AI, agents, and governed data

Build useful AI systems with clear data boundaries, operational controls, and measurable quality.

Assistance helps teams move AI from experiments into production systems that can be inspected, measured, secured, and supported. Use these guides when you are planning RAG, internal assistants, AI agents, managed MCP servers, LLMOps, or AI-ready data paths.

Assistance AI service areas#

Area	Use it for	Related service
AI as a Service	Scoping, delivery, and ongoing operation of practical AI capabilities.	AI as a Service
Generative AI engineering	RAG, assistants, model integration, prompt/version control, and evaluation.	Generative AI Engineering
AI agent infrastructure	Runtime, identity, tool execution, memory, gateway, and environment foundations for agents.	AI Agent Infrastructure
Agent orchestration	Durable workflows, retries, human approvals, handoffs, and state management.	Agent Orchestration
Agent observability	Traces, evals, cost attribution, model/tool telemetry, and production support workflows.	Agent Observability
Managed MCP servers	Operated Model Context Protocol servers with scoped tools and connector boundaries.	Managed MCP Servers
Data platform readiness	Pipelines, governance, quality checks, search indexes, and permissions for AI use cases.	Data as a Service

What Assistance operates#

Within an agreed engagement boundary, Assistance can operate AI platform components such as retrieval pipelines, model gateways, orchestration workers, tool servers, eval runners, dashboards, alerts, secrets handling, and support runbooks.

What the customer owns#

The customer owns business decisions, source-system truth, data classification, legal/privacy approvals, user access approvals, product release timing, and customer-facing communications unless a broader service agreement says otherwise.

Safe-use requirements#

Classify sources before indexing, embedding, prompting, or logging them.
Preserve tenant and role permissions in retrieval and tool execution.
Version prompts, models, indexes, tools, and evaluation datasets.
Require human approval for destructive or high-impact actions until the workflow is proven.
Capture traces for prompts, retrieval, tool calls, approvals, errors, latency, and cost.
Define incident and escalation paths for model regressions, provider outages, unsafe outputs, and data leaks.

User documentation#

Production AI governance

LLMOps evaluation playbook

RAG with permissions

Embedding pipelines

Managed MCP operations

Search and vector operations

Onboarding inputs#

Prepare these inputs for an AI scoping session:

target workflow, users, and decisions the system may influence
source systems, owners, data classifications, and retention rules
identity model, approval paths, and tenant boundaries
providers or models already approved by security/legal teams
quality expectations, representative examples, and known failure modes
cost, latency, availability, and support expectations

Support and change requests#

AI support requests should include the workflow name, environment, time window, trace or run ID, user/tenant context where safe to share, expected behavior, actual behavior, and business impact. Planned changes to models, prompts, indexes, tools, or data sources should include rollback criteria and evaluation evidence.

Getting started#

Start with an AI scoping session. Assistance will map the workflow, data boundaries, implementation slice, observability needs, and operating responsibilities.

Scope an AI implementation →