AI Integration Services: LLM Features, Agents and RAG Inside Your Existing Product

AI integration is adding AI capabilities — usually an LLM, an agent or retrieval over your data — to software you already run, without rebuilding it. The AI runs as a separate service your product calls through an API, so your database, UI and business logic stay intact. At IvorySoft an integration project starts with a one-week discovery that decides which capability fits (generation, classification, an agent or RAG), then a prototype on your real data behind a feature flag, then a production rollout with monitoring and fallbacks.

AI integration connects existing models — OpenAI, Anthropic, open-source — to your product and data. AI development trains or fine-tunes models. Almost every SaaS and web product needs integration, not development: the model is a commodity, the work is in retrieval, prompts, evaluation, guardrails and cost control. We take on fine-tuning only when an evaluation set proves that prompting and retrieval cannot reach the target quality.

A discovery week, a two-to-three-week prototype on your data, and a rollout phase — typically 4–8 weeks from kickoff to production for a first feature such as an assistant, an extraction pipeline or an agent. Adding a second capability on the same foundation is usually two to three weeks. Timelines stretch when data is scattered or access is unresolved, which is exactly what the discovery week surfaces.

Two numbers matter. The engineering budget starts with a fixed $4,900 discovery week, credited toward the build if we proceed; the prototype and rollout are priced after discovery, so you never sign an open-ended retainer. The second number is the recurring model spend — we design for it from day one with caching, routing simple requests to cheaper models and per-tenant limits, and give you a cost-per-1,000-requests figure before launch, so the API bill is a known number rather than a surprise on the first invoice.

Yes — that is our default stack. We built the Sourcd.ai OpenAI-powered assistant with React, a Node back end on AWS and Auth0, and we integrate through a provider abstraction so a product can switch between OpenAI and Anthropic models without touching its features. Mobile products get the same layer through React Native.

No. The integration runs as a separate service behind your existing API and a feature flag, so current users see nothing until you switch a cohort on. Your database schema and UI stay as they are; the AI service reads through the same permissions your app already enforces. Rollback is turning the flag off. The only changes inside the existing codebase are the calls to the new service and the UI for the feature itself.

Data stays in your cloud account; we call model providers with the minimum context a feature needs, strip identifiers where the feature allows, and use provider settings that exclude your data from training. For regulated products — fintech, healthcare — we add a human review step, prompt and response logging with PII redaction, and per-tenant isolation. You get a one-page data-flow diagram as part of discovery, useful for your own security questionnaires.

Not for every feature. Drafting, summarising, classifying and extracting work on the text your product already handles, with no training and no data set to prepare. Your own data matters when the feature has to answer from your documents, your catalogue or your tickets, which is where retrieval comes in: we index what you already store, keep it behind your existing permissions, and the model answers from it instead of from memory. Discovery tells you which of the two you are looking at before anyone writes code.

The integration is built so that this is a configuration change, not a project. Every feature calls the model through one provider layer with the prompt, the evaluation set and the fallback defined per feature, so swapping a retired model for its successor, or moving a feature to a cheaper model, is a matter of re-running the evaluations and flipping the setting. The evaluation harness we leave in your CI is what makes the swap safe: you see the quality difference before your users do.