Services AI Agent Development

Automate

AI Agent Development

LLMs doing actual work inside your systems — not just chat demos.

We build AI agents and LLM-powered tools that take actions, make decisions, and integrate with your existing software stack. RAG pipelines, document processors, internal assistants, and agentic workflows.

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What's included

Everything you need, nothing you don't.

RAG pipelines

Retrieval-augmented generation over your documents, knowledge bases, or database. Accurate, source-cited answers.

Agentic workflows

Agents that plan, use tools, call APIs, and complete multi-step tasks — not just answer questions.

LLM API integration

OpenAI, Anthropic, Gemini, Mistral, or local models. Model selection based on your latency, cost, and data-privacy requirements.

Vector stores

Embeddings and semantic search over your content using pgvector, Pinecone, or Chroma.

Private & on-premise options

For sensitive data — Ollama with local models, or Azure OpenAI. Your data stays on your infrastructure.

API & system integration

Agents connected to your CRM, ERP, database, or internal tools. Not isolated demos.

How it works

From brief to delivered.

01

Use-case scoping

We identify where AI genuinely adds value vs where it would add complexity. Not every problem needs an LLM.

02

Prototype & evaluate

Rapid prototype with real data. We measure accuracy, latency, and cost before committing to production build.

03

Production build

Robust pipeline with error handling, logging, rate limiting, and fallback behaviour when the model underperforms.

04

Deploy & iterate

Production deployment with evaluation framework so you can measure quality over time and improve the system.

Tools & technologies

The stack we use.

OpenAI

Anthropic

Python

Node.js

TypeScript

n8n AI nodes

PostgreSQL

MongoDB

Redis

Docker

AWS / GCP

Cloudflare

Stack varies by project requirements. We choose the right tool, not our preferred one.

Common questions

FAQ

An agent can take actions — call APIs, query databases, write to systems, execute code, and chain steps autonomously. A plain LLM just returns text.

OpenAI API (unlike ChatGPT) does not use your inputs for training by default. For stricter requirements we use Azure OpenAI or self-hosted models.

With well-structured content and a properly tuned retrieval layer, 85–95% accuracy on in-scope queries is achievable. We build evaluation datasets so you can measure this.

Yes — that is the core of what we build. Agents that work inside your ERP, CRM, or support system, not standalone chatbots.

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Ready to get started?

Tell us what you're building. We'll come back with a clear scope and honest timeline.

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