Project manager and developers working in front of monitoring displays in a dark control room

AI systems designed for control

LatentDrift

We build AI systems that remain controllable in production.

LatentDrift builds specialized AI systems for work where current research has to prove useful in practice. We focus on AI agents, data and source traceability, and real-time applications across voice, vision and interactive interfaces. We define access, system behavior, checks and responsibilities early so the system remains controllable once it is in use.

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Focus

Control starts in system design.

A capable model does not make a dependable system on its own. What matters is how roles, working memory, data access, state changes, checks and human approvals fit together. We start with the decision, process or interaction that needs to improve and focus on the part where value and risk can be tested. That means deciding what may be shared or retained, which outputs need a clear audit trail and when a person takes over.

Agent workspace showing planning, research and review roles alongside access controls and run status

AI agents with clearly defined roles

Complex workflows often benefit from separate agents for research, planning and review. We define which role owns each task, what information may be stored or shared, and which data and systems each agent may access. We also set cost limits, retry and approval rules, and the events that need to be logged.

Example: An agent workspace coordinates research, planning and conflict checks in one view. Sources, intermediate results and reasons for stopping remain visible. The final decision stays with the person responsible.

Real-time interaction diagram showing speech, interruption, clarification and handoff to a person

Real-time interaction across voice and vision

For live interactions, we bring together streaming, turn-taking, interruption handling, context, visual output, identity and authorization checks, and handoff. The same foundation can support voice interfaces, operational dashboards, review workflows or avatars. A clear state model shows when the system is listening, waiting, asking for clarification or handing control to a person.

Example: A service or training conversation combines speech, gaze, interruptions and pauses. When confidence is low, the system asks for clarification. The same response logic can drive a voice interface, dashboard or avatar, depending on the use case.

Document review view linking a recommendation to its source, data version, rule check and review status

Explainability and traceability

We connect sources, data versions, system logs, tests and explanations so important outputs can be traced. In regulated settings or high-stakes decisions, teams need to see what a recommendation is based on, which rules were checked and where human responsibility begins. We design that traceability into the architecture from the start.

Example: A document review links every recommendation to its source, data version, model version, rule check and review status. Users, domain specialists and technical teams each see the evidence relevant to their role.

Engagement formats

Choose the right starting point.

The three formats address different kinds of uncertainty: whether the use case is worth pursuing, how the system should be designed, or how a critical workflow behaves under realistic conditions. This keeps the scope clear without locking in later model, data or vendor decisions.

AI feasibility assessment

When practical value or technical viability is still unclear, the AI feasibility assessment creates a sound basis for a decision. It separates open assumptions from real constraints and shows whether to proceed, narrow the scope or stop deliberately.

Architecture sprint

When the purpose is clear but the architecture is not, the architecture sprint organizes the key system decisions. Product requirements, technical limits and operational questions are resolved in one framework before the project commits to models or platforms.

Pilot implementation

When an architecture can only be judged in a real workflow, the pilot implementation tests the critical part under the conditions that will matter later. It makes the interaction between data, AI logic, the interface and human decisions open to review.

Operations and ongoing development

The interface is only the visible part.

Whether an AI system works in practice also depends on code quality, tests, recorded decisions and clear responsibilities. The system design defines valid states, data, approvals and failure paths. This gives the client team a sound basis for developing the architecture further and assessing future changes to models, data sources or interfaces.

Project workspace with a documented system handover and team in the background

Contact

Start with a concrete use case.

For an initial assessment, we only need the target process, the types of data available, the people involved and one failure that would matter most. Please do not send confidential or personal data.

Outline your use case