance, and recommended actions. The same approach powers our AI financial forecasting & reporting service.
Explore financial forecastingInventory operations agent
Flags at-risk SKUs, forecasts stockouts, and drafts reorders and warehouse transfers for approval. This is the working heart of our AI inventory demand forecasting service.
Explore inventory forecastingEvery screen in the demo is labeled as synthetic data for the fictional “Contoso” companies. Deployed agents are built on your data and systems.
Process questions, answered.
Timelines, data requirements, and what each step of an engagement actually involves.
A typical Resolv engagement reaches a working, deployed pilot in about four to six weeks: roughly one week for the data audit, two weeks to a working prototype on your own data, and deployment into your systems by week six. Timelines vary with data readiness and scope — the audit gives you a concrete schedule before anything is committed.
Find out what your data is already telling you.
Book a free data audit. We'll look at your numbers, show you where AI will pay off first — in inventory or finance — and leave you with a clear plan. No obligation, no jargon.