A map of the repetitive work
Two or three weeks of watching how work actually moves. Tasks logged, volumes counted, error rates noted, then a shortlist ordered by hours saved and risk. Some items stay manual.
Fractional Teammates · AI & Automation
A senior automation specialist who watches before building
Your teammate spends the first weeks counting how often each task is repeated, then automates what is worth automating: triage, routing, document handling, agent workflows. A person still approves anything that carries risk.
A senior specialist, not a junior placement You own the work and the accounts Reply within one business day
Agent running - queue empty
Flow 04
01
Trigger
Webhook
02
Enrich
Normalise
03
Decide
Policy
04
Act
Resolve
Run log
> Matched 42 records
> Routed 7 exceptions to a human
> Closed 35 tickets automatically
35
Auto-resolved today
6h
Engineer time saved daily
What a ai automation specialist is
OlDevs places a fractional AI automation specialist inside your organisation for a defined part of each week.The work starts with observation: the specialist maps the tasks people repeat — inbox triage, quoting, intake forms, invoice and document handling, status chasing — then measures which are worth automating. They build the workflows, using rules, integrations and AI agents where an agent genuinely helps, and they design the human checkpoints: what a person must approve, what gets logged, what happens when a model is unsure. They work in your own tools and rituals, review output weekly, and hand over documented automations you own outright.
Key facts
- 01Engagement
- Ongoing fractional, quote-based
- 02Coverage
- Canada and the US, remote from Vancouver
- 03Languages
- English and French
- 04Cadence
- Weekly demo, notes in your tracker
- 05Typical commitment
- 1–2 days a week
- 06Judged on
- Hours returned per week · Exception rate · Cases resolved without a human
AI & Automation
Where the repetitive work goes
What this teammate takes off your plate
Agent workflows for defined jobs
Agents that draft replies, summarise threads, prepare quotes or update records — scoped to one job each, wired into the systems you already run, and stopped at the point a person should decide.
Document handling and data extraction
Invoices, applications, purchase orders, permits and contracts read, classified and turned into structured fields in your systems. Low-confidence pages are queued for a person instead of guessed at.
Triage and routing for inbound work
Email, forms, tickets and enquiries sorted, tagged, prioritised and sent to the right person with context attached. Rules where rules are enough; a model only where language is genuinely messy.
Thresholds, exception queues and logs
Test cases built from your own past work, confidence thresholds, an approval queue for anything below them, and a log of every run, failure and reversal your team can read unaided.
The people who run the exception queue
Every automation written up in plain language: what it touches, who reviews what, how to switch it off. Your staff are trained on the review steps, because an automation nobody supervises drifts.
Benefits
What changes when automation stops being a side project
01
Where AI may act gets decided
Which steps a workflow may complete alone, and which wait for a person, gets settled by someone who has watched automations both overstep and stall, rather than learned on your live payroll or invoice runs. That judgement is rarely needed full-time.
02
Staff stop shadow-checking every output
Once a workflow has shown its working on real cases, people stop keeping a private spreadsheet in parallel. Trust is given task by task on evidence, so the automation becomes part of the day rather than a second system to check.
03
Intake volume stops setting headcount
A heavy month becomes a question about queue capacity and cover rather than temporary hires and another training round. Growth in intake and growth in the hours spent retyping and sorting it need no longer move in step.
04
Idle AI tools get a verdict
The assistant subscriptions and automation platforms bought in a hopeful quarter either get wired into work people actually do, or get named as licences to drop. The pile stops growing quietly while nobody is willing to make the call.
05
Awkward cases stop disappearing
The items that do not fit the rule, an odd file format or a reply that arrives out of order, reach a named person with the context attached, rather than sitting in a queue nobody watches until a customer chases it.
06
The workflows keep running without us
Connection accounts, credentials, the rules themselves and the run history sit in your systems, written down as they are built. When the engagement ends your own people can read a workflow, change it, or switch it off deliberately.
How it works
From first call to first automation
Introductory call
You describe where work piles up. We say plainly whether a fractional teammate fits, or whether a single project, a hire or nothing at all would serve you better.
Scope and quote
We agree the slice of the week, the systems in play, who the teammate reports to and how access is granted. Everything is quoted before work starts.
Two weeks of watching
The teammate sits in your standups and follows the real workflow before touching it, counting how often each task runs and how often it goes wrong.
A shortlist, including what to leave alone
You get the candidates ranked by hours saved against risk, and a plain list of the ones we advise leaving manual. Automation copies a process, including a bad one.
One workflow at a time, run in parallel
Each automation ships on its own and runs beside the manual process until it earns trust. At the weekly demo you see what it handled, what it escalated and what it got wrong.
Who it's for
Where this fits
These are the places where the same handling happens hundreds of times a month, and where getting the automation wrong would cost more than doing nothing at all.
Operations teams buried in intake
Forms, emails and documents arrive faster than people can sort them. The volume is steady, the rules are knowable, and nobody has had time to write them down.
Franchises and multi-site operators
The same process runs at every location with a different variation in each. One workflow, built once and rolled out centrally, removes more handling here than anywhere else.
Teams that bought AI tools and stalled
Licences are in place, a pilot was built, nothing reached production. What is missing is someone senior owning the integration, the guardrails and the boring last twenty percent.
Back offices with a seasonal spike
Renewals, applications or tax-season paperwork arrive in one heavy quarter and are handled by temporary hands. The same workflow, automated once, carries the peak every year after.
12+
Years of studio experience since 2014
1–2
Typical commitment — 1–2 days a week
EN/FR
Languages this engagement can be delivered in
Other fractional roles
Often paired with
01
Generative AI Developer
Retrieval that grounds answers in your own content, an evaluation set that catches regressions before release, guardrails, and a named person who signs off.
Explore02Systems Integration Specialist
A senior integration specialist from our Vancouver studio joins your team part-time, in your repositories and your standups.
Explore03CRM & Automation Specialist
A senior specialist from OlDevs owns your CRM for an agreed share of each week: the data model, pipeline stages, lifecycle automation, routing and reporting.
ExploreFAQ
What buyers ask about automation
They spend their slice of the week inside your operation: sitting in standups, watching a process, building one automation, then reviewing what it did. A typical week has one build thread and one measurement thread. You see both at the weekly demo. Between sessions they answer questions from your team within one business day, and the studio covers anything outside their discipline.
Because the platform is the easy part. Most stalled automations fail on the parts a licence does not cover: agreeing what a person must still approve, handling the cases that do not fit the happy path, wiring into a system with no clean API, and keeping the thing working when a form changes. That is judgement work, and it continues after the build.
It gets caught by design or it gets caught by a customer, and we build for the first. Every workflow has a confidence threshold, an exception queue and a named reviewer. Failures are logged with the input that caused them, and the fix is either a rule, a better prompt, or moving that case back to a person permanently. We report failures at the weekly demo rather than waiting to be asked.
Often. If nobody internally owns the process, automation just makes a broken process faster and harder to see. If the work is one contained build with an end date, buy it as a project instead. If the volume is low and the judgement high, leave it manual and spend the effort elsewhere. We say this at the first call, not after signing.
Into your own accounts and your own regions, not ours. Plain rules handle most steps and need no model at all. Where a model is needed we choose it with you: a hosted API for general text, a private or on-premise model where records cannot leave your control. Every automated step is logged, and nothing is trained on your data without a written agreement.
That is your decision, not ours, and we will not pretend to know your headcount plans. What we can describe is what automation does to the work: it takes out repetitive handling and leaves the judgement calls, which changes what a role looks like more often than it removes one. We design review steps that require a person, so someone on your team stays accountable for every output.
Let’s connect
Let’s talk about the ai automation specialist gap.
Tell us what is not getting done and roughly how much of a week it needs. We’ll reply within one business day with who would cover it and a tailored quote — no obligation.