SERVICE / AI automation

Enterprise AI Workflow Automation with production controls.

Move beyond demos by embedding AI into a defined workflow with approved context, evaluation, observability, cost controls and human decision boundaries.

Discuss a project →All capabilities →

Make the workflow safe enough to operate.

Workflow-first design

Define the trigger, context, expected action, failure consequence and human owner before choosing a model.

Grounded enterprise context

Use retrieval and integrations with explicit identity, authorization, freshness and data-boundary controls.

Evaluation & guardrails

Test against representative cases, edge conditions, disallowed actions and measurable task outcomes before release.

Observability & fallback

Track latency, model/tool failures, cost and quality signals with controlled fallback or escalation paths.

An AI automation engagement can include.

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Use-case and workflow discovery

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RAG and knowledge integration

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LLM/tool orchestration

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Evaluation datasets and quality checks

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Human-in-the-loop controls

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Production monitoring and handover

LLMsRAGPythonAPIsWorkflow automationEvaluationGuardrails

Automation design is shaped by data sensitivity, decision risk, human-review requirements, integration boundaries, evaluation quality and the cost of incorrect outcomes.

Start a conversation

Have an AI use case that needs to survive production?

Share the workflow, users, source information and consequence of a wrong action. We will help define the smallest useful production design.

Discuss a project →hello@logicbits.tech