Every model decision accounted for
Track production AI calls by workflow, system, session, prompt version, model, latency, confidence, and outcome.
DriftdogPrivate AI observability
Platform
A private telemetry and control core for enterprise AI operations.
Driftdog starts with local collectors, durable storage, AI event ingestion, service-aware querying, incident timelines, baseline comparison, and deterministic drift events.
What Drift Dog monitors
The signals that decide whether AI can be trusted in production.
Drift Dog AI gives contact-center, platform, and compliance teams one operating record for what the LLM did, why it answered that way, and whether the behavior stayed inside approved bounds.
Know what proof the model used
Monitor retrieval status, source hit counts, source freshness, DB2 timing, no-source responses, and source-of-truth coverage.
Separate proof from assertion
Compare model outputs with retrieved evidence so operators can see grounded, weakly grounded, and unsupported answers.
Watch the full AI execution path
Measure orchestration, retrieval, database, and model latency so teams can find the exact step degrading response quality or speed.
Spot containment failure early
Surface fallback spikes, escalation patterns, blocked responses, and low-confidence outcomes before they become business risk.
Flag unsafe answer patterns
Track hallucination-risk signals, groundedness gaps, and policy exceptions beside the full interaction record.
Keep sensitive evidence controlled
Store redacted metadata by default and preserve data residency, retention, and audit controls inside the environment.
Catch behavior movement early
Compare model, prompt, retrieval, latency, and answer-quality behavior against approved evaluation baselines.
Deployment
On-prem, private cloud, or hybrid.
Drift Dog AI can be deployed as a private stack inside the same corporate network, VPC, or cloud boundary as the LLM. No PHI leaves your environment by default.
Runs inside the corporate environment
Deploy the collector, API, dashboard, and Postgres storage where the contact-center AI workflow already runs.
Keep telemetry inside controlled infrastructure
Run in your VPC, Kubernetes cluster, VM, or container host while preserving the same local evidence model.
Match the enterprise control boundary
Keep sensitive telemetry private by default and connect only the surfaces your security policy permits.
Healthcare contact center use case
Cisco Contact Center -> Dialogflow -> RAG -> DB2 -> On-prem LLM -> Drift Dog dashboard.
Built for healthcare payer operations where contact-center AI has to answer from trusted systems, stay grounded, protect PHI, and leave an audit-ready operating record.
Cisco Contact Center->Dialogflow->RAG->DB2->On-prem LLM->Drift Dog dashboard
Cisco Contact Center session, channel, queue, and route context
Dialogflow intent detection, fulfillment, and webhook timing
RAG retrieval metadata, source hit count, no-source responses, and DB2 latency
On-prem LLM or Triton/DGX model latency, fallback rate, drift score, and groundedness
See the contact center dashboardDrift detection
Catch AI behavior changes while they are still explainable.
The first Drift Dog engine uses deterministic baseline comparison and change correlation. The operational goal is zero unapproved drift in production AI behavior, with evidence when anything moves.
01
Compare current model, prompt, retrieval, latency, and answer-quality behavior against approved baselines.
02
Flag unapproved drift before it becomes a member-impacting contact-center incident.
03
Correlate behavior changes with Dialogflow fulfillment, RAG source changes, DB2 freshness, and DGX model versions.
04
Link every finding back to metrics, logs, guardrail outcomes, and incident evidence.
Collector-ready
Attach to the AI path without moving sensitive data out.
Start with metadata-only AI events and expand into deeper telemetry as the environment allows. The collector can ingest from webhooks, log streams, API wrappers, or the inference service itself.
OpenTelemetry traces, REST ingestion, Prometheus scrape, Triton metrics, Dialogflow payloads, and RAG metadata
Interaction, session, channel, intent, model, prompt hash, retrieval count, DB2 latency, LLM latency, and total latency
Groundedness, confidence, drift risk, fallback, escalation, error type, and timestamp on one event model
Collector can sit beside Dialogflow webhooks, the RAG service, an API gateway, or the DGX inference edge
Executive evaluation
Review Driftdog against your enterprise AI control requirements.
Walk through deployment posture, baseline evaluation logic, audit evidence, drift detection, hallucination-risk controls, and the operating record required for regulated AI systems.