Service
Dedicated development team services senior engineers who work with AI, for a fixed monthly fee
An Agentic Pod is a dedicated development team of two or three senior forward deployed engineers who join your team, take tasks from your backlog and deliver them on the Artkai AI SDLC, the working method that comes with the pod. Every task closes with a record of what each stage produced and who authorized it. You pay a fixed monthly fee for the pod instead of the time and materials of IT staff augmentation.
The situation
Two engineers short in the second week of the quarter
The roadmap for the quarter was agreed in September, and by the second week it already has a hole in it. One engineer resigned as the last quarter closed, and another is tied up on an incident that is still open. The two items the leadership team asked about are still in the queue. A job posting would take a season to fill, and the quarter ends first.
The usual answer is staff augmentation: a stack of CVs, interviews, and then hours on an invoice. The people may be good, but someone on your side still has to manage them, and at the end of the month the bill tells you how long they worked, not what was done. What the quarter needs is the queue moving, and a way to see that it moved without adding another stand-up.
How we work
How a dedicated development team works alongside your own engineers
Hire a dedicated development team you have already seen work
The first step is a trial sprint of about two weeks on several tasks from your backlog, with the pod that would work with you. At the end you hold the finished tasks, the record of how each was done and the engineers you would keep, so choosing a dedicated team rests on work you have seen, not on CVs and interviews.
The pod works from your backlog, and your product owner sets what done means
The pod takes tasks from your backlog and works in your repositories, to the conventions of your codebase. Your product owner stays reachable, because that is who decides what a task is meant to do, and your engineers review what the pod produces the way they review any other change. The pod joins your team; it does not run a separate project beside it.
Hire AI developers who bring their own method
The pod's engineers work on the Artkai AI SDLC. Agents work inside each stage of a task, from shaping the requirements to the code, the test cases and the automated tests, and a document leaves its stage only after a security check and when one accountable person has authorized it. Your project keeps who and when. Your engineers do not have to adopt the method to work with the pod. The method has run in two engagements so far, at a US health-tech company and a European education company; the pod is how it now comes to your team.
A fixed monthly fee, and a month judged by what was delivered
Under a fixed monthly fee the question at the end of each month is what was delivered, not how many hours were logged. Every task closes with the artifacts of the method and the authorization on each, and the record shows where an engineer corrected or rewrote what an agent had drafted. You can check the work without sitting in on it.
Trial sprint
- The tasks
- Several items from your backlog as your product owner wrote them, and what done means for each.
- Who worked on it
- The pod's engineers and the part each of them played.
- What each stage produced
- The document each stage produced on each task, and the role that authorized it.
- Where agents worked and where an engineer stepped in
- The points at which a person corrected, rejected or rewrote what an agent had drafted, so you can judge the method on the work itself.
- Your decision
- Whether to keep the pod, at what size, and which items from your backlog it would take first.
What you get
Engineers in your team a method in their hands, a record of every task
The pod adds capacity where your backlog needs it, the method decides how each task is done, and the record lets you judge the month on the work itself.
Senior engineers in your team
A dedicated software development team that works inside your process, not next to it.
- Senior forward deployed engineers who join your team
- Tasks taken from your backlog, in your repositories
- What done means set by your product owner
- Their work reviewed by your engineers like any other change
The method they work on
The Artkai AI SDLC comes with the pod, so your team does not have to adopt it.
- Requirements shaped with agents before code is written
- Code, test cases and automated tests produced with agents
- Each stage closed by one accountable person
- An engineer correcting or rewriting what an agent drafted
A month you can check
What was delivered, and how, without sitting in on the work.
- A trial sprint on your own backlog before you commit
- The artifacts of every task, with the authorization on each
- A record of who authorized what and when, kept in your project
- A fixed monthly fee instead of time and materials
Proof
The pod is new and has no case of its own the method it runs on has two
Artkai AI SDLC at a US health-tech company
Agents documented legacy systems and turned validated requirements into test cases.Case study →
Influencer platform
An AI system for an influencer marketing arm: it finds and scores creators, runs outreach across Instagram, email and LinkedIn, and AI agents carry deals through to a published placement.Case study →
First step
Trial sprint
How long
about 2 weeks
From you
several tasks from your backlog; a product owner reachable
You hold at the end
the tasks delivered with the Artkai AI SDLC, and the team you would keep. The code is yours, and who authorized each document, and when, is kept in your project.
Questions
How is an Agentic Pod different from IT staff augmentation?
Staff augmentation usually bills time and materials for people you then manage. A pod is a fixed monthly fee for senior engineers who bring their own working method, and each month is judged by what was delivered rather than by the hours logged.
Staff augmentation vs managed services: where does a pod fit?
Staff augmentation adds people you manage yourself; managed services hand over an outcome and keep the team out of sight. A pod sits between them: the engineers work inside your team and from your backlog, and the method records how each task was done, so you can check the work without managing every step.
Can we meet the engineers before we hire a dedicated development team?
Yes. The first step is a trial sprint of about two weeks on tasks from your backlog, with the pod that would work with you. The decision rests on work you have seen, not on CVs.
How do you manage a dedicated development team you did not hire yourself?
Through what you already have: your backlog and your product owner. The product owner decides what each task is meant to do, your engineers review what the pod produces, and every task closes with its artifacts and the authorization on each.
Do our own engineers have to adopt the method?
No. The method stays with the pod. Your engineers review what it produces, and every task closes with its artifacts and the authorization on each.
Is this a way to hire AI developers?
The pod brings senior engineers who work with agents inside each stage of a task, with a person authorizing what leaves it. You hire that way of working, not a separate AI role.
What can a dedicated software development team take on?
Tasks from the backlog of a product you already run, as your product owner writes them. When the work is a whole new product that needs a scope and a prototype first, AI MVP Development is the better way in.
What does the record of a task show?
The document each stage produced, the role that authorized it, and the points where an engineer corrected, rejected or rewrote what an agent had drafted. You see it first on the trial sprint, before you decide on the pod.
Why hire a dedicated software development team instead of outsourcing a project?
A dedicated team works from your backlog, in your repositories, under your product owner. An outsourced project hands a scope to someone else. Here the engineers join your team, and you keep the decisions about what each task is meant to do.
What does a dedicated team for software development do in your model?
Two or three senior engineers take tasks from your backlog and deliver them on the Artkai AI SDLC. Your engineers review the result like any other change, and each task closes with its record.
Related
AI MVP Development
When the work is a whole new product that needs a scope and a prototype first.
AI SDLC Enablement
When your own engineers should run the process themselves once the pod has shown it working.
Artkai AI SDLC
The method the pod's engineers work on, set out in full before you decide on a trial sprint.