WellsManager
Built the product
Operations software for oil and gas teams. It covers field maintenance, well costs, lease operating statements, and investor payments, and it connects an AI assistant to those live records.
FractionalApplied AI software engineer
Hire a fractional AI engineer who ships to production, not another demo.
One senior engineer joins your team for a few months and ships AI agents and LLM features into the product and workflows you already run. No full-time search, no agency handoff.
The embedded model is explained in what a forward deployed engineer is. The work itself is on the services page.
- Engagement
- A few months or a fixed sprint
- Where it runs
- Your repository and your accounts
- Who does the work
- One senior engineer, no agency layer
01 / The problem
Why teams hire a fractional AI engineer
Most AI projects do not fail on the model. They stall on the work around it. These are the situations I get called into.
- 01
The pilot worked in a demo and never shipped
Someone built a promising prototype. It still is not in the product, because the last 20% (auth, your data, failure cases) needs an engineer who has shipped this before.
- 02
You need a senior AI engineer, not a full-time search
A permanent hire can take months to find, and the workload may not justify a seat yet. You need the work done by someone who is already good at it.
- 03
The agency left and nobody can maintain it
Work delivered from the outside tends to arrive as a black box. You want code in your repository that your own team can read, change, and own.
- 04
Your engineers are busy with the roadmap
The team that knows your product has no spare quarter to learn agents, evals, and model behavior. A fractional engineer takes that stream so the roadmap keeps moving.
- 05
You are not sure the AI will behave on live data
The risk is an agent that takes a wrong action on a real record. Every build here has permissions, an audit trail, and a human check where a mistake costs money or trust.
02 / The process
How an engagement works
Scope, embed, hand over. The same three steps, in that order, on every engagement.
Step 1
Scope the workflow
A 30-minute call, then a short written scope: the job, the systems it touches, and what done looks like.
Step 2
Embed and ship
I join your tools and your cadence and build the agent, the integration, or the automation in the product you already have.
Step 3
Hand it over
Someone on your team can run it, change it, and explain it. The code stays in your accounts.
03 / The work
What a fractional AI engineer builds
Four kinds of work. Each one ends in your repository, or in a written decision your team can act on.
04 / The proof
Proof
WellsManager is one place this kind of work has already lived. More detail is on the about page.
05 / The questions
Questions
The short answers to what buyers ask before the intro call.
What is a fractional applied AI engineer?
A senior software engineer you hire part-time, for a few months or a fixed sprint, to ship AI agents and LLM features in your existing product. It is the forward deployed engineer model: embedded in your repository, responsible for shipping, then handing over.
When should I hire a fractional AI engineer instead of a full-time one?
When you can name the AI work but not yet a permanent seat for it, or when a full-time search would take longer than the opportunity lasts. A fractional engineer starts on a real workflow now, and the handover tells you what a later full-time hire needs to be.
How is this different from an agency or a freelance marketplace?
An agency brings a team and usually delivers from the outside. A marketplace gives you a list of profiles to vet yourself. I am one senior engineer in your repository, your issue tracker, and your release process. The code stays in your accounts.
Why do AI pilots stall before production?
Usually the model is fine and the surrounding work is missing: permissions, your real data, an audit trail, failure behavior, and a handover. Closing that gap is the job.
How long does an engagement last?
A fixed sprint is often a few weeks. A retainer is a month at a time and usually runs for a few months. The length is part of the written scope, agreed before the work starts.
Do you work alongside an existing engineering team?
Yes. If you have engineers, I work in their repository and I want their review. If you do not, I still ship in a codebase a future engineer can read.
What happens on the intro call?
Thirty minutes. You describe one workflow and the system it lives in. I tell you whether I am the right person, and which engagement type fits. The call is a conversation, not a pitch deck.
Request an intro call
Bring one workflow and the system it lives in. I will tell you whether I am the right person, and what the first stretch of work would cover.