Hire AI developers who turn models into features your users trust

An AI developer builds software features powered by AI models, most often large language models. Day to day that means designing prompts and structured outputs, building retrieval over company data, connecting models to tools and APIs, setting up evaluations and guardrails and shipping it all as reliable production code. Unlike a data scientist who trains models, most AI developers today build on top of existing models from providers like OpenAI, Anthropic and Google, or on open-source models.

Cost depends on the use case and how far you want to take it. A feasibility prototype on your data is a small, focused project. A production system with RAG over many sources, agent actions, strict privacy rules or private model hosting takes more time and people. Data quality, integrations, accuracy targets and your timeline all play a part. After a short call we give you a free, fixed quote, and we can also estimate your ongoing model API costs.

It depends on your task, data rules and budget, and the answer changes as new models come out. We don't pick by reputation. In the prototype phase we run your real examples through two or three candidates and compare accuracy, speed and cost on your evaluation set. Often the result is a mix, such as a strong model for hard reasoning and a small, cheap one for simple steps. We build so you can switch later without a rewrite.

Retrieval-augmented generation (RAG) means the system first searches your own content, such as docs, tickets or a database, and then gives the most relevant passages to the model to answer from. It lets the AI answer about your products and policies with sources, without training a custom model, and it updates as soon as your documents change. If your AI needs to know things that are specific to your company, you almost certainly need RAG rather than fine-tuning.

We reduce hallucinations in layers. Retrieval gives the model the right facts, and prompts tell it to answer only from those facts and to say when it doesn't know. Answers include citations users can check. Structured outputs are validated against a schema. Our evaluation set includes questions with no answer in your data, so we can measure how often the model admits it doesn't know. For high-stakes tasks we add human review before anything is sent or saved.

It can be, with the right setup. We use business API plans whose terms say your data is not used to train the provider's models, and we keep sensitive fields out of prompts when they aren't needed. Retrieval respects each user's existing permissions, so the AI can't reveal documents a user couldn't open themselves. When data must stay in your environment, we use Azure OpenAI, Amazon Bedrock, Vertex AI or self-hosted open-source models. We sign an NDA on request.

Yes. We build agents that use tools such as your APIs, CRM, ticketing system or database through function calling or MCP. We start with read-only tools and add write actions one at a time, each with clear limits. Risky actions like sending emails, issuing refunds or changing records go through human approval until the agent has proven itself in your evals and in a staged rollout. Every step is logged so you can review what the agent did and why.

A feasibility prototype running on your real data usually takes one to two weeks. A focused production feature, such as a support assistant over your help center or an extraction pipeline for one document type, typically ships in four to ten weeks. Larger systems with many data sources, several agent tools or strict compliance needs take longer. The prototype phase gives us real numbers, so the production timeline is based on evidence rather than hope.

Yes, this is very common. We start by building an evaluation set from your real data and measuring the prototype as it stands, so we know where it fails. Then we fix the weak points, which are usually retrieval quality, missing error handling, no guardrails and high cost per request, and add tracing, monitoring and a staged rollout. If your prototype was built quickly with AI coding tools, our vibe code fixers can help clean up the surrounding app too.

You do. Code, prompts, evaluation sets and configuration all live in your own repository, and all code and IP are assigned to you. Model API accounts, vector databases and hosting are set up under your company. That means you can switch providers, bring the work in-house or move to another team at any time without losing anything.