AI factory
An AI factory is the repeatable setup, infrastructure, team, and process, that turns AI use cases into shipped software, one after another. We build yours, or run it for you.
Design an AI delivery roadmapWhat is an AI factory?
When an AI factory makes sense, and when it does not
An AI factory is not the right first move for a company with a single AI idea. If you have one use case, build that one well; the shared infrastructure only pays for itself once there is a queue of use cases behind it. The factory earns its keep when AI stops being a project and becomes a pipeline: when the business keeps generating candidate use cases faster than one-off projects can absorb them, when every new project keeps rebuilding the same evaluation and deployment plumbing, and when 'is this good enough to ship' means something different to every team. If that is your situation, the factory is the fix. If it is not yet, we will tell you so rather than sell you infrastructure you do not need.
One-off projects, an in-house team, or a factory?
| One-off projects | In-house AI team | AI factory | |
|---|---|---|---|
| First use case live | Fast | Slow; hire and ramp first | 8 to 12 weeks with the shared base |
| Second and later use cases | Rebuild the plumbing each time | Faster once the team gels | Significantly faster; the base is reused |
| Quality consistency | Varies by project and vendor | Consistent once standards exist | Consistent by design; one evaluation framework |
| Upfront cost | Low per project | High; salaries before output | Shared base plus first use cases, then per-case cost drops |
| Who runs it | A vendor, per engagement | You, once hired | Built for you to own, or operated by Codiot |
| Best when | You have one clear idea | AI is core and permanent staff make sense | You have a queue of use cases and want repeatability |
What we build.
Shared infrastructure
Reusable data pipelines, vector stores, and deployment patterns, so each new AI use case starts from a working base instead of zero.
Standardized evaluation
A common framework for testing AI quality across use cases, so 'good enough to ship' means the same thing every time.
A dedicated team or embedded pod
Either a team we staff and run, or one embedded inside your organization, depending on how much you want to own directly.
Prioritization process
A repeatable way to evaluate and rank new AI use case requests, so the factory doesn't get clogged by every idea that comes in.
Governance and guardrails
Consistent safety and compliance checks applied across every use case the factory ships.
How it works with Codiot.
Build the shared base
Data pipelines, evaluation tooling, and deployment patterns get built once, for every use case after to reuse.
Ship the first two or three use cases
Early wins prove the model and refine the shared infrastructure before scaling headcount.
Scale the intake process
A prioritization framework lets the factory absorb new requests without every one becoming a custom project.
Stack we use, and why teams choose us.
- ·Senior engineers only, no hand-offs to juniors mid-project
- ·Overlap hours with US/EU time zones
- ·Weekly demos and a single point of contact
- ·Code you own, documented and tested
When it fits, and when it doesn't.
Signs you need this
- ·AI projects are one-off and reinvented by each team.
- ·There is no shared way to build, evaluate, deploy, and monitor AI.
- ·Good pilots stall because there is no path to production.
- ·You are scaling from one AI feature to many across the organization.
What's included
- ·Reusable delivery infrastructure: pipelines, evaluation, and monitoring.
- ·Shared patterns for retrieval, guardrails, and deployment.
- ·Governance and standards applied across AI projects.
- ·Enablement so your teams can build on the platform.
What's not
- ·Any single AI feature in isolation, which is AI development.
- ·Model and infrastructure usage fees, billed by providers.
- ·A mandate to adopt AI where it is not warranted.
When this isn't the right fit
- ·You have one AI use case, not a portfolio; start with that instead.
- ·There is no organizational commitment to AI beyond a single pilot.
- ·Basic delivery discipline for software is not yet in place.
Common questions, answered plainly.
What is an AI factory?
How is this different from AI development services?
How much does building an AI factory cost?
Can you run our AI factory for us instead of us hiring a team?
How long until the factory is producing results?
Do we need our own data team to have an AI factory?
How is an AI factory governed so it does not ship unsafe use cases?
Explore related work.
Let's talk about an AI factory.
Tell us what you're building. We'll reply within two business days with an honest take on scope, timeline, and cost.
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