Nearshore teams for AI Operations companies
Industrial, energy and SaaS teams putting AI into the operation. The people who build the automations, keep the models honest and turn data into decisions.
The roles that move the operation
AI Operations Specialist
Runs and monitors AI agents and copilots inside your daily workflows: prompts, evaluations, exceptions and adoption.
Automation Engineer
Builds the integrations and RPA that remove manual steps across CRM, ERP, monitoring and field tools.
Data & Analytics Analyst
Turns plant, field and CRM data into dashboards leadership acts on.
Industrial Intelligence Analyst
Monitoring, predictive maintenance and asset performance insight for plants and portfolios.
People with ai operations experience
Most industrial AI projects die in maintenance, not in the build
The pattern repeats across energy and industrial companies: an enthusiastic build phase produces a set of working automations, everyone is pleased, and then within a year the person who built them has moved on, an upstream API changed, three of them fail silently, and the team quietly goes back to the spreadsheet. The failure was never the model. It was that nobody owned the portfolio after launch.
The roles here are defined around that reality. An AI operations specialist is measured on what is still running and what it saves, not on what was shipped. Automations go live with documentation, error handling, alerting and a manual fallback, because an automation your team cannot route around when it breaks is worse than no automation.
Start where the hours are, not where the demo is
The first month of this role is counting: sitting with the people doing the work and measuring where hours actually go. The results are usually boring and valuable — document assembly, status chasing, data re-entry between two systems that should talk. The exciting use cases tend to rank low once you attach a real hours-saved estimate to each one. Ranking honestly is most of the value, and it is the step that gets skipped.
What stays yours
Everything. The prompts, the agent configurations, the integration code and the documentation live in your accounts and your repository, not in a vendor platform you would have to leave behind. If the engagement ends, the automations keep running and someone else can maintain them. We consider that a requirement rather than a concession — an AI capability you cannot take with you is not a capability you own.
What an AI operations team actually does, day to day
Less model-building than the name suggests. The work splits roughly three ways: finding where hours are lost, building the automations that recover them, and — the part everyone skips — keeping those automations alive once the novelty wears off.
A typical first month is counting, not coding. Sitting with the people doing the work and measuring where time actually goes. The findings are usually unglamorous: document assembly, status chasing, re-entering the same data between two systems that should talk to each other. The exciting use cases tend to rank low once you attach an honest hours-saved estimate.
Why these projects die
The pattern repeats across industrial and energy companies. An enthusiastic build phase produces working automations. Everyone is pleased. Within a year the person who built them has moved on, an upstream system changed, three of them fail silently, and the team quietly routes around them back to the spreadsheet.
The failure was never the technology. It was that nobody owned the portfolio after launch. That is why the roles here are measured on what is still running and what it saves, not on what was shipped.
What we build into every automation
Documentation a second person can follow. Error handling that fails loudly rather than silently. Alerting when a run does not complete. And a manual fallback, always — an automation your team cannot route around when it breaks is worse than no automation at all.
Everything lives in your accounts and your repository: the prompts, the agent configurations, the integration code. If the engagement ends, the automations keep running and someone else can maintain them. We consider that a requirement rather than a concession.
The order that works
Automating a process you have not documented produces an automated mess. If your workflows still live in people's heads, hire the operations role first and the automation role second, roughly six months apart. Teams that invert this order spend the first year rebuilding.
The exception is data reconciliation: if your CRM, your accounting system and your project tracker disagree about basic numbers, that is worth fixing before anything else, because every decision downstream inherits the error.
Where AI genuinely helps in an energy operation
Document extraction from submittals, invoices and spec sheets. Drafting the first version of repetitive correspondence. Flagging anomalies in performance data that a human then investigates. Summarising long technical threads for someone who needs the decision, not the history.
Where it helps less than promised: anything requiring judgement about a specific site, a specific supplier relationship, or a regulation with local interpretation. Those stay with people, and a vendor telling you otherwise has not run one of these operations.
Frequently asked
Is this a machine learning hire?
No, and conflating the two is expensive. Machine learning means training models on your data — a scarcer and pricier profile. These roles compose existing models and tools into workflows that run and keep running.
What should the first ninety days produce?
A measured baseline of where hours actually go, then the two or three highest-hours lowest-risk workflows shipped with documentation, alerting and a manual fallback. Anyone promising twelve automations in month one is selling launches, not outcomes.
Who owns the prompts and the automations we pay for?
You do. Prompt libraries, agent configurations, integration code and documentation live in your accounts and your repository. An AI capability you cannot take with you is a subscription, not a capability.
"More capacity, clearer workflows and a cost structure that gives the team room to grow."Founder, Good Faith Energy · Solar