Model versions, prompts, retrieval context and tool behavior can change output quality over time.
SHELLKODE ENGINEERING · Cloud Transformation & AIOps
Modernize the cloud.
Make operations continuously smarter.
ShellKode AIOps brings evaluations, observability, governance, lifecycle controls, resilience and economics together across production models and agents so AI remains reliable, auditable and cost aware as it changes.
THE PROBLEM WE SOLVE
AI transformation cannot endure without operations built to adapt.
Traditional infrastructure monitoring cannot tell you whether an AI response or agent decision is good.
Token, inference and tool costs can grow without a business level view of value and usage.
Without traces, policy controls and fallback paths, AI incidents become difficult to explain and recover from.
Instrument the system
Capture traces, prompts, model calls, tool actions, latency, cost and workflow state.
Evaluate continuously
Run offline and online quality, safety and regression evaluations against defined thresholds.
Govern change
Control model, prompt, policy and agent releases with approvals, auditability and rollback.
Optimize and operate
Manage SLOs, routing, resilience, incidents and AI economics as the system scales.
WHAT WE BUILD
From migration to intelligent operations.
AI Assisted Migration
We apply AI and automation to accelerate discovery, dependency mapping, migration planning, code analysis, mordenize, validate and cutover. The result is a faster, lower risk migration with clearer decisions and less manual effort
AI observability & evaluations
See whether the AI system is working not just whether the API is up. Quality, traces and production signals: Traces, task success, hallucination, latency, online/offline evals, regression.
Model & agent lifecycle
Change models, prompts and agents without turning production into an experiment. Controlled change from development to production: Versioning, release gates, experiment tracking, rollback, change approval.
AI governance & security
Keep AI behavior inside business, security and regulatory boundaries. Policies, access and auditability: Policy checks, permissions, PII controls, audit trails, human authority.
Resilience & AI SRE
Design for model outages, tool failures, degraded quality and dependency changes. Fallback, failover and incident operations: SLOs, fallback, multi model resilience, incident response, continuity.
AI economics & optimization
Connect inference and token spend to the workload so optimization decisions have a business context. Cost per request, task and outcome: Cost telemetry, routing, caching, prompt/token optimization, capacity planning.
PRODUCTION PROOF
Evidence before adjectives.
20M
ShellKode's IndiaMART case study describes a robust Bedrock translation pipeline designed for high volume processing and zero downtime.
IndiaMART
50K/day
The GeBBS GenAI workflow combines automated processing with human in loop routing and a traceable audit layer.
GeBBS
WHAT YOU WALK AWAY WITH
Deliverables. Not promises.
AI service level objectives and operating thresholds
Trace, quality, latency and cost dashboards
Evaluation and regression suite
Governance, access and change control design
Fallback, routing and resilience configuration
Incident playbooks and AI economics baseline
Questions customers ask
The questions that decide the engagement.
What is AIOps?
AIOps is the production discipline for monitoring, evaluating, governing, changing, recovering and optimizing models and agents after deployment.
How is AIOps different from MLOps?
MLOps focuses heavily on model development, deployment and lifecycle. AIOps extends production control to prompts, retrieval, agent actions, evaluations, governance and inference economics.
How do you monitor AI quality in production?
By combining traces and operational telemetry with task specific evaluations, regression datasets, human feedback and business thresholds not by relying on uptime alone.