Generative AI Integration Services

Generative AI integration services connect large language models to the software, data, and workflows a business already uses. That might mean adding a Claude-powered drafting assistant to your CRM, building AI-driven search over internal documents, or automating document processing in an operations app. The work covers everything from picking the right model to running it reliably in production. It’s different from building a new AI product from scratch, and for most teams, integration is what they actually need.

Generative AI integration timelines vary, but they tend to follow a similar pattern. The architecture phase is usually two weeks. A proof of concept takes two to four weeks. A production-ready integration most often takes eight to sixteen weeks, depending on how much it touches and how clean your data is. Larger platform builds run six months or longer. We’ll give you a clearer estimate after the architecture phase, once we know what we’re actually working with.

LLM costs are manageable, but only if you measure them from day one. We set per-conversation cost ceilings, route cheaper models to cheaper tasks, cache prompts where it makes sense, and trim outputs when long responses aren’t adding value. We also instrument cost tracking so you can see spend by feature, by user, and by request. The teams that get surprised by their AI bill are the ones who didn’t measure until the invoice arrived.

Yes. Most of the AI integrations we ship live inside the systems our clients already use. That includes Salesforce, custom internal apps, customer portals, and older ERPs. We’ve built workflows that read and write to Salesforce, parse documents into existing schemas, and embed AI features into web apps without rebuilding the surrounding product. Twelve years of custom software work helps a lot here, especially when the system you’re integrating into is older or messier than you’d like.

Generative AI integration adds AI features to software you already run. Generative AI development builds a new AI-first product from the ground up. Most businesses we talk to need integration. The systems exist, the team is using them every day, and the goal is to make them smarter. Development makes more sense when you’re launching something new where the AI is the core of the product. Both come from the same engineering practice; the scope is what changes.

It depends on the use case. Claude tends to do well on safety-critical reasoning, long documents, and tool use. GPT has a deep tooling ecosystem and broad capability. Gemini is a strong choice when cost matters, and you need multimodal. Open-source models like Llama or Mistral, run through AWS Bedrock or Vertex AI, are worth considering when data residency or model ownership is on the table. We’re not loyal to any one provider, and we’ll usually test a couple before committing.

LLM evaluation is something you do continuously, not once. We build an evaluation harness during the proof-of-concept phase using tools like Braintrust or Langfuse, and we run it every time a prompt changes, a model updates, or a new use case shows up. The harness combines automated checks, LLM-based scoring, and human review where the stakes are high. The point is simple: catch quality drift before your users do.

Yes. We’ve shipped AI features in financial services, healthcare, and insurance, where audit logging, access controls, PHI handling, and data residency aren’t optional. Our work on a healthcare insurance platform’s AI claim review was built inside a HIPAA-compliant framework with audit-ready outputs and security protocols at every step. Compliance is part of the architecture from the start, not something we retrofit before launch.