Rettare

AI workflow automation consulting

AI workflow automation consulting, done-for-you implementation.

We implement agentic workflows and AI automations inside your business - integrated with your systems, governed properly, and measured for ROI. Not a course. Not a tool list.

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Send one messy workflow. We will tell you what should be automated first, what should stay manual, and what it takes to ship safely.

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Implementation scope

What Rettare helps you implement.

The highest-value work is usually a process with real volume, repetitive decisions, clear failure points, and a measurable payoff once cycle time drops.

Prioritise the use cases that pay back.

ROI + risk first

We rank workflows by payoff, effort, and risk so your first implementation is worth doing and feasible in your current environment.

Typical outputs

  • Workflow map from current state to target state.
  • Ranked use cases with trade-offs called out.
  • Implementation path matched to your systems and data reality.

Build and integrate the workflow.

Inside your stack

We implement the workflow end-to-end across the tools, documents, APIs, and decision points the team already uses.

Common work

  • Triage, drafting, routing, classification, and approvals.
  • Integrations across CRM, ticketing, docs, and internal apps.
  • Testing, documentation, and handover.

Add governance and control points.

Privacy and oversight

The build includes approvals, access boundaries, audit trails, and fallback paths so AI use does not turn into a governance problem.

Controls included

  • Privacy-by-design for sensitive data handling.
  • Approval gates for higher-impact actions.
  • Run logging and auditability.

Operationalise what gets shipped.

Runbooks + monitoring

A live workflow still needs ownership, monitoring, and exception handling. We set that up before handover.

Operational outputs

  • Runbooks, monitoring, and rollback paths.
  • Named ownership and exception handling.
  • Clear acceptance criteria before go-live.

Support adoption so the work sticks.

Rules + rollout

Teams do not default to public tools when the governed workflow is easier, clearer, and backed by rules they understand.

Adoption support

  • Training on what is approved and what is not.
  • Operating model updates for the team using the workflow.
  • Rollout support so usage survives beyond launch week.

If you already know the workflow, we can move straight to implementation. If you need alignment first, we will recommend the smallest engagement that can deliver a real outcome.

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Agent Ops

Agent Ops is the operating standard behind the implementation.

It is how Rettare handles approvals, logging, fallbacks, auditability, and ownership when AI is inside a real workflow. It is not a separate package ladder.

Want the operating model in detail? The Agent Ops page explains how Rettare handles governance, run logging, QA and evals, approvals, and controlled rollout when AI is involved.

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Ways to engage

Start with the smallest engagement that gets a real workflow live.

Discovery Sprint

Confirm the highest-value workflow, the systems involved, and the safest build path.

Deliverables

  • Workflow map.
  • Prioritised opportunities.
  • Recommended architecture.
  • Acceptance criteria.
  • Rollout notes.

Workflow Build

Implement one workflow end-to-end with integrations, controls, and handover.

Deliverables

  • One production workflow.
  • Integrations.
  • Testing.
  • Monitoring.
  • Documentation.

Rollout Program

Implement multiple workflows with shared controls, ownership, and team rollout support.

Deliverables

  • Shared components.
  • Multiple workflows.
  • Operational visibility.
  • Exception handling.
  • Adoption support.

Managed Ops

Keep the automations stable as tools, APIs, and business rules change.

Deliverables

  • Monitoring.
  • Incident response.
  • Improvements.
  • Vendor and API change handling.
  • Monthly reporting.
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Governance built in

Controls are part of the build, not a cleanup job after launch.

Teams are already using AI. The safer move is to replace ad hoc public-tool usage with governed workflows, clear rules, and operational visibility.

  • Human-in-the-loop approvals for high-impact actions.
  • Audit trails (inputs, outputs, decisions, timestamps).
  • Access controls and least-privilege data handling.
  • Fallback paths when tools are unavailable.
  • Monitoring and alerts for failures and exception volume.
  • Testing with real edge cases before cutover.
  • Adoption rules for what teams can and cannot use.

Avoid public AI tool drift.

If teams are already using public AI tools, the answer is not another memo. It is a governed workflow, clear rules, and an implementation people will actually use.