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Service Detail

AI Agents Built for Autonomy & Engagement

Custom AI agent development. Conversational assistants, autonomous task agents, RAG, LLM fine-tuning, and intelligent chatbots that understand and act.

Conversational AI with context awareness
RAG and knowledge retrieval from your data
Tool-using and multi-agent systems
AI Agents
The Challenge

Generic Chatbots Fall Short

Off-the-shelf chatbots give vague answers and lack domain knowledge. You need agents that understand your data, use your tools, and complete real tasks—not just reply with canned responses.

Generic Chatbots Fall Short
Our Solution

Custom AI Agents That Act

We build agents with RAG, tool use, and optional fine-tuning. They retrieve from your knowledge base, call your APIs, and orchestrate multi-step workflows. Real intelligence for real tasks.

  • Accurate answers grounded in your data
  • Autonomous task completion with tools
  • Seamless handoff to human agents when needed
Capabilities

What We Deliver

Conversational AI Assistants

Natural dialogue, context awareness, multi-turn conversations, and persona tuning.

Autonomous Task Agents

Agents that plan, execute, and complete tasks with minimal human oversight.

RAG Systems

Retrieval-augmented generation for accurate, sourced answers from your knowledge base.

LLM Fine-Tuning

Custom models for your domain, tone, and use cases with reduced hallucinations.

Tool-Using Agents

Agents that call APIs, search, compute, and interact with your systems.

Customer Service Chatbots

24/7 support with escalation, sentiment analysis, and CRM integration.

What You Get

Tangible Deliverables at Every Stage

From agent design to deployment and ongoing iteration.

Agent Codebase

Modular agent logic, prompts, and tool integrations with full ownership.

Documentation & Prompts

Architecture docs, prompt libraries, and integration guides.

Evaluation & Metrics

Accuracy, latency, and engagement reports with benchmarks.

Deployment & Monitoring

Production configs, guardrails, and monitoring dashboards for safety and performance.

Technology

Our Tech Stack

LangChain
LlamaIndex
LlamaIndex
Python
Vector DBs
OpenAI
OpenAI/Claude
Docker
Roadmap

Typical AI Agent Timeline

1

Discovery & Agent Design

Week 1-2

Use case definition, data/knowledge audit, and agent architecture design.

Architecture Data Map
2

Development & RAG Setup

Week 3-5

Build agent logic, RAG pipeline, tools, and prompt engineering.

POC Pilot
3

Integration & Testing

Week 6-7

API integration, safety testing, and evaluation against metrics.

QA Eval
4

Deployment & Launch

Week 8-10

Production deployment, monitoring, guardrails, and handoff.

Go-Live Docs

What Our Clients Say

"The team at GenovaTech took our vague concept and turned it into a robust, scalable platform. Their strategic input was just as valuable as their coding skills."

Robert Fox
Robert Fox
VP of Product, FinServe

AI Agents FAQs

RAG retrieves from your data before generating answers. Use it when accuracy and sourcing matter—knowledge bases, docs, FAQs, internal data.
Yes. We build tool-using agents that execute actions: search, book, calculate, update records, trigger workflows.
Yes. We fine-tune when you need domain-specific tone, output format, or reduced hallucinations. We assess if fine-tuning or prompting is best.
We implement content filters, output validation, rate limits, and human-in-the-loop escalation. We design for safe production use.

Ready to Build AI Agents?

Let's discuss your use cases and how custom AI agents can transform customer engagement and internal workflows.