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.

See How It Works →Open the Repository

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

Scores, forecasts, carbon estimates, biodiversity-credit assumptions, and institutional-readiness labels are not guarantees. They are planning and reviewing tools until supported by verified records, external standards, partner due diligence, and any required third-party validation.

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

Clone the repository

git clone https://github.com/wasalo/Kokonut-Intelligence.git
cd Kokonut-Intelligence

Configure environment variables

cp .env.example .env
# Edit .env with database passwords and API keys

Start local services

docker compose up -d
docker compose ps

Seed schemas and demo data

./scripts/seed.sh
./scripts/seed-pilot.sh

Open local tools

open http://localhost:8055  # Directus
open http://localhost:3001  # Metabase

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.

​Kokonut Intelligence turns farm activity into decision-ready evidence.

​Intelligence at a glance

​Why this layer exists

​How it works

​Architecture

​What it implements from the Knowledge Base

​Core capabilities

Multi-source ingestion

Governed farm records

Verification pipeline

Analytics and forecasting

Partner dashboards

Agent access

​Record lifecycle

​Farm scoring and opportunity mapping

​EAS attestation layer on Celo

​Celo contracts

​Registered schemas

​Roles and access

​Builder quickstart

​Local service endpoints

​What to build first

Improve farm data quality

Build MRV tooling

Create analytics modules

Design agent workflows

Improve dashboards

Strengthen privacy and access

​Claim safety rules

​Internal documentation map

​Next steps

Build with Kokonut

MRV Methodology

Common Data Schema

Kokonut × AI Agents

Ecological Impact Frameworks

Adelphi Data Hub

Kokonut Intelligence turns farm activity into decision-ready evidence.

Intelligence at a glance

Why this layer exists

How it works

Architecture

What it implements from the Knowledge Base

Core capabilities

Record lifecycle

Farm scoring and opportunity mapping

EAS attestation layer on Celo

Celo contracts

Registered schemas

Roles and access

Builder quickstart

Local service endpoints

What to build first

Claim safety rules

Internal documentation map

Next steps