Generative & Agentic AI services, built for production

We design, build and deploy custom AI chatbots that hold natural, on-brand conversations across your website, WhatsApp, Slack, Messenger, and voice channels. Unlike rigid decision-tree bots, our assistants are powered by the latest large language models — Claude, GPT and open-weight models — so they understand intent, remember context, and respond like your best team member.

Retrieval-Augmented Generation (RAG) lets an AI assistant answer from your own private knowledge — PDFs, wikis, SharePoint, Confluence, tickets, contracts and databases — instead of guessing. We build RAG systems that retrieve the most relevant passages, generate a precise answer, and cite the exact source so your team can trust and verify it.

Agentic AI goes beyond chat: an AI agent can reason about a goal, break it into steps, call tools and APIs, check its own work, and complete multi-step tasks with minimal supervision. We build reliable, production-grade agents — single-agent and multi-agent — that automate real workflows across your business.

Most teams lose hours every day to repetitive, rules-based work: copying data between apps, processing documents, chasing approvals and compiling reports. We build intelligent automation and scheduled AI routines that handle this work reliably in the background — combining classic workflow orchestration with AI where judgment is needed.

The Model Context Protocol (MCP) is the emerging open standard — introduced by Anthropic — for connecting AI models to tools, data and systems. An MCP server exposes your resources (databases, APIs, files, actions) in a standard way so any MCP-compatible AI, like Claude, can use them securely. Build it once, and every agent can use it.

An MCP client is the other half of the Model Context Protocol: it lets your AI application or agent connect to any MCP server and use its tools and data. If you're building an AI product, an internal agent, or a custom copilot, an MCP client gives it a universal way to plug into the fast-growing ecosystem of MCP servers.

Off-the-shelf models know the internet; they don't know your domain. We fine-tune large language models on your data — instruction tuning, LoRA/QLoRA parameter-efficient training, and domain adaptation of open-weight models like Llama, Mistral and Gemma — so the model speaks your terminology, follows your formats and stays inside your compliance boundaries.