We integrate GPT, Claude, Gemini, Llama and Mistral into your product and internal tools — with routing, caching, evals, observability and cost controls from day one.
LLM integration services embed large language models (like GPT, Claude, Gemini or Llama) into an existing product or internal system — covering prompt design, model routing, caching, observability, evals, security and cost optimization.
Summarize, draft, classify, search — embedded in your UX.
Pick the right model per request; fall back on errors.
Version-controlled prompts with evals and A/B tests.
Prompt, response and semantic caching; budget guardrails.
Traces, costs, latency and quality per prompt, per model.
A plain answer up front. We'd rather not sell you something you don't need.
Pricing is quoted after discovery based on scope, team shape and delivery timeline.
The people you meet in discovery stay involved through architecture, delivery and launch.
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
Repos, infra, analytics and documentation live in your accounts from the beginning.
Real delivery examples tied to this service area, so buyers can move from claims to shipped work.
A product team added multiple LLM-powered workflows into an existing SaaS platform with model routing, prompt controls and request-level observability.
A product team replaced a brittle Python knowledge surface with a grounded Next.js and RAG stack to improve onboarding and support resolution.
A regulated fintech team needed Arabic retrieval and bilingual answer quality without moving sensitive data to external infrastructure.
“What we needed was not a demo bot. We needed AI features inside the product with cost visibility and sensible controls, and Cuibit built the layer we could actually operate.”
“The difference was that Cuibit treated retrieval quality, evals and guardrails as part of the product, not as cleanup after launch. That is why the system earned trust internally.”
Supporting articles that help buyers understand the tradeoffs, architecture choices and implementation details behind this service area.
Choosing an AI development agency in 2026 is no longer just about prompt engineering. The right partner should be able to design retrieval pipelines, tool integrations, context-aware agents, and the web or mobile product layer that makes AI usable in the real world. This guide explains what to evaluate, which architecture patterns matter, and how to tell whether an agency can deliver production-grade RAG development and LLM integration.
AI in 2026 has shifted from standalone models to full systems built on RAG and LLM integration. Learn how modern businesses are building scalable, accurate, and production-ready AI applications.
A practical May 2026 guide to AI chatbot development cost covering pricing ranges, RAG, LLM integration, workflow automation, hidden costs, and what businesses should actually budget for.
Depends on the task. We route — often Claude for writing, GPT for tool use, Llama local for privacy-sensitive, Gemini for long context.
Prompt + semantic cache, smaller models for easy tasks, budgets and rate limits, response truncation, batching where possible.
Zero-data-retention APIs, PII redaction pre-prompt, fully on-prem options with open models.
Pricing is quoted after discovery based on scope, team shape and delivery timeline. Adding a single AI feature to an existing product, a multi-model routing layer with caching and observability, and an on-prem deployment are each scoped differently, so we share a written proposal after discovery.
Yes — multi-model routing is a core capability. We pick the right model per request based on task type, cost, latency and quality requirements, with automatic fallbacks on errors.
Prompts are version-controlled in code, tested against golden eval sets, and A/B tested in production. Prompt changes go through the same review process as code changes.
Tell us about your project. A senior strategist replies within one business day — with a written first take.