Truwise AI

There's a moment of excitement when you deploy your first autonomous AI agent. Then, there's a moment of sheer panic when you realize it can now take actions, call APIs, and move data—all without asking for permission. That's the problem the industry calls the "Trust Gap," and it's the reason over 80% of modern AI projects fail to scale . This platform exists solely to solve that. It provides a "runtime control tower" that sits between your AI's intent and its execution, evaluating every single action before it can touch your enterprise systems . I've seen teams using it to sleep soundly at night, knowing their "digital workers" are running within a strict policy perimeter.

Key Features

This tool isn't about monitoring what already happened; it's about actively preventing things from happening in the first place . It’s built from the ground up to control agentic behavior, not just flag it after the damage is done.

User Interface

On the surface, you get a clean dashboard that provides visibility across all your AI agents. However, the real power lies under the hood. The platform is designed to be developer-friendly, offering a modular approach where you can deploy just one shield (like cost optimization) and then add layers like compliance or security as you grow . It delivers its core functionality via APIs, SDKs, and "Guardian Agents," making it a seamless control layer rather than a clunky, stand-alone app .

Accuracy & Performance

This is a system built for speed. Runtime policy checks are executed in real-time, evaluating decisions before they execute . We're talking about latency measured in milliseconds (10-300ms) so it doesn't slow down your agent workflows. It uses a proprietary set of 12 small language models (SLMs) to evaluate safety and policy, ensuring high accuracy without the overhead of massive, general-purpose models . This means it’s as fast as it is effective.

Capabilities

The capabilities are deep and comprehensive. The core is the "AI Control Tower," which applies policy before execution, controls agent behavior at runtime, and creates audit-grade evidence for every decision . It also features "AI Shields" that block tool misuse and data leaks automatically . According to a feature comparison, the platform boasts over 30 guardrail modules, has mapped 1,100+ controls to standards like NIST and the EU AI Act, and maintains a library of over 20,000 red-team prompts for testing . It’s a full suite for safety, compliance, and even cost/carbon optimization .

Security & Privacy

Security is the entire reason this platform exists. It protects your organization against "runaway costs," "unsupervised data access," and "wrong actions" performed by AI agents . For enterprises, it provides the operational maturity needed to enforce policies across hybrid infrastructure and multiple clouds without creating new insider risk vectors .

Use Cases

Imagine a customer service AI agent in a bank receiving a request that contains hidden instructions to expose sensitive data. Traditional tools would log this after the breach happened. This platform would intercept the instruction, evaluate it against policy, and block the action before any data leaves the system . This makes it essential for any high-stakes environment like healthcare, finance, and legal, where risk management isn't optional . It's used to ensure AI systems don't deviate, blow budgets, or break compliance rules.

Pros and Cons

Pros:
- Provides real-time control, not just post-incident visibility .
- Reduces AI operational costs by up to 83% and carbon footprint by up to 64% .
- Ensures 90-100% runtime alignment with enterprise policies .
- The modular design means you can start with one shield and scale up .

Cons:
- It's designed for large, regulated enterprises; it may be overkill for small startups piloting single-agent use cases.
- Deployment requires a certain level of operational maturity to fully leverage its infrastructure .

Pricing Plans

The pricing model is consumption-based and designed to scale. They offer a "Quickstart" plan that is free and includes 10,000 API calls per month, ideal for small teams to trial the system . As you scale, they have "Explorer," "Pro," and "Enterprise" tiers that offer more API calls, higher GPU access for evaluations, and priority support . The enterprise plan provides unlimited calls and custom GPU allocation for mission-critical systems .

How to Use Trustwise

You can get started easily with the free tier. After signing up, you typically integrate the platform via its API or SDK into your existing AI stack . This allows you to apply one of their ready-made "Blueprints" (industry-specific templates for banking, healthcare, or industrial uses) or build custom policies. The system then acts as a proxy, intercepting requests to your AI models and enforcing your policies before any action is taken .

Comparison with Similar Tools

Unlike traditional security tools that guard the perimeter, this platform protects the core decision layer. While some competitors focus on testing and quality monitoring (like Advai or TrustLab), this product differentiates itself with its "live mitigation" capabilities . It doesn't just tell you there was a problem; it actively prevents it from happening in real-time . It’s also seen as more comprehensive than some developer-focused tools (like WitnessAI), providing enterprise-level oversight for the entire AI fleet, not just one department .

Conclusion

In the rush to adopt AI, many companies are inadvertently introducing massive new risk vectors. Tools that simply observe are no longer enough. For organizations that want to deploy autonomous agents safely, this platform offers the control, compliance, and visibility needed to govern the entire lifecycle. It’s the insurance policy that lets you innovate without the sleepless nights.

Frequently Asked Questions (FAQ)

What does "runtime control" mean?
It means the system intercepts every action an AI wants to take and checks it against your policy—authorizing, blocking, or escalating it—before the action is actually executed.

Is it cloud-agnostic?
Yes, it is model-agnostic, cloud-agnostic, and works across frameworks like AWS, Azure, GCP, and agents built with LangGraph or AutoGen.

What is the Trust Gap?
It refers to the critical barrier where AI projects fail to scale due to a lack of control, reliability, and safety .

Who uses this?
It is used by large enterprises in regulated industries like financial services (e.g., NatWest, Lloyds), healthcare, and professional services (e.g., KPMG) .