Generative AI Development Services

Generative AI development services cover building a new product whose core capability is generation: a copilot, a document or knowledge product, a content tool, an agentic workflow, or a multi-tenant platform.

The work is more than prompting a model. It includes the retrieval layer the product is grounded in, the software around it (authentication, permissions, billing, interface), the evaluation set that proves quality before release, and the cost design that keeps inference costs under what you charge.

They are two different jobs. Development means building a new generative AI product or a substantial new system, where the architecture, the data model, and the commercial shape are all yours to decide. Integration means adding generative capability to existing software with existing users, where your current data model and permissions set the constraints.

Adding AI to systems you already run is covered by our AI Integration service and a dedicated Generative AI Integration Services page. Plenty of clients need both, and we will tell you which one your goal actually is during scoping.

Per industry averages, a focused generative capability or proof of concept typically runs from around $20,000 to $80,000, while a production-grade generative AI product usually lands between roughly $70,000 and $300,000+, depending on data readiness, retrieval complexity, integrations, and compliance scope. Inference is a separate running cost that scales with usage, which is why cost per user is modeled before launch rather than discovered afterward.

Custom work doesn't fit a fixed price list, so we scope your project and provide a tailored estimate after an initial consultation. In the meantime, the scope generator gives you a quick ballpark.

Our architect-led, AI-accelerated delivery gets you a working proof of concept on your real data in about 1 week and a functional MVP in roughly a month, then iterates to production from there.

Products carrying regulated data, heavy document ingestion, or a large evaluation surface take longer, and the definition phase tells you which category you are in before you commit to a timeline.

The first step is a Scope & Design Document that turns the idea into a build-ready plan, which is what our Product Discovery engagement produces.

We build across OpenAI, Anthropic, Google, Llama, and Mistral, behind a provider abstraction layer, so switching models is a configuration change rather than a rebuild. We also use managed services (Amazon Bedrock, Azure OpenAI, Google Vertex AI) where they fit. This is how we run our own products, routing across several providers in production, which is also the fastest way to take advantage of a cheaper or better model when one ships.

By treating cost per user as an architecture decision rather than an invoice surprise. We right-size the model per call, cache repeated work, batch where it helps, and route simple tasks to smaller models, then measure the result against a target cost before launch. On an AI talent-acquisition platform we took from proof of concept to production, a large share of the savings came from removing redundant model calls and pre-processing payloads before they ever reached the model.

With a labeled evaluation set and a baseline score, run before release and re-run on every prompt and model change. Answers are grounded in your records and shown with sources attached so users can check them, and output validation plus refusal paths sit in front of anything outside scope. Anything that writes to a system of record or contacts a customer passes an approval gate first.

Yes. For regulated or sensitive data, we deploy open models in your own cloud or on-premises and design retrieval so it respects the access rules you already run. We have built exactly this: Sylo is a self-hosted AI agent platform we built from scratch that ships as a single binary on the user's own hardware, where memory and model credentials never leave the machine.

Yes, completely. You own all the source code, prompts, evaluation sets, retrieval pipelines, fine-tuned weights where they exist, and intellectual property, with full rights transferred to you. There is no vendor lock-in: you are free to host, extend, or move the product to another team whenever you want.