LangSmith Traceability
Complete audit trail for every AI inference
DeepV leverages LangSmith's enterprise-grade observability platform to provide complete transparency, debugging, and compliance for all AI operations.
What is LangSmith?
LangSmith is the industry-leading observability platform for LLM applications, providing DeepV with enterprise-grade tracing and monitoring.
Complete Trace History
Every AI inference is logged with full input/output data, model parameters, and execution metrics.
Persistent Storage
All traces are stored indefinitely for regulatory compliance and historical analysis.
Secure & Compliant
Operates under SOC 2-aligned controls (independent attestation not yet published) with encryption in transit and at rest.
Real-Time Monitoring
Live visibility into AI operations with instant alerts for anomalies or performance issues.
How DeepV Uses LangSmith
Automatic Trace Capture: Every AI inference is automatically logged with zero code overhead
Performance Monitoring: Real-time metrics on latency, token usage, and model performance
Debugging & Optimization: Identify bottlenecks and optimize prompt engineering with detailed analytics
Compliance & Audit: Complete audit trails meet regulatory requirements for AI transparency
Interactive Trace Viewer
Trace records are shown only when the authenticated observability backend is connected
Trace Connection
Live trace data is unavailable until the authenticated observability API is wired into this surface.
Trace Source
Not connected
No production LangSmith trace API binding is configured for this public page.
Trace Viewer
Disabled
This surface does not render synthetic spans, token counts, or model names.
Audit Exposure
Fail closed
Runtime traces must come from the authenticated enterprise observability backend.
Deterministic Outputs
Production review chains are constrained with stable parameters, versioned rules, and replayable audit records
Pinned model parameters for deterministic review chains
Rule-pack versioning on parser and validation output
Persisted trace identifiers on authenticated backend executions
Audit-log replay from real stored execution records
Regulatory Trust
Auditors can verify results independently with confidence in consistency
Reproducibility
Recreate any decision at any time with identical results for dispute resolution
Quality Control
Eliminate randomness and ensure predictable, defensible AI decisions
Complete Transparency Stack
LangSmith + Glassbox = Enterprise AI Governance
LangSmith
- Complete trace capture
- Performance metrics
- Token usage tracking
- Historical analysis
Glassbox
- Explainable decisions
- Human-readable traces
- Validation workflows
- Audit readiness
Together: Unmatched AI Governance
The combination of LangSmith's technical observability and Glassbox's business transparency creates the most comprehensive AI governance solution in the industry.
Experience Enterprise AI Transparency
See how LangSmith and Glassbox work together to deliver unmatched AI governance