Pricing built around the scale of your operation.

Once we understand your core systems and operating structure, we can sketch what your annual fee would look like. We don't publish broad ranges because two operations with the same revenue can need very different data work, but here are three illustrative shapes from our customer base. Most engagements also include a one-time onboarding fee — see the next answer for what that covers and how it scales. The formal proposal always includes a fixed annual price in writing.

Sometimes — it depends on how much business logic Oxrow needs to encode for your operation. The fee covers embedding your operating context in our engine — your pool returns, contract structures, reporting hierarchies — so the AI behaves like a member of your team, not a generic chatbot. We charge for that work the way an ERP vendor does: a one-time fee, scoped and quoted up front. Simple, single-source connections generally don't require an onboarding fee, because the business logic carries over from similar customers we've already onboarded. As the number of source systems grows, so does the scope of the logic we need to encode.

Two layers — a live data foundation and an always-on AI agent. We scope the work before signing, so the annual price is fixed, not a running services meter.

Most customers should see year-one returns that exceed their annual investment through time saved, reporting automation, recovered margin, profit improvements, and avoided cost.

Initial returns often come from replacing manual reporting and data processes that consume staff time every week. We build every scope around a specific business case, not a generic software package, so you see the expected value before you commit.

That's often the right starting point. We can scope an initial source around a specific decision, prove value, and expand coverage once the next highest-leverage source is clear.

A growing catalog. Below is a representative sample — not an exhaustive list. We add new connectors with most releases, and our standard implementation can take on any system with a clean API or structured export. If yours isn't listed, we'll walk through it on the intro call.

Internal builds consume hidden costs — one ag analytics team we know spent 18 months and $1.2M before shipping their first production dashboard. Oxrow comes pre-wired for agriculture: the context engine, the data connections, the row-level security, and the operator-grade dashboards are already in place. And Oxrow's engineering team tracks data anomalies and keeps your business logic up to date. You get the insight at a fraction of the fixed cost of staffing an in-house team.

Generic AI lacks ag context, persistent data, and org-wide sharing. A BI tool lacks the intelligence layer that knows what a pool return is, how pack-out ties to block yield, or why your CFO and your agronomist are looking at the same cost differently. Oxrow is a permanent secure data layer + context-aware intelligence + live pipelines + dashboards — in one place.