Capabilities

Where we apply systems work

Three capability lines. Each maps to a system class with a control model - not a service menu of vague AI offerings.

Agent systems

Autonomous agents for real work

Tool-using systems that research, decide and execute multi-step work with explicit permissions and human approval at critical actions.

Problem: Work that needs continuity and tools but cannot be left fully unsupervised.
System: Runtime, tool adapters, policy, memory, halt and approval paths.
Outputs: Task trails, decisions, logs, escalations.
Control: Scoped tools; approval gates; global halt.
Status: Lab systems live; portable patterns for client deployments.

Intelligence products

Interfaces around intelligence

Interfaces and internal products that turn fragmented information into traceable decisions, workflows and measurable outcomes.

Problem: Model output without ownership, review or audit is not an operating system.
System: Product surfaces binding models, retrieval, state and operator review.
Outputs: Decision objects, queues, audit trails, action metrics.
Control: Roles, immutable events, override on publish and spend.
Status: Build and operate for internal and client product surfaces.

Adaptive growth

Growth as a closed loop

Infrastructure that connects customer signals, experimentation and execution into a continuously improving operating loop.

Problem: Manual growth, broken attribution, platform refusal.
System: Signal capture, experiment routing, execution adapters, feedback.
Outputs: Attributed funnels, experiment ledgers, operator dashboards.
Control: Spend/publish gates; tracking integrity; automation kill switches.
Status: Live product surface via Feral Signal stack.

Deployment standard

Every capability engagement inherits the Infravane standard: scoped autonomy, observable decisions, human approval gates, least-privilege tools, continuous evaluation, measurable outcomes. See the homepage standard section or company page for how engagements start.