Organization-wide AI observability
Instrument AI workflows across customer operations, internal copilots, service teams, and regulated business processes with one evidence model.
DriftdogPrivate AI observability
Enterprise AI observability, compliance evidence, and control
Enterprise Observability forEverything AI
Deploy Driftdog across your organization to monitor every AI workflow, every model, and every critical use case against approved baselines for grounding, guardrails, hallucination risk, drift, and audit evidence.
Plug in any model. Hold every AI system to your baseline evaluations, policy controls, and audit expectations.
metadata-onlyPHI postureguardrails activeControl statebaseline checkedEval posture
Model-agnosticModel coverage
Baseline-enforcedOperating standard
Audit-readyEvidence trail
Executive evaluation
Enterprise AI assurance for every model your organization runs.
Driftdog is positioned as the observability and governance layer for production AI: compliance evidence, audit-readiness, model-agnostic baseline enforcement, drift detection, and hallucination-risk containment.
Compliance and audit evidence
Preserve the telemetry, policy outcomes, and change history needed for compliance review, internal audit, incident response, and executive oversight.
Model-agnostic baseline assurance
Plug in any model and measure it against approved baselines for grounding, latency, guardrail behavior, and hallucination containment.
Technical walkthrough
See the enterprise AI control surface in context.
A short tour of the command-center view for baseline conformance, drift posture, groundedness, hallucination risk, incidents, and audit evidence, using contact center operations as one representative enterprise workflow.
Product proof
A command center for enterprise AI assurance.
The operator view shows how many model calls have run, whether outputs were grounded in evidence, whether guardrails held, and whether prompt, model, or retrieval behavior drifted away from baseline.
Health Plan Services AI Contact Center ObservabilityOn-prem mode
Cisco -> Dialogflow -> RAG -> DB2 -> TritonLive path
timeintentlatencysourcestatus
10:42Benefits eligibility812msDB2 x4Healthy
10:41Claim status1,050msDB2 x3Fallback
10:39Prior auth770msDB2 x5Healthy
Baseline conformanceEnforced
Audit evidenceRetained
Drift statusWatch
Why it matters
The governance layer between experimentation and enterprise deployment.
Enterprise AI moves faster than governance programs do. The operating layer has to prove that every model and workflow stays inside approved baselines, controls, and audit expectations.
Every model needs the same operating standard
Whether the organization uses OpenAI, Anthropic, Gemini, open-weight models, or internal systems, the operating question is the same: is behavior staying inside the approved baseline?
Regulated enterprises need evidence, not screenshots
CIO, CTO, CISO, compliance, and internal-audit teams need operational evidence that accuracy, privacy, latency, and controls are working in production.
Production AI needs a control narrative
Leadership needs a live record of what the model saw, what it retrieved, how it answered, when it drifted, and whether guardrails contained hallucination and policy risk.
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.
Drift 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.
Private deployment
Installed in your environment for controlled AI evidence.
For healthcare, financial, and enterprise AI systems, the monitoring layer should live where the sensitive workflow already runs. Drift Dog AI is designed for that private deployment pattern.
Runs inside your on-prem, private-cloud, or hybrid environment
No PHI leaves your environment by default
Stores redacted prompt and response metadata unless policy allows more
Designed for API-key boundaries, local-only operation, retention policy, and audit review
Preserves evidence for compliance review, investigation, and model-governance workflows
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.