AI models & the harness layer

Production agents with memory, planning, and governance—not single-turn chat—on the models that fit your workflow.

Platform

The AI harness layer

Infrastructure that turns foundation models into reliable agents for multi-step business work.

Memory systems
Short-term context for active tasks and long-term patterns so agents stay coherent across sessions.
Planning workflows
Decompose complex requests into steps and adapt when the situation changes.
Sub-agent delegation
A master agent routes work to specialists—data quality, SQL, visualization—in parallel.
Error recovery
Detect failures, retry with adjusted logic, and learn from recurring issues.
Continuous learning
Post-run reflection and knowledge updates so outputs improve over time.
Configurable roles
Define analyst personas, communication style, and risk tolerance per use case.
Org modeling
Map departments, access boundaries, and cross-team coordination into agent structure.
Agent handoffs
Sequential, parallel, and iterative workflows across multiple agents.

Models

We select the best model for each workflow—balancing cost, latency, accuracy, and your compliance requirements. No single-vendor lock-in.

Security

Configurable policies from standard API retention to zero data retention for sensitive workloads.

Layer 1
Training data protection
Enterprise API agreements with major providers ensure your data is not used to train their models.
Layer 2
Controlled API retention
Standard ~30-day retention with context caching for efficiency, or stricter policies on request.
Layer 3
Zero data retention (ZDR)
No retention after processing—suited for HIPAA, financial, and regulated workloads.

Discuss architecture, model selection, security, and deployment options.