Strategy
Thesis. When objectives are unambiguous, action is simple. We build decision infrastructure that favors clarity and disciplined execution while remaining comfortable saying “not now.”
Enterprise delivery.
- Proprietary APIs and SDKs for secure integration into existing systems and workflows.
- Purpose-built adapters and schemas that fit your environment; data remains under your control.
- Authentication, authorization, scoped access, and auditability by default.
- Operational readiness: SLAs/SLOs, observability, and change controls suitable for the enterprise.
Edge pillars.
- Context awareness: Classifies operating states in real time (load, latency, dispersion, data quality) to decide readiness and action.
- Signal coherence: We act only when multiple independent features align (telemetry breadth, structural patterns, anomaly scores, change velocity), not on a single indicator.
- Orchestrated execution: Pre-defined workflows, guardrails, and budget policies convert intent into consistent outcomes.
Research engine.
- “Aletheia” (internal codename): our ML stack for feature engineering, regime labeling, and decisioning.
- Features include telemetry breadth, structural relationships, behavioral signals, and cross-system context.
- Walk-forward, cross-context validation; no in-sample heroics. Every model change is versioned with a changelog.
Reliability & controls.
- Guardrails and budget policies with per-request and aggregate limits aligned to SLOs.
- Scenario exercises and chaos drills; automatic safing when health thresholds are breached.
- Operational controls: pre-flight checklists, emergency stop, and audit-ready logs.
Transparency.
- We publish components of our analytics in Intelligence (models, datasets, notebooks, API/GitHub) and document what’s live vs. in research.
- Service-level metrics are shown with methodology notes (latency, availability, error budgets).