Contexts
Typical cases
- AI/ML framing (use cases, risks, data readiness)
- Deploying models/LLMs with monitoring, security, and cost control
- Documentation and handover: playbooks, runbooks, MLOps
Expertise
AI / ML
Useful models with traceability and guardrails.
Overview
Framing AI/ML use cases, deploying models and LLMs with monitoring, security, cost control, and guardrails for sensitive contexts.
MLOpsLLM + guardrailsModel observabilitySecurity / IAM
Deliverables
What you get
- Framed AI use cases (value, risks, data)
- Models/LLMs deployed with observability and security
- Usage guides and runbooks for operations and cost control
Tooling
Stack and tools involved
MLOpsLLM + guardrailsModel observabilitySecurity / IAMCost controlPlaybooks / runbooks
Go further
Method
SharedSight approach: short diagnosis, written decisions, and guided implementation
Offerings
Offerings: engagement models, expected outcomes, and implementation
Related expertise
Data
Pragmatic data architecture, quality and governance, ingestion pipelines, and data observability.
Related expertise
Software development
Robust software development: pipelines, quality, security. Teams coached on practices (tests, reviews, CI/CD) with product-oriented implementation.
Related expertise
Software & application architecture
Pragmatic target architecture aligned with security, scalability, and implementation constraints. Tracked decisions (ADR), incremental trajectories, guardrails to avoid drift.
Related expertise
Observability
Coherent instrumentation, alert budgets focused on users, operational dashboards. Less noise, more action.
Value in Vision