AI transformation for SMEs

Transform your business with secure AI agents.

TypeScope helps businesses identify, pilot, and scale valuable AI workflows. Our specialized agents are confined with compile-time sandboxing, securing business data and systems with defined boundaries.

Why specialized agents How we work

The Risk

Your AI agent can overreach

Automating real work requires clear limits: what the agent may touch, what it must never touch, and what happens when a prompt or generated code goes wrong.

Prompt injection

A crafted input convinces the agent to act outside its intended task — deleting records, calling unintended APIs, or following the attacker's instructions instead of yours.

Data exfiltration

Generated code can read data it should not access and send it to an external endpoint — sensitive rows, internal secrets, or another customer's records.

Infrastructure damage

Malicious code generated by a compromised prompt can drop database tables, delete files, or wipe valuable data.

Least Authority

Why Specialized Agents

Unlike general purpose agents with ambient authority and potential AI security issues, we build specialized agents that has predefined roles, so its access and privilege are clearly defined and scoped. This is the principle of least authority.

A general-purpose agent and a specialized agent A general-purpose agent can reach all five systems. A specialized agent cannot reach three systems and has only narrow access to the other two. Hover or focus either panel to explore it. General-purpose agent broad authority databaseREST APIsfilesshellemail All of every system is in reach. Specialized agent least authority filesshellemail databaseREST APIs Most systems are out of reach, and the rest only in part.

Operating principle

Human in the Process

AI handles routine cases. Its actions remain reviewable and, where possible, reversible. Exceptions return to people, and consequential decisions remain subject to human approval and accountability.

✓

Reviewable by default. People can inspect what the agent changed.

↶

Reversible where possible. Actions can be corrected or undone.

!

Human-approved when irreversible. Important actions require a person first.

Human control throughout an AI-assisted workflow An AI agent handles routine work and creates a reviewable record. People can approve, correct, or reverse its actions. Consequential or irreversible actions require human approval before execution. AI agent ROUTINE WORK Reviewable record WHAT CHANGED · WHY Human control REVIEW · DECIDE Approve Correct Reverse Important or irreversible? APPROVAL REQUIRED FIRST

Better Actions

Why Code Generation?

Our agents generate and execute code to automate complex, multi-step tasks. CodeAct reported up to a 20% higher task success rate when agents used executable code instead of text or JSON actions. Anthropic’s Code execution with MCP shows how agents can reduce token consumption, thereby reducing AI bills.

Code outperforms JSON and text action formats for GPT-4, Claude 2, and GPT-3.5 on the M³ToolEval benchmark.
Source: Executable Code Actions Elicit Better LLM Agents , Wang et al., 2024.

The Mechanism

Compile-time sandboxing

TypeScope uses compile-time sandboxing to confine code-generating AI agents, helping protect business data and systems even when an AI response is incorrect or manipulated.

Access authorized
Runtime sandboxing compared with compile-time sandboxing Both panels show an agent surrounded by the same five systems. Runtime sandboxing can restrict operating-system resources such as filesystem paths. Compile-time sandboxing expresses small, named application operations and makes everything else unavailable. Runtime sandboxing databaseall rows REST APIsall endpoints files selectedpath shell email OS-LEVEL · RESOURCE BOUNDARIES Compile-time sandboxing databaseall rows REST APIsall endpoints files shell email tenant Arows onlycreatedraft onlyselectedpath APPLICATION-LEVEL · FINE-GRAINED

A limited first step

A small pilot, a clear decision

Start with a free consultation and prove value through a small pilot before making a bigger commitment.

Consultation

Explore one workflow

Pilot

Build and measure

Scale

Expand only if proven

See the pilot workflow

The Foundation

Open source, all the way down

TypeScope’s approach to building secure AI agents is grounded in security and programming-language research. The foundations are open source, so teams can inspect the model, evaluate its guarantees, and build on it directly.

Open Source

Evaluate the approach directly

Jo's language, compiler, docs, and case studies are open source. Inspect the security model, run it yourself, and build on a transparent foundation.

View on GitHub

Capability Model

Contextual capabilities

The capability model behind Jo is open source too. Read the contextual-capability work that grounds how authority is passed, narrowed, and checked.

View the model

Have a workflow worth improving?

Tell us how the work is done today. We will help identify whether a small, focused AI pilot could make a measurable difference.

Try a pilot