REZINX
Transparent AI Solutions
In-Product AI Features & Agent Automation
We add AI features to your existing SaaS without a rebuild, from semantic search and RAG to LangGraph agent workflows and in-product assistants. Built with LangChain, Qdrant, and OpenAI or Claude, wired straight into your current stack.
What We Build
Every feature integrates with your existing product, data, and infrastructure, no full rebuild required.
Semantic search that understands user intent, not just keywords. Built with LangChain and Qdrant vector search on top of your existing product data. Users find what they need instantly, even with vague or natural language queries.
Connect your SaaS to OpenAI, Claude, or Gemini APIs with proper prompt engineering, context management, streaming responses, and cost optimization. We handle the full integration layer so your team can focus on product features.
Build a RAG (Retrieval-Augmented Generation) layer on top of your product data, user documents, knowledge bases, or databases. Your users get AI answers grounded in their own content, not generic LLM responses. Built with LangChain and Qdrant.
LangGraph agents that automate complex multi-step workflows inside your SaaS. An agent can read user data, make decisions, call external APIs, update records, and notify users, all triggered by a single action. Turns your product into an autonomous system.
Embedded AI assistants that help users navigate your product, answer questions about their data, and complete tasks through conversation. Built as LangGraph agents with access to your product's APIs and user context.
Intelligent dashboards that surface insights from your product data automatically. LLM-powered summaries, anomaly detection, and predictive analytics that help your users understand their data without needing to be analysts.
Why Us
Most AI vendors want to rebuild your product. We integrate AI into what you already have, connecting to your existing database, APIs, and user workflows without disruption.
Our SaaS AI stack uses LangChain and LangGraph for agent orchestration, Qdrant for vector search, OpenAI or Claude for LLM inference, and FastAPI for low-latency APIs, all integrated with your existing Next.js or React frontend and Supabase or Postgres backend.
AI features added to your existing product via APIs and integrations.
RAG systems trained on your product data, not generic LLM responses.
Most AI features shipped in 4–8 weeks with bi-weekly demos.
Architecture designed to handle growth from 100 to 100,000 users.
We connect directly to your existing SaaS database, backend APIs, and frontend state without requiring a full rewrite. Using secure API integrations, middleware adapters, and asynchronous task workers, we add LLM capabilities and agentic features incrementally so your systems remain stable and functional throughout the process.
A LangGraph agent is a stateful, multi-step orchestration pipeline that allows LLMs to interact with tools and make decisions in loops. Inside a SaaS, this means the agent can execute complex actions like checking user subscription statuses, querying your database, triggering external Webhooks (e.g., Stripe, HubSpot), and generating structured output.
Retrieval-Augmented Generation (RAG) is a system that retrieves relevant private data (documents, database rows, chats) and passes it to the LLM as context to ensure precise, grounded answers. SaaS products need RAG to build intelligent search systems, personalized assistants, and custom reporting tools that base their answers strictly on the user's secure account data.
We are model-agnostic. We integrate whichever models best fit your security and quality needs, including OpenAI's GPT-4, Anthropic's Claude 3.5, and Google's Gemini Pro. For strict data privacy and offline compliance, we can also host and fine-tune open-source models like Llama 3 or Mistral on your private servers.
A standard SaaS AI feature integration (such as semantic search, LLM summaries, or simple chatbots) usually takes between 4 to 8 weeks. We work in bi-weekly sprint cycles and provide live staging environments, so you can test features in real-time before they deploy.
Absolutely. We design data sync pipelines that connect to your SQL/NoSQL databases (Postgres, MongoDB, DynamoDB, etc.), extract and embed relevant data into secure Qdrant vector databases, and synchronize updates in real-time, respecting your existing multi-tenant user access control levels.
Ready to scale? Book a free consultation with our AI architects to discuss your SaaS features, integration models, and security requirements.
Book a Free SaaS Consultation