AI Development & Integration
Devs & Logics adds production AI to your product—OpenAI and multi-model APIs, RAG over your documents, agents with tool calling, and monitoring so LLM costs stay predictable.
The problem we solve
Teams prototype ChatGPT wrappers that break in production: no evals, runaway API bills, or hallucinations in customer-facing flows. You need engineering that treats AI as a product surface with limits and observability.
What we deliver
We integrate AI into existing Next.js and Node apps for US SaaS teams—not standalone demos—so features ship inside your auth, billing, and compliance boundaries.
- OpenAI and multi-model integrations
- RAG and knowledge-base chat
- AI agents and tool calling
- Prompt engineering and evaluation
- Rate limiting and cost controls
- HIPAA-aware patterns where required
Our process
- 1
Use-case & risk review
Define what the model may and may not do; PII, HIPAA, or finance rules documented upfront.
- 2
RAG or agent design
Embeddings, retrieval, prompts, and tool schemas—with evaluation sets before launch.
- 3
Integration & UI
Streaming chat, copilot panels, or background jobs inside your existing app architecture.
- 4
Cost & quality ops
Rate limits, caching, logging, and dashboards for token spend and failure modes.
Proof & outcomes
- OpenAI, LangChain, and vector DB integrations on live SaaS products
- RAG chat and workflow automation with evaluation before go-live
- Token cost controls and caching strategies for scaling user bases
Technologies
Related articles
Frequently asked questions
Can you add ChatGPT-style features to our existing SaaS?
Yes. We embed chat, copilots, or batch AI jobs into your current Next.js or Node app with your auth and tenant model—users never leave your product.
What is RAG and do you implement it?
Retrieval-Augmented Generation grounds answers in your documents or database. We build embedding pipelines, vector search, and cited responses for support bots and internal tools.
How do you control OpenAI API costs?
Semantic caching, model routing (mini vs flagship models), per-user rate limits, and usage dashboards—so AI spend scales with revenue, not surprises.
Areas we serve
8 US markets — explore local pages:
Discuss your AI Integration project
Share your goals and timeline. We will propose a clear MVP or build plan.
Contact us