Autonomy. Authorized.
Heimel lets autonomous systems act in production without giving them uncontrolled power. Every consequential action is checked against current authority before it becomes real.
Move beyond copilots. Deploy AI workers with a boundary your organization can trust.
HEIMEL v0.2.0 is the current immutable public conformance release: 79 required vectors, formal models, a public reference implementation and signed release evidence.
Copilots make people faster. Autonomous systems create a second lever: more work can be completed without adding people at the same rate. The economic upside is capacity, operating leverage and 24/7 execution.
Security is not the product. It is what lets organizations capture that upside without surrendering authority.
annualized AI-assistant savings
Klarna reported that its AI assistant had engaged more than four million customers and was expected to deliver $40 million in annualized savings. The company also reported 11% lower operating expenses while 90% of employees used AI in daily work.
Source ↗equivalent support workload
Airtable reports an 80% AI resolution rate and says its agent now carries roughly the workload of 95 full-time support agents. Human staff concentrate on the harder work while AI absorbs volume.
Source ↗inquiries resolved without humans
Flashfood says AI handles 100% of incoming support tickets and resolves more than 90% without human intervention, including API-driven refund and account workflows.
Source ↗Move from answers and drafts to bounded execution.
Let agents complete a larger share of the workflow.
Keep consequential actions inside current mandates and constraints.
Grow completed work faster than human headcount.
External examples illustrate the economics of putting AI into operational work. They are not Heimel customer results and do not imply that Heimel caused these outcomes.
Why execution governance is missing
The World Bank’s Global Trends in AI Governance describes the gap directly: self-governance can be vague, non-binding and lack effective oversight or enforcement; high-level principles often do not explain how they are implemented in context.
The report points to technical standards as a way to operationalize responsible-AI principles, calls for governance across the lifecycle, says ex-post enforcement alone is insufficient, and emphasizes human involvement, auditability, empirical evidence and continuous monitoring.
Technical specifications, documentation, monitoring, audits and human involvement turn broad principles into mechanisms that can be assessed in practice.
The report does not specify a consequence-time authority protocol. Heimel addresses that narrower execution problem: resolve authority fresh, return deterministic ALLOW / DENY / ESCALATE, bind the permit to the exact effect, and retain evidence of what actually happened.
Independent direction of travel
The Alan Turing Institute’s frontier-AI risk work argues that model evaluation alone is not enough: safety has to be treated as a whole-system problem, including the controls around what an AI system can actually do.
It points toward limiting agent access and authority, strengthening controls as authority grows, preserving traceability, and using independently verified checking mechanisms before actions are allowed to execute.
Do not rely on model behaviour alone. Constrain the system around consequential action.
Resolve whether the exact effect is authorized under current authority immediately before it can occur.
Consequential execution proceeds only through the governed boundary, with the decision bound to the observed effect.
Models and agents can reason, plan and act. Heimel sits at the point where intent becomes consequence. It resolves current authority for the exact action and state, then allows, denies or escalates before execution.
Agent proposes real work.
Current authority is fetched fresh.
Authority is bound to the exact effect.
Authorized work proceeds through one path.
The observed effect is retained as evidence.
Open Heimel gives you local governed execution. Enterprise makes the same boundary operable across teams, agents and production workflows: authoritative policy and delegation, fleet-wide administration, enterprise evidence, assurance and deployment support.
The result is not less AI. It is more AI doing real work under explicit, current authority.
Run more agents and workflows without multiplying uncontrolled effect paths.
Manage organization-wide authority, delegation and policy.
Retain enterprise evidence, replay and attestation.
Deploy across cloud, private, sovereign or air-gapped environments with conformance support.
Heimel Enterprise is aligned with production value, not AI activity. We do not price the core settlement unit by seats, tokens, agents or model calls. A verified governed consequence is an authorized action that executed and has evidence of the observed effect.
The commercial model does not punish you for deploying more agents, models or automated workflows.
Settlement attaches to governed consequences with evidence that the authorized work actually occurred.
DENY, ESCALATE, failed execution and unverified effect settle at $0 by default. Contract terms define the applicable verified-consequence rate.
Approval cannot survive revocation, expiry or changed conditions.
A permit applies only to the state, action and effect authorized.
Missing authority or evidence never degrades into permission.
Execution authority is never a reusable bearer token.
Actions pass through the boundary or they do not happen.
Decision, execution and observed effect remain attributable.
Start with one consequential workflow: payment, deployment, records, approvals, infrastructure or another action you want AI to execute autonomously without surrendering organizational control.