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N° 01 AI & Automation → AI Workflow Automation

Automation a controller will sign off on

Queues, triggers, human-in-the-loop review gates, retry and escalation logic. Not a chatbot bolted to the website. The repetitive operational work inside Odoo and across adjacent tools: data entry, routing, and reconciliation that currently needs a person on every pass.
The unglamorous half that keeps production honest.
N° 02 The shape of the work

What does workflow automation actually involve?

Encoding a repeated operational path so the system moves the work, a person reviews only the exceptions, and every step is visible enough for someone accountable to sign off. At Entrivis that means triggers, queues, retry, and escalation built into the ERP or the tools around it: not a black-box agent nobody can audit.

The pattern we see is consistent. A team buys a clever automation, it works in a controlled test, and it goes live without a review gate or an owner for failures. Six weeks later people have quietly gone back to the spreadsheet, because nobody could see what the automation did when it was wrong.

We start from the path: which handoff, how often, who is accountable today, and what “wrong” looks like in the ledger. Sometimes that path needs a model. Often it needs a queue and a well-placed exception report.

N° 03 Coverage

Paths we typically automate

01

Intake and routing

Documents, tickets, and requests land in the right queue with the fields the next person already needs.

02

Approval and exception gates

Human-in-the-loop steps where the automation proposes and a named role confirms before anything posts.

03

Reconciliation and matching

Bank lines, invoices, and operational records compared on a schedule, with mismatches escalated instead of silently skipped.

04

Retry and escalation

When a step fails, the system retries with a rule and then wakes a person: not an infinite loop into a forgotten log.

N° 04 Proof

What “signed off” looks like

Pattern

Visible steps, not a black box

Automation that a finance controller is willing to own because they can see every step it took: who triggered it, what changed, what was held for review, and what escalated.

Delivered

Applied automation for an ecosystem partner

We have built automation into a healthcare platform for an ecosystem partner: applying judgement to operational data inside a working system, rather than running a standalone AI product. (Client not named.)

N° 05 The hard part

If you cannot see the steps, it is not production

Unglamorous is the point. The automation that survives month-end is the one a named person can reconstruct after a bad run. We design the review gate and the escalation path before we wire the trigger.

If the hard part is a custom model or document product surface, that work lives under AI custom solutions. If the gap is logging, drift monitoring, and fallback when the model is down, start at AI governance and observability. Cross-system connectors without the AI layer sit under Odoo integration.

N° 06 Engagement shape

How workflow automation runs with us

We scope the path, not a platform subscription.

  1. 01

    Path discovery

    Map the handoffs, volumes, and cost of a bad step. Kill paths that should stay manual.

  2. 02

    Gate and escalation design

    Who reviews, what auto-posts, what retries, and who wakes when the queue stalls. Written down before build.

  3. 03

    Build into the system of record

    Triggers and queues live where the work already lives. No parallel shadow process.

  4. 04

    Production tuning, then sustained ownership

    The first weeks after go-live are where trust is earned. Across the firm, 35% of delivery effort sits post-go-live. This work is why.

N° 07 FAQ

Common questions about workflow automation

Is this just Odoo Studio / automated actions?

Sometimes the standard product is enough, and we will say so. This page is for paths that need engineered queues, review gates, and escalation across modules or adjacent tools: not a click-path demo.

Do you always use an LLM?

No. Many “AI automation” scopes are rules, schedules, and exception reports. We add a model only when the judgement cannot be encoded more simply.

Who owns the automation after go-live?

You do. We document the path, the gates, and the owners. Managed Continuity is available when you want us to stay on the queue with you (engagement models).

What if automation already failed once?

Start at Rescue & Stabilization. We find what is still running unwatched, then decide what to keep, replace, or turn off.

How does this relate to custom AI solutions?

Custom solutions own the model and product surface. This page owns the operational path around it. Most durable engagements need both; we fence them so buyers can enter at the right door.

Show us the path that still needs a person every time

Tell us the handoff your team repeats and dreads. We will tell you whether it should be a queue, a gate, or left alone.

Talk to us