Constitutional governance infrastructure

Making exploitation mathematically impossible.

Prahari.ai builds deterministic, formally verified governance for cooperatives, DAOs and autonomous AI systems — so that fairness is enforced by mathematics, not left to goodwill.

5integrated components
7constitutional laws
12 µsper constitutional evaluation
Lean 4machine-verified foundations
The stack

Five components. One constitutional operating system.

Rules are written once, checked against a constitution, verified for integrity and decided fairly — with an auditor coordinating every step.

Deterministic core

Algorithmic intelligence at the centre; AI and ML only augment accessibility, never the verdict.

Formally verified

Key guarantees are proved in a theorem prover, not just tested.

Open interface

The rule language is open source, so anyone can write and inspect governance.

Jurisdiction-agnostic

One universal engine; local law and domain specifics are configuration.

MLSC

Fair collective decisions

Multi-Layer Stakeholder Cooperative

Patent pendingMachine-verified

A governance protocol in which every stakeholder layer has a real voice — and no single layer can capture the outcome.

  • Cardinal preferences. Members express how much they support each option, not just a ranking — sidestepping the classic impossibility results of ranked voting.
  • Multi-layer veto. Workers, management, members and the platform each hold a check; proposals pass only with cross-layer consent.
  • Incentive-aligned by design. Honest participation is the rational strategy, so the system does not depend on trust.
  • Capture-resistant. Built to withstand concentration of voting power and vote manipulation.
KLK.AI

The constitutional auditor

Kanoon Logic Keeper

Patent pendingOrchestrator

A seven-module auditor that sits above the stack, coordinating GRDL, CFAIS, RITAM and MLSC into a single, accountable decision trail.

  • Apex orchestration. Routes every proposed action through the right rules, checks and decision process.
  • Auditable by default. Every verdict is traceable to the rule and the law that produced it.
  • Multi-model consensus. Where language models assist, independent models must agree before their output is relied on.
  • Human override preserved. Automation supports people with authority; it never replaces them.
GRDL

Governance, made computable

Governance Rule Definition Language

Open source · Apache 2.0Patent pending

A formal grammar for writing governance rules that humans can read and machines can execute — the same text for the auditor, the regulator and the runtime.

  • Human-readable. Bylaws, policies and agent permissions expressed in plain, structured statements.
  • Compiler-checked. Rules compile to deterministic evaluations; ambiguity is caught before deployment.
  • Templates included. Starting points for enterprise, DAO and AI-safety governance.
  • Open for adoption. The language is free to use, so governance can be shared and inspected openly.
# illustrative only
rule "treasury-withdrawal"
  when   action.type == withdraw and action.amount > limit
  require approval from every stakeholder layer
  law    Accountability, Human Override
  else   deny and log
RITAM

Integrity, in real time

Real-Time Integrity Through Algorithmic Mathematics

Patent pending · FRAND licensing

Named for the Vedic principle of cosmic order, RITAM continuously checks that a governed system stays within its mathematical invariants — on every action, not at audit time.

  • Multi-law verification. Twelve invariants checked together, so compliance with one law cannot quietly break another.
  • Deterministic. The same input always yields the same verdict — reproducible for regulators and courts.
  • Continuous. Integrity is enforced as the system runs, rather than discovered after the damage.
  • Licensable. Available to implementers on fair, reasonable and non-discriminatory terms.
CFAIS

A constitution for autonomous systems

Constitutional Framework for Autonomous Intelligent Systems

Patent pending12 µs / evaluation

Seven constitutional laws that every autonomous system must satisfy — with mandatory graceful degradation when it cannot.

  • Evaluation engine. Every proposed action is checked against the seven laws in about twelve microseconds.
  • Fails safe. When a law cannot be satisfied, the system steps down to a safer mode instead of failing open.
Human Dignity
Transparency
Accountability
Human Override
Self-Limitation
Value Alignment
Graceful Degradation
Where it applies

One engine, three kinds of institutions.

Cooperatives

Member-owned institutions where every stakeholder layer keeps a meaningful voice, and records stay tamper-evident.

DAOs

On-chain communities governed by cross-layer consent rather than token weight alone.

AI agents

Autonomous systems that act within declared constitutional limits — and degrade gracefully at the edge of them.

Research

Peer-reviewed and presented.

European DAO Workshop 2026 · DAWO26

Cardinal Preference Aggregation with Multi-Layer Veto Mechanisms for DAO Governance: A Machine-Verified Approach

Accepted and presented at the European DAO Workshop 2026, introducing MLSC to the decentralised-governance research community.

The full paper will appear in the Springer conference proceedings. Paper contents and formal proofs are not reproduced on this site.

Author
Jaganmohan V.S. Kataru
Venue
Winterthur, Switzerland · 6–7 July 2026
Publication
Springer LNBIP (forthcoming)
About

Rooted in how cooperatives really work.

Prahari.ai is founded by Jaganmohan V.S. Kataru, drawing on three decades in IT infrastructure and security, a background in agricultural economics, and a family history in cooperative administration. Based in Bengaluru.

Contact

Pilots, research and licensing

For pilot partnerships, research collaboration or licensing enquiries:

cjags@prahari.ai