AI development

AI development services

We're an AI-native agency: AI in how we build, not just what we build. LLM features, agents, and AI products taken from idea to production, with the engineering discipline AI projects usually lack.

Get an AI feasibility review

What is AI development?

AI development is building LLM-powered features and products, from prototype through production, with the same engineering rigor as any other software. Codiot ships AI features, agents, and full AI products end to end, including the evals, guardrails, and monitoring that separate a shipped product from a demo that impressed one meeting.

What is an AI-native agency?

An AI-native agency uses AI throughout its own engineering process, not only in the products it builds for clients. Codiot is one: our engineers use AI tooling daily to move faster and catch more, which is part of why we can ship AI features for you with the same discipline as any other software, rather than treating AI as a separate, riskier category of work.

Three ways to adapt a model

Prompting vs RAG vs fine-tuning.

	Prompting	RAG	Fine-tuning
Best for	Style, format, simple tasks	Answering from your own changing knowledge	A consistent style or narrow task
Uses your data	No	Yes, at question time	Yes, baked into weights
Updates when data changes	Not applicable	Instantly	Needs retraining
Supports citations	No	Yes	No
Cost to update	Lowest	Moderate	Highest

Capabilities

What we build.

LLM feature development

Chat interfaces, copilots, and generative features integrated into existing products.

Agentic workflows

Multi-step agents that take real actions in your systems, with guardrails and human-in-the-loop checkpoints where needed.

RAG and retrieval systems

Grounding LLM outputs in your actual data, so answers are accurate instead of confidently wrong.

Evals and monitoring

Automated evaluation and production monitoring, so AI quality is measured, not assumed.

AI strategy and scoping

Helping you identify which AI use cases are worth building now versus which ones sound good in a slide but don't hold up.

How it works

How it works with Codiot.

Scope the real use case
We separate genuine AI opportunities from ones that are simpler as regular software, before building anything.
Prototype against real data
Early prototypes run against your actual data and edge cases, not a clean demo dataset.
Ship with evals in place
Production launch includes monitoring and evaluation, so quality regressions get caught, not discovered by users.

Why Codiot

Stack we use, and why teams choose us.

OpenAIAnthropicLangChainVector databasesPythonTypeScript

·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

Is this right for you?

When it fits, and when it doesn't.

Signs you need this

·You have an AI idea but no clear path from prototype to production.
·Off-the-shelf AI tools cannot use your own data or workflows.
·Earlier AI pilots stalled before shipping or degraded after launch.
·You need AI features that are measured, governed, and maintainable.

What's included

·Production AI features built on your data and workflows.
·Retrieval, evaluation, and monitoring so quality holds after launch.
·Integration into your existing product and systems.
·Governance, access control, and data-privacy design.

What's not

·Foundation-model training; we build on existing models.
·Model or infrastructure usage fees, billed by the provider.
·Data your use case needs but that does not yet exist.

When this isn't the right fit

·A simple prompt or an off-the-shelf tool already solves the problem.
·The use case has no measurable outcome or clear owner yet.
·The data the feature depends on is not accurate or reachable yet.

FAQ

Common questions, answered plainly.

What is an AI-native agency?

An AI-native agency is one that uses AI in how it builds software, not only in what it builds for clients. Codiot is one: we use AI tooling throughout our own engineering process, which shapes how quickly and carefully we can ship AI features for you.

How much does AI development cost?

It depends on scope: a focused AI feature like a chat interface or a single agent workflow is a much smaller build than a full AI product. We scope and quote after a short discovery phase.

How is this different from just using an off-the-shelf AI tool?

Off-the-shelf tools work for generic use cases. Custom AI development is for when the value is specific to your data, workflow, or product, and a generic tool can't reach it.

How do you prevent AI 'hallucinations' in production?

Retrieval grounding, evals, and guardrails are built in from the start, plus monitoring that flags quality drops after launch rather than relying on users to report them.

Do you help decide what AI to build first?

Yes. Part of the engagement is helping you separate AI use cases that are genuinely valuable from ones that sound good but don't justify the build.