AI and LLM integration for apps
We integrate AI into real products — not proof-of-concept demos. That means LLM-powered features wired to your data, RAG pipelines built on your documents, agents that take actions inside your product, and workflow automation that saves your users hours each week. We work with the leading models and providers and choose the right tool for the job, not the most hyped one. If your product needs AI that actually ships and performs in production, that's what we build.
Everything in the engagement
LLM feature integration
GPT, Claude, Gemini, or open-source models integrated as product features — summaries, generation, classification, extraction, and conversational UIs.
RAG systems
Retrieval-augmented generation pipelines that let your AI answer questions grounded in your own documents, knowledge base, or database.
AI agents & tool use
Agents that can browse, search, call APIs, write code, or take actions inside your product — with guardrails that keep them reliable in production.
Prompt engineering & evaluation
Structured prompts, system messages, and an evaluation harness to measure output quality and catch regressions as models or prompts change.
Workflow automation
AI-powered automations that trigger on events, process data, and hand off to humans at the right moment — integrated with your existing stack.
Our approach
Diagnose — we identify exactly where AI adds genuine value in your product, not just where it's technically possible.
Plan — we define the data flow, model selection, latency budget, and cost model before building anything.
Build — feature integration with streaming responses, error handling, fallbacks, and cost controls from the start.
Ship — load-tested, monitored, and deployed — with logging and observability so you can see how the AI is performing.
Support — model versioning, prompt iteration, and ongoing evaluation as your usage grows.
Technologies we use
- OpenAI API
- Anthropic Claude API
- Vercel AI SDK
- LangChain
- LlamaIndex
- Pinecone
- pgvector
- Node.js
- Python
- TypeScript
- Redis
- Next.js
Related work
- SaaS Platform
Minute Master
An AI-powered meeting productivity platform that captures, transcribes, and surfaces action items so teams spend less time in meetings and more time shipping.
- Marketplace
Kamingoo
An AI-powered e-commerce marketplace with visual segmentation that lets buyers find products by image — not just keyword search.
Frequently asked questions
Which AI model should we use for our product?
- It depends on your latency, cost, and capability requirements. We evaluate the right model for each use case rather than defaulting to one provider — and we benchmark them against your actual data before committing.
What is RAG and do we need it?
- Retrieval-augmented generation lets an LLM answer questions using your own documents or data, rather than relying only on what it was trained on. If your use case involves searching internal knowledge, support docs, or user data, you almost certainly need some form of RAG.
How do you control AI costs in production?
- We implement token budgeting, caching for repeated queries, model tiering (cheaper model for simple tasks, expensive model only when needed), and usage monitoring so costs scale predictably with your user growth.
Can you add AI features to our existing app without rebuilding it?
- Yes — integration is one of the most common engagements we do. We add AI features as new endpoints and UI components, leaving your existing architecture intact.
How do you handle AI hallucinations in production?
- Through structured outputs, grounding responses in retrieved documents (RAG), confidence thresholds, human-in-the-loop steps for high-stakes actions, and regression testing across a suite of known-good examples.
Related services
Ready to get started?
Tell us about your project. We scope precisely, quote honestly, and ship production software — not demos.
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