NeoSyntropyDeterministic runtime engine for LLMs
NeoSyntropy is a runtime engine that controls LLMs through finite state machines. Every action your AI takes follows a path you defined: 0% deviation from your business logic, enforced at the engine level, not begged for in a prompt.
Built for fintech and e-commerce workflows where improvisation is not an option.
# No prompt chaining. Just state control.
engine.state("checkout")
.on("payment_confirmed")
.transition("fulfillment")
.guarantee(deterministic=True)transition verified · O(1) · deviation 0%The paradigm shift
Autonomous agents improvise. In fintech and e-commerce, improvisation is a liability. A finite state machine is not an academic exercise here: it is the control layer that forces the AI to operate only inside states and transitions you predefined, so every decision is explainable, auditable, and compliant by construction.
Agent frameworks
Nobody can replay why the agent chose a path. Every run is a new roll of the dice.
The model can improvise steps you never approved, with real side effects.
Audits turn into log archaeology because there is no defined path to check against.
NeoSyntropy
Every workflow is an explicit finite state machine. The model can only move along edges you defined.
The engine blocks any move outside the graph before a side effect can happen. 0% deviation.
Each step is a logged, auditable state change. Reviews check a graph, not a transcript.
How it works
From a raw business event to a verified state transition, the engine never re-reads history and never leaves the graph you defined.
A live business event arrives: a checkout is submitted, a transfer is requested, a dispute is opened.
user eventGlimpse compresses the full context into a dense vector. No token re-reading, no prompt stuffing.
vectorThe vector is injected straight into the LLM's KV cache. Constant time, regardless of history length.
O(1) injectThe engine advances to exactly one predefined state. Verified, logged, and billed as a single unit.
verifiedUnit economics
Replace opaque token spend with one measurable unit your finance, product, and engineering teams can all agree on: the successful state transition.
THE OLD TOKEN APPROACH
Every step re-reads the entire history, so token costs grow exponentially with workflow length.
Retries, hallucinations, and self-correction loops all land on your invoice as billable tokens.
Usage-based token spend is a number finance discovers at month end, not one it can forecast.
OUR TRANSITION APPROACH
One successful state transition costs $0.005. The unit never changes, no matter how long the context.
Cost scales linearly with business volume, so AI spend becomes a line item finance can model.
Glimpse never resends history, and failed or hallucinated steps are never billable events.
Transitions, sessions, memory, knowledge, and traces are available the moment you connect an agent. Meter only verified outcomes, then improve the path with coding agents.
Stateless runtime, per-session isolation, JWT-based RBAC, and structured tracing. These are not abstractions you want to debug at 2am when an agent loop goes sideways.
Every workflow declares the invariants that must hold for a transition to count. If an axiom fails: wrong state, missing approval, failed guardrail, or invalid ledger write, the step does not bill and the system stops before bad logic compounds.
Guardrails, human-in-the-loop, and admin approval flows sit inside the runtime. Audit logs and post-execution hooks watch sensitive actions so business logic stays accountable.
One economic primitive
Model agent behavior as explicit states, ship deterministic workflows, and pay only when useful work completes.
Start buildingForecast AI spend by business volume, eliminate runaway token bills, and prove ROI at the unit-economics level.
Talk to SalesThe token era is ending