AI Agent Development

AI Agent Solutions

We build LLM-powered agents and RAG pipelines that automate knowledge-intensive work — from internal Q&A systems to fully autonomous decision-making workflows grounded in your data.

What We Build

Production-grade AI systems — not demos. Every agent we build is evaluated, monitored, and optimised for real-world use.

Custom LLM Agents

Autonomous agents with tool use, memory, and multi-step reasoning — built on GPT-4, Claude, or open-source models depending on your requirements.

RAG Pipelines

Retrieval-Augmented Generation systems that ground LLM responses in your internal knowledge base — accurate, source-cited, and hallucination-resistant.

Conversational AI

Intelligent chatbots and virtual assistants for customer support, internal Q&A, and sales enablement — trained on your data, deployed on your channels.

Autonomous Workflow Automation

Multi-agent systems that execute complex multi-step workflows autonomously — from document processing to data enrichment and decision routing.

Safety, Guardrails & Evaluation

Agent evaluation frameworks, output validation, PII redaction, and safety guardrails that keep AI behaviour predictable and compliant.

LLM Observability & Cost Optimization

Token usage tracking, latency monitoring, prompt caching, and model routing strategies that cut LLM costs by 40–60% in production.

What You Get

End-to-end AI agent delivery — architecture, data pipelines, agent code, evaluation, and production deployment with ongoing observability.

  • AI agent architecture design and technical specification
  • RAG pipeline with vector database setup and indexing
  • Custom LLM agent development with tool integrations
  • Knowledge base ingestion, chunking, and embedding pipelines
  • Agent evaluation framework and quality benchmarks
  • Safety guardrails, output validation, and PII filtering
  • LLM cost optimization and caching strategy
  • Production deployment, monitoring, and ongoing improvement

Tech Stack

LangChain
LangGraph
OpenAI
Claude
Pinecone
pgvector
Python
FastAPI
RAG
LLMs

How We Build AI Agents

01

Use Case Design

We identify the highest-value automation opportunities and design the agent architecture around your data and workflows.

02

Data Preparation

Knowledge base ingestion, cleaning, chunking, and vector embedding — the foundation of accurate AI responses.

03

Build & Evaluate

Agent development with continuous evaluation — we test against real queries before any production deployment.

04

Deploy & Monitor

Production rollout with observability, cost tracking, and regular model performance reviews.

Ready to build something that lasts?

From initial scoping to production deployment — we partner with you end-to-end. Let's start with a conversation.