DriftdogPrivate AI observability
Product overview
Organization-wide AI observability for compliance, audit, and model assurance.
Driftdog connects model calls, retrieval evidence, source grounding, latency, guardrail outcomes, audit events, and drift findings so operators can prove what changed before trust, compliance, or service quality breaks.
Cisco -> Dialogflow -> RAG -> DB2 -> TritonLive path
timeintentlatencysourcestatus
10:42Benefits eligibility812msDB2 x4Healthy
10:41Claim status1,050msDB2 x3Fallback
10:39Prior auth770msDB2 x5Healthy
Built around the response workflow.
The product surface prioritizes contact-center AI posture, intent and route ownership, recent retrieval changes, and the specific drift events that explain why model, prompt, or source-grounding behavior moved away from baseline.
LLM call volume, latency, fallback rate, and escalation rateRAG grounding, DB2 source freshness, and no-source response posturePHI/PII redaction status, hallucination risk, and deterministic drift feedOn-prem collector, local Postgres storage, and private dashboard deployment
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 dashboardWhat 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.
Every model decision accounted for
Track production AI calls by workflow, system, session, prompt version, model, latency, confidence, and outcome.
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.
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.