The budget is enforced before the money moves.
Runaway AI spend breaks nothing. It just runs.
The market builds better statements — dashboards, alerts, and invoices that report a violation after the spend. Loriqa prevents the violation. Economic policy is evaluated in the execution path, on every action, ahead of the tokens being consumed.
One enterprise, one provider, one month. Spending controls were available on the platform — nobody had configured them. A control that fails open is a suggestion.
A multi-agent tool slipped into a recursive loop. Every dashboard stayed green the whole time, because nothing was failing. The only instrument that registered it was the invoice.
of agentic AI projects are projected to be canceled by 2027 — unclear ROI, escalating costs, inadequate controls. Not because the technology failed. Because nobody could prove it worked.
Whatever governs AI economics has to operate where the spend happens — at runtime, before consumption — not on a statement after it.
Control flows down. Not up.
AI economics is a cascade from intent to invoice. The market's tools start at the bottom and climb — but the layers that govern everything beneath them cannot be built from below. Loriqa starts at the top, because it was built there.
Four properties. One boundary.
Prevention of violation — not observation and report. The Budget Boundary is defined by what the architecture guarantees, not by what a dashboard displays.
Enforced before the spend
The platform evaluates before execution. A process that would exceed its budget is stopped ahead of the spend — elevated to a human for exception approval, or denied. The outcome is decided by policy set deliberately, in advance. The order of events is reversed: in bottom-up tools, spend happens and control reacts.
Nothing fails open
A design principle, not a feature. There is no unconfigured default that permits unlimited consumption — no forgotten setting that quietly authorizes a $500 million month. Spending beyond policy is not what happens when no one intervenes. It is what cannot happen unless someone approves it.
Every dollar attributed
Every unit of spend is captured where it happens — the agent that incurred it, the task it served, the model it ran on, the authorization behind it — recorded at execution in the same tamper-evident chain as everything else the platform proves. The CFO's question becomes a query. The regulator's demand becomes a report the record already contains.
Continuously optimized
A budget set once is a snapshot. The platform keeps redrawing the lines in your favor — evidence-backed recommendations from how your agents actually run, each reviewed and approved before anything changes. Caps limit damage. This reduces cost — structurally, permanently, compounding.
The boundary is not a wall
A boundary that only says no makes AI safer and smaller. The same architecture that controls the spend continuously finds ways to reduce it — built on the same foundation as everything else: the record.
Continuous Improvement Program · CIP
Evaluates deployed agents against the metrics that define value — cost, speed, accuracy — and produces specific, evidence-backed proposals: where spend concentrates, where runs take longer than the work requires, where budgets are over-provisioned against observed need. Every recommendation is reviewed and approved by you before anything changes.
Quality Insurance Authority · QIA
Answers the question most organizations have never had the instrumentation to ask: is each task running on the right model? QIA evaluates agents and tasks against multiple models and scores the results against your priorities — lowest cost, highest accuracy, fastest response. A measured answer, per task, to a question whose answer changes as the market does.
Deterministic offload · stop paying for intelligence where none is needed
When the record shows an agent performing the same pattern over and over, that pattern is a candidate to move off LLM execution entirely — same output, near-zero marginal cost, fully repeatable. A dashboard sees that an agent cost $40,000 last quarter. The record sees that $28,000 of it was the same routine performed four thousand times.
Governance is the enabler.
Every agent operates inside a budget that cannot fail open — so leadership never faces the invoice that triggers the freeze. Value is provable, and what is provable can be defended, renewed, and scaled. When the regulator asks for the governed account of your AI, the account already exists — because it was never a report to assemble. It was the architecture all along.