Agriculture data analytics, at decision grade.
For agribusinesses, cooperatives, food processors, and multi-farm operators whose records live in a dozen systems that were never designed to agree. We design and build the vendor-neutral data layer across them — integrated, governed, and maintained — so leadership, operations, auditors, and supply-chain partners are reading the same numbers. Senior-led. Architecture before software. Your data stays yours.
The operational problem
Agriculture runs on consequential data — planting and harvest records, input applications, supplier lots, shipments, quality results — held in systems chosen for the job each one does, not for how they combine. The ERP knows the money. The farm-management platform knows the field. Intake logs, scale tickets, lab results, and spreadsheets know much of the rest, in formats that match nothing else. The consequences are structural: a traceability question takes days of manual assembly instead of minutes; a supplier-performance conversation runs on anecdote; a sustainability report is rebuilt from scratch every season; and every executive question — which operations, suppliers, or acres are actually performing — gets a different answer depending on who assembled it. No single vendor platform fixes this, because the fragmentation is the business: many parties, many systems, one supply chain.
Many systems, no agreement
Fields, lots, suppliers, and seasons defined differently in every system — so joins are manual, slow, and quietly wrong.
Traceability & sustainability proof
Lot-level traceability and Scope 3 evidence demand key data elements at every handoff — retrievable in hours during an investigation, not reconstructed over weeks.
Performance nobody can compare
Supplier, site, and season performance compared across incompatible reports — so capital and attention go where the story is loudest, not where the evidence is.
The data we work with
The sources are rarely the problem. The agreement between them is. A typical architecture draws on:
Production records
Field and planting records, input applications, harvest and yield data, irrigation logs, greenhouse and environmental sensor streams.
Supply-chain records
Supplier and grower records, receiving and intake logs, lot and batch identifiers, pack and grade results, shipment and cold-chain data.
Business systems
ERP and accounting, CRM and member records, quality and lab results, contracts and settlements — and the operational spreadsheets nobody admits to running the business on.
Systems integrated
Vendor-neutral by design: the studio works across your existing systems — farm-management platforms, ERP and CRM, equipment and IoT feeds, quality systems, and the files that move between partners — rather than asking you to abandon them. Integration is API-first where the systems allow it, file-based where they don't, and documented either way: every flow has an owner, a schedule, and a failure alert.
What integration includes
- An inventory of every source system, its owner, and its data contract
- Master data for the entities that must agree: fields, lots, suppliers, sites, seasons
- Identity resolution across systems — the same farm, lot, or supplier recognized everywhere
- Lineage recorded end to end, so any number can be traced to its source records
What it deliberately is not
- Not a platform migration you didn't ask for
- Not a dashboard skin over ungoverned data
- Not a black box: definitions, models, and code are documented and yours
- Not a demo stack — the same architecture discipline runs our own production platform
Deliverables
Fixed in scope before work begins, with acceptance criteria in writing.
The foundation
- Architecture & data audit — systems inventory, data-flow map, definitions review, and a phased plan with dependencies
- Governed data layer — warehouse and models across your sources, with master data and lineage
- Metric dictionary & governance model — every number defined once, owned by name, change-controlled
- Integration build — API and file integrations with monitoring and failure alerts
The decision layer
- Decision-grade dashboards — supplier performance, traceability readiness, operations and yield views, executive reporting
- Traceability & evidence workflows — key data elements at critical tracking events, retrievable on demand
- Managed analytics operation — the system maintained, quality monitored, exceptions surfaced
- Monthly Decision Brief — what changed, why, and the next actions, reviewed with leadership (the practice, in full)
Governance & security
Agriculture data is commercially sensitive and, for growers and cooperative members, a matter of trust. The approach is consistent across every engagement:
Ownership, in writing
Your data, your definitions, your architecture — held in your infrastructure and exportable in open formats at any time. The studio operates under contract as a processor, never as an owner.
Access discipline
Least-privilege, named accounts — never shared credentials. Read-first access during design; every access listed in a register you can revoke line by line.
Pooling with consent
Where data is pooled — benchmarking across members, suppliers, or sites — consent, purpose, and aggregation thresholds are agreed before data moves. No party's operation is ever exposed to another.
A representative architecture
Every engagement differs in systems; the shape does not. Four layers, each independently owned and auditable:
Sources
Farm-management platforms · ERP & CRM · equipment and sensor feeds · supplier and lot records · lab and quality results · operational files
Governed data layer
Warehouse with master data for fields, lots, suppliers, and seasons · identity resolution · data-quality checks and lineage on every flow
Decision layer
Defined metrics from the dictionary — supplier performance, traceability readiness, yield and operations views, sustainability evidence — served to dashboards and reports
Operating cadence
Freshness and anomaly alerts · exception review · the monthly Decision Brief to leadership · quarterly architecture and priorities review
Decisions this improves
Commercial & operational
- Supplier and grower performance, compared on the same definitions across sites and seasons
- Where yield and margin are actually made — by field, site, input program, and operator
- Recall and investigation response: lot history assembled in hours, from records, not recollection
- Inventory, spoilage, and cold-chain exceptions surfaced while they are still correctable
Strategic & evidentiary
- Sustainability and Scope 3 reporting built from farm-level evidence, not seasonal reconstruction
- Cooperative member value: benchmarking returned to members under governance they approved
- Capital allocation — expansion, equipment, and program spend aimed where the evidence is
- Board and lender reporting drawn from governed systems instead of one-off assemblies
Why this studio, for this work
Most analytics firms have never operated inside the industry they measure. This studio has: we design, build, and operate Farm2Chef — a live food-systems platform holding a directory of 1,400+ gardens, farms, and markets across 44 states, with field-reported sightings, garden-crawl and passport activity, and event data flowing through it daily. The data problems on this page — fragmented sources, inconsistent definitions, evidence that has to hold up — are the problems our own platform survives. The practice is led personally by the studio's founder, Aubrey McCoy, a data scientist who has worked both sides of this industry: in the field and in the data. Senior attention is not a tier here; it is the structure.
Questions buyers actually ask
We already run a farm-management platform and an ERP. Are you proposing we replace them?
No. The studio is vendor-neutral: the work is a custom data layer across the systems you already operate — connected by API, governed under shared definitions, and owned by you. Platform replacements are occasionally right, but they are a decision to make with evidence in hand, not a precondition for getting value from your data.
Who owns the data — and the architecture?
You do. Data, definitions, documentation, and the architecture itself are delivered as your assets, in your infrastructure, exportable in open formats. Grower and member data in particular is governed under rules we agree in writing before the first integration: who may see what, at what grain, for what purpose.
How do you handle grower and member data trust?
As a design constraint, not a policy afterthought. Consent and purpose are recorded per data source; benchmarking is aggregated so an individual operation is never exposed to another party; access is least-privilege, named, and revocable. For cooperatives and producer organizations, the governance model — pooling rules, anonymization thresholds, member value returned — is designed before any data moves.
What does an engagement actually start with?
A paid architecture and data audit, typically four to six weeks: an inventory of systems and data flows, a definitions review, and a written architecture with phased scope, dependencies, and acceptance criteria. It ends with a decision document you can take to your board — including the option that the right answer is a vendor platform, or no build at all. Implementation follows only when the case is proven on paper.
We are not a technology company. Who runs this after you build it?
We do, if you want us to. Most clients keep the studio on as the operating team: the data system is maintained, its quality is monitored, and a monthly Decision Brief puts the interpretation in front of leadership. And because everything is documented and exportable, bringing it in-house later is a handover exercise, not a hostage negotiation.
The next step is a qualification call
Thirty minutes, with the practice lead — not a sales representative. Bring the question your systems can't answer consistently today. We'll tell you plainly what an architecture audit would examine, whether the case for a build exists, and whether this studio is the right firm for it. If it isn't, we'll say so and point you at the better fit.