AI Agents & Intelligent Automation
Build production AI agents that automate complex workflows. From customer support to research assistants — we design, implement, and deploy enterprise-grade AI agent systems.
Proven Results
Real AI agent implementations delivering measurable business impact
Enterprise Customer Support Automation
Built a multi-agent RAG system that handles complex customer queries, integrates with knowledge base, and escalates to humans when needed.
Financial Research Agent
Developed autonomous research agents that analyze financial reports, extract key insights, and generate comprehensive summaries for investment decisions.
Healthcare Document Processing
Created specialized AI agents for medical document processing, insurance claims automation, and patient data extraction with full compliance.
Our Development Process
From discovery to production — a proven methodology for building reliable AI agents
Discovery & Requirements
Deep dive into your business processes, pain points, and automation opportunities. We identify high-impact use cases where AI agents deliver maximum value.
Architecture Design
Design agent architecture, data flows, and integration points. We select the right LLMs, vector databases, and frameworks for your specific needs.
Development & Testing
Build agents with comprehensive testing, validation, and safety measures. We implement human-in-the-loop workflows for critical decisions.
Deployment & Optimization
Production deployment with monitoring, logging, and continuous optimization. We track performance metrics and refine prompts for maximum accuracy.
Technology Stack
We work with cutting-edge AI tools and infrastructure to build production-ready systems
LLM Providers
Agent Frameworks
Vector Databases
Infrastructure
Monitoring & Evaluation
Technical Capabilities
- Custom AI agent development with LangChain, LangGraph, and native frameworks
- RAG (Retrieval-Augmented Generation) with Pinecone, Weaviate, pgvector
- Multi-agent orchestration for complex business processes
- Integration with OpenAI, Anthropic Claude, Groq, and other LLM providers
- Agent monitoring, evaluation, and continuous optimization
- Tool use and function calling for real-world system integration
- Custom model fine-tuning and deployment strategies
Use Cases
- Automated customer support with contextual awareness
- Research agents for data analysis and synthesis
- Workflow automation for business operations
- Document processing and knowledge extraction
- Sales and marketing automation agents
Frequently Asked Questions
How much does it cost to build an AI agent in 2025?
AI agent development typically costs between $20,000 - $300,000, depending on complexity. Simple chatbots start around $10,000 - $50,000. Autonomous agents and enterprise systems range from $80,000 - $500,000+. Factors affecting cost include: agent capabilities (rule-based vs autonomous), integration complexity, data preparation needs, and ongoing maintenance. We provide detailed cost breakdowns during discovery.
What's the difference between AI agents and chatbots?
Chatbots are primarily conversational - they answer questions and follow scripts. AI agents are autonomous systems that can execute tasks, make decisions, and take actions on your behalf. While chatbots wait for user input, AI agents can be proactive, learn from interactions, understand context, and perform complex multi-step workflows. Agents can integrate with systems, update databases, and complete actual work, not just provide information.
How long does it take to develop an AI agent?
Timeline varies by complexity: Basic FAQ agents (2-4 weeks), moderate complexity with RAG and integrations (6-10 weeks), enterprise multi-agent systems (3-6 months). The process includes discovery, architecture design, development/testing, and deployment. We provide milestone-based timelines and can deliver MVPs quickly for validation before full builds.
What are the challenges of integrating AI agents with existing systems?
Common integration challenges include: legacy systems lacking modern APIs, data quality issues and silos, security and compliance risks, performance bottlenecks, and unclear business metrics. We address these through: custom API development where needed, data assessment and cleanup, security-first architecture with proper controls, performance optimization, and clear ROI measurement frameworks.
When should I use RAG vs fine-tuning for my AI agent?
Use RAG when: you need dynamic/current information, have large knowledge bases, require citations and sources, or need frequent updates without retraining. Use fine-tuning when: you need consistent output formats, want specific domain expertise, require brand voice control, or need low latency. Best practice: combine both - fine-tune for domain expertise and behavior, use RAG for current information and knowledge access.
Can AI agents integrate with our existing CRM, databases, and tools?
Yes. We specialize in integrating AI agents with existing business systems including CRM platforms (Salesforce, HubSpot), databases (PostgreSQL, MongoDB), APIs, internal tools, and third-party services. We handle authentication, data mapping, error handling, and ensure secure, reliable integrations that maintain data integrity and system stability.
Do you provide ongoing maintenance and optimization after deployment?
Yes. AI agents require continuous monitoring, evaluation, and optimization. Our support includes: performance tracking and analytics, prompt refinement based on real interactions, system updates and security patches, handling edge cases, scaling for increased usage, and retraining as needed. We offer tiered support packages from basic monitoring to full managed services.
How do you ensure AI agents are reliable and make accurate decisions?
We implement multiple reliability measures: comprehensive testing before production, human-in-the-loop workflows for critical decisions, fallback mechanisms for edge cases, performance metrics and monitoring, gradual rollout strategies (canary deployments), continuous evaluation of outputs, and clear escalation paths to human agents. This ensures agents perform reliably and safely in production.
What AI agent technologies and frameworks do you specialize in?
We work with the full AI agent stack: LLM providers (OpenAI GPT-4, Anthropic Claude, Groq, Llama), agent frameworks (LangChain, LangGraph, AutoGPT, CrewAI, custom frameworks), vector databases (Pinecone, Weaviate, pgvector, Chroma), infrastructure (AWS, GCP, Azure, Kubernetes), and monitoring tools (LangSmith, Weights & Biases). We choose the right stack based on your specific needs.
What are common AI agent use cases for businesses?
Popular use cases we've implemented: automated customer support with contextual awareness, research agents for data analysis and synthesis, workflow automation for business operations, document processing and knowledge extraction, sales and marketing automation, code generation and review, internal knowledge management, data analysis and reporting, and task orchestration across systems.