Move Fast
Build ReliableTM
Govern generative AI with the disciplines enterprises already trust.
Move fast. Existing governance. One endpoint change.
Build reliable. Runtime control. Audit-ready evidence.
Powered by Equilibrium-Constrained DecodingTM
The runtime control layer for generative AI systems.
A control layer between
the workflow and the model.
Assiduity's Equilibrium-Constrained DecodingTM
translates enterprise objectives, procedures, controls, and policies
into semantic contracts that guide generative AI while it works.
Derived from policies, procedures,
requirements, and project artifacts
Branching decisions
Governance signals
Use existing governance artifacts Provider-agnostic No retraining OpenAI-compatible proxy One API endpoint change
AI systems do not only fail. They drift.
AI systems are being asked to perform governed work, but they are often managed by prompts or post-hoc review. Prompting defines intent before generation begins. Post-hoc review checks the result after it ends.
They are not procedures. They are not controls. They do not provide reviewable evidence that the system pursued the organization’s intent.
Not guardrails.
Not evals.
Not a bigger model.
Most AI governance tools inspect outputs after the fact.
| Common approach | What it does | The gap | Assiduity |
|---|---|---|---|
| Prompt engineering | Defines the task before generation | Cannot correct what happens after the first token | Controls generation against the contract while the output is being produced |
| Post-hoc evaluation | Reviews the final output for quality or compliance | Finds issues after drift has already happened | Records the generation path and produces telemetry as it unfolds |
| Observability / logging | Shows what the model produced | Describes output; does not influence it | Adds a control layer that scores and selects continuations in real time |
| Larger or fine-tuned models | Improve baseline capability | Do not guarantee objective persistence across long horizons | Improves reliability of any model, including smaller ones, without retraining |
Runtime control
during generation.
Assiduity’s core technology, Equilibrium-Constrained DecodingTM, evaluates candidate continuations against a semantic operating contract while the model is generating.
Translate governance artifacts
Policies, procedures, requirements, and project materials become semantic contracts.
Control execution
Generative AI is evaluated during generation against the contract, not only after output is complete.
Return evidence
Assiduity records telemetry, constraint status, and review artifacts that show how the system behaved.
Built for workflows where
drift is costly.
Long outputs. Defined constraints. Consequences when the output drifts.
What AI Execution Changes
Generative AI changes more than workflows.
As generative AI systems move from assistance to execution,
they change authority, accountability, and advantage. This research stream examines how agentic systems reshape:
project governance,
firm boundaries, competitive advantage, and human–machine responsibility.
See what changes for your firm.
Full manuscripts, journal strategy, and supporting materials are available upon request.
What the research shows.
2 corpora consistent results without model-specific tuning
Controlled evaluations show consistent drift reduction across long-form government reports, scientific papers, and large-model settings. Placebo tests indicate the effect depends on the semantic content of the operating contract — not simply on sampling, reranking, or applying an uninformative selection rule.
Latest Thinking
AI That Stays on Brand
Why fashion retail needs runtime control as generative AI moves into brand, product, and customer workflows.
Stop Prompting. Start Governing.
Why agentic AI needs an operating mandate, not a longer instruction.
When Human Oversight Becomes Blame Absorption
When final review turns accountability into exposure.
Frequently asked questions
What is Assiduity?
Assiduity builds runtime control infrastructure for generative AI systems. The goal is to help enterprises keep long outputs, workflows, and agents aligned with operating contracts while producing telemetry that supports review, governance, and audit.
What is runtime control?
Runtime control means applying constraints while the model is generating, not only before generation through prompting or after generation through review. It gives the system a way to monitor and correct behavior during the generation path.
What is Equilibrium-Constrained Decoding?
Equilibrium-Constrained DecodingTM is Assiduity’s patent-pending control approach. At a high level, it evaluates candidate continuations against a semantic operating contract and selects continuations that remain closer to the intended objective.
Does Assiduity replace the model?
No. Assiduity is designed to sit above model providers as a runtime control layer. The current implementation can operate through an OpenAI-compatible proxy without requiring the enterprise to replace the underlying model.
Does it require model retraining?
No. Assiduity applies control at generation time. That allows teams to evaluate runtime behavior without modifying model weights or retraining the base model.
How is this different from prompting?
Prompting states the task before generation begins. Assiduity focuses on what happens after that: whether the output continues to follow the task, policy, and constraints as the model generates.
How is this different from post-hoc evaluation?
Post-hoc evaluation reviews the final output. Assiduity records and influences the generation process itself, producing telemetry about constraint behavior, ε trajectory, branching decisions, completion signals, and stop reasons.
How is this different from fine-tuning or RLHF?
Fine-tuning and RLHF improve a model’s baseline behavior by modifying its weights. Assiduity operates at inference time without touching the model. It can be deployed as a control layer above any model — including models that have already been fine-tuned — and can be evaluated, updated, or removed without retraining.
Can Assiduity help with smaller or lower-cost models?
Assiduity can improve the operating reliability of smaller and lower-cost models by applying control during generation. It does not make every smaller model equivalent to a frontier model, but it can reduce the need to rely on the largest model for every controlled workflow.
What does the protected demo show?
The demo compares baseline generation with ECD-controlled output and displays the telemetry behind the generation path, including constraint satisfaction, ε behavior, branching, and governance signals.
Who should request access?
Enterprise AI teams, design partners, researchers, investors, and governance reviewers evaluating reliable generative AI workflows may request access to the protected demo and additional materials.
Ready to evaluate
runtime control?
Assiduity is available for controlled review with selected enterprise teams, design partners, researchers, and prospective investors.
Evaluated across government reports, scientific papers, and large-model settings.