Generative AI Integration Services

Generative AI integration services add generative capabilities to existing software with existing users: semantic search, in-product assistants, copilots, content generation, and document processing.

The work goes beyond an API call. It includes the retrieval layer that grounds answers in your data, the guardrails that constrain what the model can say and do, the evaluation set that proves quality before rollout, and the monitoring that keeps it from drifting afterward.

They are two different jobs with different starting points. This page covers adding generative AI into a product you already run, where the constraints are your existing data model, your permissions, and users who will notice a regression immediately.

Building a new generative AI product from scratch involves product discovery, a new architecture, and a different commercial shape, and that work is documented on our AI Product Development page. Plenty of clients need both, and we will tell you which one your goal actually is during scoping.

Yes, and it is a large share of this work. Older systems are usually approached via a documented adapter layer rather than by modifying the core, so the generative capability reads and writes through a controlled surface while the existing system continues to run.

Where business logic is undocumented, we add a human-in-the-loop review on anything that writes back, because a model filling in an undocumented rule silently is the failure mode that costs the most to unwind.

Per industry averages, a single grounded capability or proof of concept typically runs from around $20,000 to $80,000, while a production generative layer across several surfaces usually lands between roughly $60,000 and $250,000, depending on data readiness, retrieval complexity, the systems being connected, and compliance scope.

Ongoing inference is a separate running cost, which is why cost ceilings are engineered in from the first sprint.

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 production-ready capability in roughly a month, then iterates from there. Integrations into heavily regulated or undocumented enterprise systems take longer, and the readiness assessment tells you which category you are in before you commit to a timeline.

We integrate across OpenAI, Anthropic, Google, Llama, and Mistral, and we build 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 are the right fit.

For regulated or sensitive data, open models can run inside your own cloud or on-premises, so your data never leaves your environment.

Grounding first: answers are retrieved from your records and shown with the source attached, so a user can check what the system used. On top of that, sit output validation, refusal paths for out-of-scope questions, and a labeled evaluation set that scores the feature before rollout and again whenever a provider ships a new model version. Anything that writes to a system of record or contacts a customer goes through an approval gate.

Your data stays in systems you control wherever the architecture allows, retrieval respects the access rules you already run, and prompts and logs are scrubbed of personal data before they reach an observability tool. For regulated environments, we design around relevant obligations from the first architecture decision, including private or on-premises model deployment where data cannot leave your environment, and audit trails that record what the system retrieved and what it generated.

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