AI Integrations

FAQs

• Who builds custom LLM integrations for B2B SaaS in 2026?

  LLM integration is the process of connecting a large language model — like OpenAI's GPT, Anthropic's Claude, or a similar AI model — to an existing software product so it can perform useful work within that product's context. This is different from using a standalone AI tool like ChatGPT. An integrated LLM has access to your product's data, understands your users' workflows, operates within your security and permission model, and behaves according to rules you define. The result is an AI capability that feels native to your product rather than bolted on — and that does something meaningful for your specific users rather than something generic.
  

• What is the difference between using ChatGPT and integrating an LLM?

  Using ChatGPT means opening a tab and typing. Integrating an LLM means the AI capability lives inside your product, connected to your data, operating under your rules, visible only to the right users, and doing work that's specific to your workflows. The technical difference is substantial: integration requires authentication, data access architecture, security and permission modeling, cost controls, reliability engineering, and a user experience designed around what your users are actually trying to accomplish. The gap between "I can use ChatGPT" and "I've shipped an LLM feature my users rely on" is where most of the real engineering work lives.
  

• How can AI integrate into my web app or mobile app?

  The range is broader than most people initially expect. Common integrations include in-app assistants that understand your product and can answer questions using your actual data, support ticket triage and response drafting, knowledge base question and answer systems that search your documentation and return cited answers, semantic search that finds relevant content based on meaning rather than keyword matching, data-to-text reporting that turns structured records into readable summaries, content operations like summarization, classification, extraction, and rewriting, and workflow automation with human approval gates built in. The right starting point is almost always the specific workflow where your users are currently spending the most time or experiencing the most friction — not a general "add AI" initiative.
  

• What is RAG and do I need it?

  RAG stands for Retrieval-Augmented Generation. It's a framework that improves LLM responses by having the model retrieve relevant information from your own data sources — documents, databases, wikis, PDFs — before generating an answer, rather than relying solely on what it was trained on. You need RAG when your use case requires the AI to answer questions based on your specific content rather than general knowledge. If you're building a knowledge base assistant, a support bot that references your policies, or any feature where accuracy depends on your proprietary data, RAG is the right architecture. Without it, the model will answer confidently from its training data — which may be outdated, incorrect, or simply not specific to your context.
  

• How do I prevent the AI from making things up or giving wrong answers?

  Hallucinations — where an LLM generates plausible-sounding but incorrect information — are a real risk and one of the most common concerns we hear. The primary tools for reducing them are grounded retrieval through RAG so the model is answering from verified sources rather than memory, constrained tool use so the model can only access data it's explicitly permitted to use, validation rules that check outputs before they're shown to users, and explicit "I don't know" behavior so the system declines to answer rather than guessing when confidence is low. No system eliminates hallucinations entirely, but a well-architected integration reduces them to a level that's manageable for most business applications — and fails gracefully rather than confidently when it does encounter the edge of its knowledge.
  

• How do I control the cost of running an LLM in my product?

  LLM costs are driven primarily by token volume — the amount of text sent to and received from the model. Without controls in place, costs can scale unexpectedly as usage grows. The most effective cost controls are prompt optimization to reduce unnecessary token consumption, response caching so repeated or similar queries don't hit the model unnecessarily, rate limiting per user or per feature, model routing so cheaper models handle simpler requests and more capable models are reserved for complex ones, and prompt budgets that cap spending per interaction or per user. Cost architecture should be part of the initial integration design — retrofitting it after launch is harder and more disruptive than building it in from the start.
  

• How do I keep my users' data secure when using an LLM?

  Data security in LLM integrations has several dimensions. PII redaction ensures that personally identifiable information is stripped or masked before it reaches the model. Tenant isolation ensures that in multi-tenant applications, one customer's data can never be surfaced in another customer's context. Least-privilege access means the model can only see data that's relevant to the specific task it's performing. Secrets management ensures that API keys and credentials are handled securely and never exposed in prompts or logs. And audit logging creates a record of what data was accessed and when. These aren't optional security additions — they're the baseline for any LLM integration handling real user data in a production environment.

• Can you integrate an LLM into an existing app without rebuilding it?

  Yes — and this is the most common scenario we work in. Most LLM integrations are additive: a new capability layered onto an existing product without requiring changes to the core architecture. What it does require is thoughtful integration work — connecting the model to your data sources, building the retrieval layer if needed, designing the API surface, handling authentication and permissions, and fitting the new capability into the existing UX in a way that feels natural. The complexity varies depending on how your existing application is architected, but a well-structured codebase can typically accommodate LLM features without a significant rebuild.

• How long does an LLM integration project take?

  A focused integration — a single well-defined feature with clear data access requirements — can typically be designed, built, and launched in four to eight weeks. More complex integrations involving multiple features, custom retrieval pipelines, extensive security requirements, or significant infrastructure work take longer. The most reliable predictor of timeline is how clearly the use case and success criteria are defined before development starts. Integrations that begin with a well-scoped discovery phase consistently move faster and produce better outcomes than those that try to define the scope during the build.
  

• Does Delta Systems build LLM integrations for existing products?

  Yes. Delta Systems integrates large language models into existing web applications, mobile apps, APIs, and internal tools for US-based product teams. We've been building the integrations, databases, and application infrastructure that B2B products run on for years — LLM integration is the same discipline applied to a newer and more capable set of tools. We work with product teams who know what problem they want to solve but need experienced engineers to design and build the AI layer safely, reliably, and in a way that their users will actually find useful. If you're building something and you're not sure whether LLM integration is the right approach or how to scope it, that's exactly the kind of conversation we're set up for.