AI Agent Development

We build lead-qualification and sales agents that research inbound enquiries and draft follow-ups, support agents that resolve order and policy questions, research agents that compile market or competitor briefs, operations agents that check order status and inventory and generate reports, and content agents that draft listings and posts in your brand voice. Each starts with one narrow job and expands once it performs on measured tests.

There is no single best model; the right choice depends on the task, so we test candidates on your own examples before deciding. Claude, GPT and Gemini differ in tool use, long-document handling, language coverage, speed and price, and smaller or open-source models are often good enough for classification and extraction at a fraction of the cost. We build model-agnostic so you can switch when a better or cheaper model ships.

Build cost is driven by the number of tools and systems the agent connects to, the data preparation and retrieval it needs, the approval and security requirements, and how many edge cases must be handled. Running cost is mostly model usage per task, which we control with model routing, caching and token budgets and report monthly. A narrow agent on one system costs far less than a multi-agent workflow spanning ERP, CRM and WhatsApp.

Agents are built on the paid API and business tiers of the major model providers, which by default do not use your inputs to train their models; free consumer apps can have different terms, so we never build on them. Agents run in your own cloud and provider accounts, read only what their permissions allow, and log every action. For highly sensitive data we can use region-specific hosting or an open-source model on your own servers.

AI agents can make mistakes, so accuracy is engineered and measured rather than assumed. We ground answers in your documents with citations, restrict actions to defined tools, instruct the agent to say when it does not know, and re-run an evaluation set of real cases after every change. High-impact actions such as refunds, price changes or outbound messages stay behind human approval until measured accuracy justifies relaxing it.

You do. Code, prompts, evaluation sets and MCP servers live in a repository in your name, model and cloud accounts are yours, and everything is documented at handover. You can run, change or move the agent without gaa-tha.

Choose a team that starts from a business process rather than a model, shows how it measures accuracy, explains cost per task, and builds in your accounts with no lock-in. Ask how they handle permissions, approvals and logging, which models they tested for your use case, and what happens when a model is updated or retired. Be cautious of anyone promising full autonomy or guaranteed results before seeing your data.