AIWatch - bandhan

See Every Model. Catch Every Drift. Govern Every Decision.

AIWatch is Bandhan’s enterprise AI observability and governance platform, monitoring model performance, hallucination, drift, cost, and policy compliance across classical ML and generative AI estates, so every AI decision your customers experience is safe, accountable, and on-policy.

Built for Chief AI Officers, Heads of ML and AI Engineering, AI Governance and Risk Leaders, and Enterprise Architects responsible for AI safety, performance, and compliance.

Most Organizations Have Built Almost No Observability Around the Models Running in Production

Enterprise AI is moving from pilot to production at speed, and most organizations have built almost no observability around the models in flight. Classical ML models drift silently when input data shifts. Generative AI applications hallucinate, leak data, and behave unpredictably under edge-case prompts. AI costs balloon when token usage is invisible. And regulators are increasingly demanding evidence Read More...

What Is AIWatch?

AIWatch (also delivered as AiMonitor for embedded deployments) is Bandhan Technologies' enterprise AI observability and governance platform. It monitors classical ML models, large language model applications, agentic AI systems, and AI-augmented workflows, surfacing performance issues, drift, hallucination, cost anomalies, and policy violations in real time. Read More...

How AIWatch Works

AIWatch is built on a model-aware observability and governance architecture purpose-engineered for AI’s distinct monitoring needs.

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Instrumentation

Lightweight SDKs and proxies capture prompts, completions, model inputs, outputs, costs, and performance signals.

Behavioral Analysis

Real-time analysis for hallucination, drift, toxicity, PII leakage, and policy compliance.

Model Registry and Governance

A centralized inventory of every model in production with version, owner, risk classification, and a full audit trail.

Alerting and Action

Configurable thresholds trigger alerts, fail-overs, or human-review workflows.

Reporting

Role-based dashboards for AI engineers, risk officers, and compliance auditors.

Core Capabilities

Model and Output Monitoring

Real-time monitoring of model performance, drift, hallucination, toxicity, and policy compliance across classical ML and generative AI.

AI Cost and Token Observability

Granular visibility into model invocation costs, token usage, and cost-per-business-outcome, with anomaly detection and forecasting built in.

Model Registry and Lineage

Centralized registry of every production model covering version, training data lineage, owner, risk classification, and review history.

Audit and Compliance Trails

Tamper-evident logs and policy enforcement support regulatory and internal audit requirements.

Policy Enforcement

Configurable guardrails enforce on-policy output, data-handling rules, and risk thresholds before content reaches users.

Multi-Cloud, Multi-Model Coverage

Vendor-neutral monitoring across Azure, AWS, GCP, OpenAI, Anthropic, open-source, and self-hosted models.

Visibility. Control. Confidence

Faster detection of model issues

Drift, hallucination, and policy violations surface in real time, not after customer complaints.

Reduced AI risk exposure

Unsafe or off-policy outputs are caught before they reach users.

Lower AI operating cost

Token and invocation visibility identifies waste and optimization opportunities.

Audit-ready AI governance

Tamper-evident logs and policy enforcement support regulatory and internal audit requirements.

Faster AI engineering iteration

Engineers see actual model behavior in production, accelerating tuning and improvement cycles.

Confidence to scale AI

Robust observability is the precondition for moving AI from pilot to mission-critical deployment, and for earning the trust of customers whose decisions, experiences, and data AI is acting on.

Where AIWatch Is Applied

Generative AI Application Monitoring

Hallucination, toxicity, PII leakage, and policy monitoring across customer-facing generative AI applications.

Classical ML Model Operations

Drift, accuracy, and fairness monitoring across production ML models in BFSI, healthcare, and operations.

AI Cost Optimization

Token and invocation-level cost analytics with anomaly detection and budget forecasting.

Regulated AI Governance

Audit-ready monitoring and reporting for BFSI, healthcare, insurance, and government AI deployments.

Agentic AI Oversight

End-to-end observability across multi-agent systems covering tool use, decision paths, and outcome quality.

What Sets AIWatch Apart

Built for classical ML and generative AI

One platform across model types, not separate tools for ML drift and generative AI hallucination.

Governance as a first-class concern

Model registry, audit trails, and policy enforcement are core capabilities, not afterthoughts.

Vendor-neutral

Works across Azure, AWS, GCP, and all major model providers with no model-vendor lock-in.

Cost observability built in

Token and invocation cost visibility at the same granularity as performance monitoring.

Regulated-industry ready

BFSI, healthcare, and government compliance frameworks supported from day one.

Integrates with your existing stack

Native integration with observability, SIEM, and ITSM tooling, not a parallel silo.

Proven in Practice

AIWatch is engineered by Bandhan Technologies' AI Engineering and platform practice, combining MLOps, generative AI, and enterprise observability expertise across BFSI, healthcare, retail, and media. Read More...

Ready to Make Every Model in Production Observable, Governed, and Accountable?

Book an AIWatch demo with a Bandhan AI observability specialist. We will review your current AI estate, the models in production, and your governance and cost priorities, and outline a proof-of-value engagement that delivers measurable observability outcomes in weeks.