AI Development for
SaaS & Tech Companies

We help SaaS founders and CTOs add AI features to their products , LLM integrations, RAG systems, LangGraph agents, and agentic workflows. Launch AI-powered features in weeks without rebuilding your product.

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LangGraph Agents
RAG Systems
LangChain
OpenAI API
Qdrant
4–8w
Typical AI feature delivery
From scoping to production deployment without disrupting your existing product.
3x
User engagement increase
SaaS products with AI features see significantly higher daily active usage.
60%
Support ticket reduction
In-product AI assistants resolve common user questions before they reach support.
What We Build

AI features built for SaaS products

Every feature integrates with your existing product, data, and infrastructure , no full rebuild required.

AI-Powered Search

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.

LangChainQdrantSemantic SearchVector DB

LLM & API Integration

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.

OpenAIClaudeGeminiStreaming

RAG Systems on Your Data

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.

RAGLangChainQdrantDocument AI

LangGraph Agentic Workflows

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.

LangGraphAI AgentsWorkflow AutomationTool Calling

In-Product AI Chatbots

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.

LangGraphIn-Product AIUser ContextAPI Access

AI Analytics & Insights

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.

LLM SummariesAnomaly DetectionPredictive AIDashboards
Why Us

AI features that ship in weeks, not months

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.

No Full Rebuild

AI features added to your existing product via APIs and integrations.

Your Data, Your AI

RAG systems trained on your product data , not generic LLM responses.

Fast Delivery

Most AI features shipped in 4–8 weeks with bi-weekly demos.

Scales With You

Architecture designed to handle growth from 100 to 100,000 users.

Related Services

Generative AIAI Chatbots & AgentsSaaS DevelopmentCustom SoftwareWeb Development
FAQ

Common questions

We integrate AI capabilities into your existing SaaS without a full rebuild. We connect your product to LLM APIs (OpenAI, Claude, Gemini), build RAG systems on top of your data, and add LangGraph agents that automate workflows inside your platform. Most AI feature additions are completed in 4–8 weeks without disrupting your existing codebase or user experience.
LangGraph is a framework for building stateful, multi-step AI agents. Inside a SaaS product, a LangGraph agent can automate complex user workflows , for example, an agent that reads a user's data, generates a report, sends a notification, and updates a dashboard, all triggered by a single user action. This turns your SaaS into an autonomous system that works for users, not just responds to them.
A RAG (Retrieval-Augmented Generation) system connects an LLM to your product's data , user documents, knowledge bases, product content, or databases. Instead of generic AI responses, your users get answers grounded in their own data. This is the foundation of AI search, intelligent assistants, and document Q&A features inside SaaS products. We build RAG systems using LangChain and Qdrant.
We work with all major LLM providers , OpenAI (GPT-4o, o1), Anthropic Claude, Google Gemini, and open-source models via Ollama or Hugging Face. We help you choose the right model based on your use case, latency requirements, cost constraints, and data privacy needs. We also implement fallback logic so your product stays available if one provider has downtime.
A simple AI feature like a chatbot or AI search typically takes 3–5 weeks. A more complex feature like a LangGraph agent workflow or full RAG system takes 6–10 weeks. We use agile sprints with bi-weekly demos so you see working features throughout development, not just at the end.
Yes , this is the most common integration pattern. We connect AI systems to your existing Postgres, Supabase, or custom database using LangChain tool calling. The AI can query your data, generate insights, and take actions based on real user context. We also build vector search layers on top of your existing data for semantic search and RAG capabilities.
Get Started

Ready to add AI to your SaaS product?

Schedule a free consultation. We will review your product, identify the highest-impact AI features, and outline a delivery plan.

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