Kokonut Intelligence turns farm activity into decision-ready evidence.
Every harvest record, soil reading, satellite observation, expense entry, MRV event, EAS attestation, Data Hub metric, and AI-agent output needs a single trusted place to be structured, queryable, and reviewable. Kokonut Intelligence is that layer. It sits between real-world farm activity and the systems that depend on it: farm operators, DAO reviewers, grant funders, impact analysts, public dashboards, AI agents, and future partner integrations.Built for farm operators, DAO reviewers, data contributors, developers, MRV analysts, and agent builders.
Kokonut Intelligence is not a replacement for MRV methodology. MRV defines how farm activity becomes evidence. Kokonut Intelligence implements the data infrastructure that stores, validates, analyzes, attests to, and exposes evidence.
Intelligence at a glance
| Question | Short answer |
|---|---|
| What is it? | The canonical data and analytics backbone for Kokonut farms. |
| What does it store? | Farm registry records, operations, harvests, expenses, sales, soil data, remote sensing, MRV events, attestations, partner data, and agent logs. |
| What does it power? | Data Hub dashboards, MRV workflows, EAS attestations, EBF reports, CRISP risk analysis, farm scoring, forecasts, and AI-agent workflows. |
| Who uses it? | Farm operators, DAO members, Guild contributors, grant reviewers, partners, developers, and agents. |
| What is live? | Repository architecture, local stack, canonical schemas, farm/MRV data model, Directus, ClickHouse, PostgreSQL, Metabase, Celo EAS integration, and developer quickstart. |
| What should be treated carefully? | Forecasts, scores, carbon or biodiversity-credit claims, institutional-readiness language, and partner-facing outputs until supported by verified records and external due diligence. |
Why this layer exists
Regenerative farms generate many kinds of records. Without a governed intelligence layer, those records become fragmented across spreadsheets, screenshots, wallets, dashboards, reports, sensors, notebooks, and chat messages. Kokonut Intelligence gives the network a shared source of truth.| Without Kokonut Intelligence | With Kokonut Intelligence |
|---|---|
| Farm data is scattered across tools | Farm data is structured into canonical records |
| Impact claims depend on narrative | Claims can reference MRV events, evidence CIDs, and attestations |
| DAO reviewers inspect one-off documents | Reviewers query consistent data across farms |
| Agents need direct database or manual access | Agents use scoped access, manifests, and audit logs |
| Forecasts are hard to compare with actuals | Forecasts can be checked against harvests, losses, sales, and MRV records |
| Annual reports are manually assembled | Reports can be generated from governed snapshots |
How it works
The core idea is simple: farm events should become structured records before they become public claims. That means every important claim should be traceable back to:- a farm ID
- a timestamp
- a source or operator
- a record type
- a review status
- a payload or evidence file
- An attestation or report reference when applicable
Architecture
Kokonut Intelligence is a six-layer stack. Each layer has a specific job.| Layer | Technology | Role |
|---|---|---|
| Canonical core | PostgreSQL, PostGIS, Directus | Source-of-truth records, APIs, role permissions, admin UI, workflows |
| Analytics | ClickHouse | Time-series analysis, sensor aggregation, event queries, performance reporting |
| BI | Metabase | Internal dashboards, operational summaries, and ad-hoc analysis |
| Intelligence services | Python | Forecasting, scoring, ecological analytics, opportunity maps, report generation |
| Verification | EAS on Celo, off-chain signed claims | Attestations for MRV, impact, harvest, financial, and compliance records |
| Blockchain ingestion | RPC, subgraphs, Foundry | Wallet activity, attestations, treasury events, DAO data, resolver contracts |
Chain clarification: Gnosis Chain hosts Kokonut DAO governance contracts and treasury execution. Celo hosts farm-data attestations through EAS. Governance capital and farm evidence are connected, but they are not on the same layer.
What it implements from the Knowledge Base
| Knowledge Base concept | Intelligence implementation |
|---|---|
| Common Data Schema | farm_registry_record and validation services for the 13-field farm record |
| MRV workflow | Remote sensing, sensor readings, field observations, MRV events, evidence payloads, and attestation requests |
| Data Hub | Directus and ClickHouse-backed APIs that expose farm data and metrics |
| EAS attestations | Celo schemas, attestation records, resolver-gated publishing, onchain/offchain modes |
| EBF reports | Generated snapshots based on verified ecological and operational records |
| CRISP review | Risk factors derived from ecological, governance, and evidence-quality data |
| 8 Forms of Capital | Metrics that classify farm value across natural, financial, social, human, material, intellectual, cultural, and health capital |
| AI agents | Agent identity, capability manifests, scoped tokens, agent tasks, and action logs |
Core capabilities
Multi-source ingestion
Pull farm records from field logs, Directus entries, sensors, weather APIs, satellite imagery, blockchain data, and attestation indexes.
Governed farm records
Store farm registry entries, crop cycles, harvests, expenses, sales, losses, MRV events, soil readings, and partner data with clear lifecycle states.
Verification pipeline
Convert eligible records into evidence payloads, IPFS/Filecoin references, Celo EAS attestations, public dashboards, and annual reports.
Analytics and forecasting
Generate farm scores, revenue forecasts, loss-rate analysis, ecological trends, opportunity maps, and metric snapshots.
Partner dashboards
Expose buyer, funder, vendor, operator, and internal dashboards with role-based and row-level data controls.
Agent access
Let AI agents read canonical data and write verified outputs through scoped access, MCP integration, and full audit logging.
Record lifecycle
Every operational record should pass through a review flow before it becomes trusted enough for dashboards, reports, or attestations.| Status | Meaning |
|---|---|
| Draft | A record exists but is not ready for review. |
| Submitted | A field worker, integration, or supervisor has submitted it for review. |
| Verified | A manager, finance reviewer, analyst, or authorized process has approved it. |
| Published | The record can be used in dashboards, reports, API responses, or attestations. |
| Rejected | The record needs correction before it can become evidence. |
Farm scoring and opportunity mapping
Kokonut Intelligence can help rank and compare farms, but scores should be treated as decision support, not automatic certification.| Tool | What it helps with | Trust requirement |
|---|---|---|
| Farm score | Compare financial, ecological, governance, and growth performance | Requires clean records, transparent weights, and reviewable metric definitions |
| Revenue multiplier map | Identify possible revenue-uplift opportunities | Requires market assumptions, operator review, and actuals tracking |
| Revenue forecast | Model scenarios across revenue, NOI, yield, and cash flow | Requires forecast-vs-actual comparison and sensitivity analysis |
| Ecological analytics | Track soil, vegetation, biodiversity, and water indicators | Requires baselines, consistent measurement periods, and MRV methodology |
| Governed metrics engine | Version and compute metrics across farms | Requires source lineage and change governance |
EAS attestation layer on Celo
Kokonut Intelligence uses EAS on Celo for farm-data attestations. This separates governance execution from farm evidence:| Chain | Kokonut role |
|---|---|
| Gnosis Chain | DAO governance, treasury, membership, proposal execution |
| Celo | Farm evidence, MRV attestations, impact records, harvest proofs, compliance records |
Celo contracts
| Contract | Address |
|---|---|
| EAS v1.3.0 | 0x72E1d8ccf5299fb36fEfD8CC4394B8ef7e98Af92 |
| SchemaRegistry | 0x5ece93bE4BDCF293Ed61FA78698B594F2135AF34 |
| KokonutResolver | 0x6E1502c7a14b45aba5FC420dC92C1E3b38BD79Ad |
| Kokonut multisig | 0x03779B674CbCBfc0B801c4cAc9DFaC8aACbbD5c5 |
Registered schemas
| Schema | Use case |
|---|---|
kokonut-mrv | MRV claims: location, crop, quantity, evidence CID |
kokonut-impact | Environmental impact: soil carbon, biodiversity index, vegetation indicators |
kokonut-financial | Financial summaries: NOI, revenue, costs, farm score snapshots |
kokonut-harvest | Harvest verification: quantity, quality grade, date, operator |
kokonut-compliance | Partner compliance: audit trails and contractual adherence |
Sensitive data should not be pushed publicly by default. Kokonut Intelligence supports off-chain records, onchain hashes, selective disclosure, and future privacy-preserving proof layers for records that should not be fully public.
Roles and access
The Intelligence Layer should make data useful without making every user an administrator.| Role | Access level | Main responsibility |
|---|---|---|
| Administrator | Full access | Platform configuration, schemas, permissions, infrastructure operations |
| Field Worker | Create and read your own location records | Activities, harvests, expenses, sales, losses, field notes |
| Supervisor | Read and submit | Review drafts and submit records for verification |
| Manager | Approve operational records | Verify operations, field data, crop records, and milestones |
| Finance | Approve financial records | Verify expenses, sales, payments, and revenue events |
| Analyst | Read verified and published data | Dashboards, metrics, forecasts, reports, and DAO review support |
| Agent | Scoped access only | Read or write specific records through capability manifests and audit logs |
Builder quickstart
Local service endpoints
| Service | URL | Purpose |
|---|---|---|
| Directus | http://localhost:8055 | Schema management, admin UI, API, primary data entry |
| Metabase | http://localhost:3001 | Internal dashboards and BI |
| ClickHouse HTTP | http://localhost:8123 | Analytical queries |
| PostgreSQL | localhost:5432 | Canonical data store |
What to build first
Improve farm data quality
Add validation rules, import tools, field-worker UX improvements, or better error messages for farm records.
Build MRV tooling
Create ingestion, QA, visualization, or attestation workflows for satellite, sensor, drone, or field-observation data.
Create analytics modules
Improve loss-rate analysis, farm scoring, ecological trends, forecast-vs-actual tracking, or revenue opportunity maps.
Design agent workflows
Build agents that read canonical data, produce bounded outputs, submit MRV events, and leave full action logs.
Improve dashboards
Build clearer dashboards for operators, DAO reviewers, buyers, funders, vendors, or public visitors.
Strengthen privacy and access
Improve scoped permissions, selective disclosure, partner dashboards, private data handling, and audit trails.
Claim safety rules
Use Kokonut Intelligence outputs carefully.| Claim type | Safe framing |
|---|---|
| Forecasts | Scenario estimates that should be compared against actuals. |
| Farm scores | Decision-support signals based on metric weights and available data. |
| Carbon outcomes | Climate co-benefits are not verified through approved methodology and third-party standards. |
| Biodiversity claims | Evidence-backed observations, species records, and trends, not automatic credits. |
| Financial performance | Records and projections, not guaranteed returns. |
| Institutional readiness | A review pathway that requires due diligence, not a status the system grants by itself. |
Internal documentation map
The repository includes deeper documentation for each subsystem.| Document | What it covers |
|---|---|
docs/architecture.md | System overview, data flows, security model |
docs/data-dictionary.md | Tables, fields, metrics, and relationships |
docs/api-reference.md | REST, GraphQL, and ClickHouse API reference |
docs/openapi.yaml | OpenAPI 3.0 specification |
docs/attestation-guide.md | EAS on Celo, schemas, attestation modes, private data, CLI |
docs/agent-access.md | MCP integration, scoped tokens, and audit logging |
docs/deployment.md | Docker setup, environment variables, and backup procedures |
docs/sandbox.md | Developer quickstart and hello-world tutorial |
Next steps
Build with Kokonut
Repositories, contracts, schemas, MRV primitives, contribution rules, and builder paths.
MRV Methodology
How farm activity becomes structured evidence, public records, attestations, dashboards, and annual reports.
Common Data Schema
The 13-field farm record that makes farms comparable, fundable, governable, and verifiable.
Kokonut × AI Agents
Agent identity, scoped access, MCP integration, x402 payments, and verifiable agent outputs.
Ecological Impact Frameworks
How EBF and CRISP interpret verified farm data for reporting and risk review.
Adelphi Data Hub
Explore live farm data, MRV events, harvest records, and impact metrics for Kokonut’s reference farm.