Skip to main content
RuyaTech

AI Agents & Automation

Custom AI agents for your SaaS product. We build RAG pipelines, multi-agent workflows, and intelligent automation that plugs into your existing stack — not science projects.

OpenAIAnthropic ClaudeLangChainCrewAIAWS BedrockPinecone+12 more
RuyaTech

An AI agent is an LLM-powered system that reads from your databases, calls your APIs, and takes actions within defined boundaries — distinct from chatbots, which only generate text responses without taking action. You have a product that works. Now you need it to do more — answer customer questions from your actual knowledge base, process documents without manual review, or coordinate multi-step workflows that eat hours every week. We build AI agents that connect to your existing databases, CRM, and tools through secure API integrations. RAG pipelines that ground every answer in your real business data. Multi-agent systems using CrewAI and LangChain that handle tasks end-to-end. Every agent we ship is production-grade with monitoring, guardrails, and human escalation built in — because AI that's unreliable is worse than no AI at all.

We build AI intelligence into the SaaS products we ship — RebillHub's error classification engine decides in real time whether to retry, reroute, or escalate failed payments automatically.

See how intelligent automation drives RebillHub
Why Us

Why Choose AI Agents & Automation

What you get when you work with us on this.

AI Agents That Handle Real Support Tickets

Customer support agents that pull context from your knowledge base, CRM, and order history to resolve tickets with accurate, personalized responses. They escalate to humans when the situation requires judgment — not when they're unsure about everything.

RAG Pipelines on Your Private Data

Retrieval-augmented generation systems that search your internal documents, SOPs, and databases. Your AI gives answers grounded in your actual business data — not generic training data. Built with Pinecone, Weaviate, and LlamaIndex.

Integrates With Your Existing Stack

Every agent connects to your existing tools through API integrations — Slack, email, your app dashboard, CRM, payment systems. No need to rebuild your product. AI features plug in alongside what already works.

Production-Grade With Monitoring

Deployed with observability built in: token usage tracking, response quality scoring, latency monitoring, and automated alerts. You see exactly what your AI is doing, how well it performs, and where it needs tuning.

Our Process

Our Process

Step-by-step approach tailored for AI Agents & Automation.

01

Understand Your Workflows

We map your current processes to find where AI creates the most value. Not every task needs AI — we focus on the ones that do.

02

Design the Solution

We pick the right approach for each problem — choosing the right models and designing how they integrate with your product.

03

Build & Integrate

We build the AI features and connect them to your existing systems, databases, and user-facing product.

04

Test With Real Scenarios

We test with your actual data and edge cases. AI needs to be reliable before it touches your users or your operations.

05

Deploy & Monitor

We launch with monitoring in place so you can see exactly how the AI is performing and where it needs adjustment.

AI Agents & Automation — service overview
Technologies

Tech Stack We Use

The tools and frameworks we use to build your product.

OpenAI
Anthropic Claude
LangChain
CrewAI
AWS Bedrock
Pinecone
LlamaIndex
Weaviate
Python
Node.js
TypeScript
Next.js
Vercel AI SDK
n8n
Make.com
PostgreSQL
Redis
Docker
AI & Automation

Want AI That Actually Earns Its Place in Your Product?

Tell us where AI could create real value. We'll scope a focused agent or automation that ships in weeks, not quarters.

Plan Your AI Feature

We respond within 4 hours during business hours. No obligation.

FAQs

AI & Automation FAQs

Common questions about adding AI to your product.

No. Most clients already have a working product and want to add AI features on top — automating customer support, processing documents, generating reports, or building smarter workflows. We integrate with what you have.

Simple AI features (automated responses, document processing) can be live in 2-4 weeks. More complex systems with multiple integrations typically take 6-10 weeks. We start focused and expand from there.

No. AI handles the repetitive, high-volume tasks so your team can focus on the work that actually requires human judgment. Most clients use freed-up capacity for growth, not headcount reduction.

We build guardrails and human-in-the-loop workflows where needed. For high-stakes tasks, the AI flags decisions for human review rather than acting autonomously. We test extensively with real scenarios before launch.

Focused AI features typically ship in 2-4 weeks. Larger systems with multiple integrations take longer. We scope everything during the strategy session and provide a fixed-price proposal.

Customer support agents that resolve tickets autonomously, document processing and data extraction pipelines, RAG-powered knowledge base assistants, multi-agent workflow orchestrators using CrewAI and LangChain, AI-powered recommendation engines, automated report generators, and intelligent lead qualification agents. If your use case involves repetitive work, data processing, or pattern-based decision-making, we can likely automate it.

A chatbot follows scripted conversation flows — it answers questions from a fixed set of responses. An AI agent takes autonomous actions: it reads your database, calls external APIs, makes decisions based on context, and executes multi-step tasks. A chatbot tells you your order status. An agent processes your refund, updates your account, and sends the confirmation email.

Yes — that's our most common AI engagement. We integrate AI features into existing products without rebuilding anything. We connect to your current database, APIs, and user interface. The typical first AI feature is live in 2-4 weeks.

We work with OpenAI (GPT-4.1, GPT-4o), Anthropic Claude (Claude 4.5 Sonnet, Claude 4.6 Opus), and AWS Bedrock for model access. For agent frameworks, we use LangChain, CrewAI, and LlamaIndex depending on the use case. Vector databases include Pinecone and Weaviate for RAG pipelines. We choose the right tool for each problem — not a one-size-fits-all approach.

Yes. RAG (Retrieval-Augmented Generation) is one of our core AI capabilities. We build pipelines that ingest your documents, SOPs, knowledge bases, and databases into vector stores, then connect them to LLMs so your AI gives answers grounded in your actual business data. Common use cases include internal knowledge assistants, customer-facing FAQ agents, and intelligent document search systems.