What our AI agents do
An AI agent combines a language model, a set of tools and a goal. Given "follow up every lead that has not replied in three days", it can look up the leads, read the conversation history, draft a personalised message, send it on WhatsApp, and log the activity in your CRM. Appmetry builds these agents as reliable software, with the same engineering standards we apply to apps used by millions of people.
AI agents we build
- Sales agents: qualify inbound leads on WhatsApp, web chat and email, answer product questions, book meetings and update the CRM. Our own Bizbotify platform runs this pattern for growing businesses.
- Customer support agents: resolve common questions from your knowledge base, collect details, create tickets and escalate with full context.
- Finance agents: read invoices and bills, match them against purchase orders, flag exceptions and post entries to ERP or Tally.
- Operations agents: track orders and dispatches, chase delayed deliveries and keep customers informed.
- HR and internal agents: answer policy questions, run onboarding checklists and prepare documents.
- Research and reporting agents: gather data from several systems and produce weekly summaries for leadership.
How we build agents that can be trusted
- Define the job. Inputs, outputs, allowed tools, escalation rules and what success looks like.
- Design the tools. Narrow, well-described actions such as "create quotation" rather than open database access, exposed through APIs or MCP servers.
- Build the evaluation suite. Real examples, including edge cases, scored automatically on every change.
- Add guardrails. Approval steps, spending and messaging limits, output validation and safe fallbacks to a human.
- Observe in production. Traces of every run, cost per task, success rate and user feedback on one dashboard.
Agents versus chatbots versus automation
| Rule-based automation | Chatbot | AI agent | |
|---|---|---|---|
| Handles unstructured input | No | Yes | Yes |
| Takes actions in your systems | Yes, fixed steps | Rarely | Yes, chooses steps |
| Adapts to new situations | No | Limited | Yes, within guardrails |
| Best for | Fixed, high-volume flows | FAQs | Multi-step work with judgment |
Often the right answer is a mix: deterministic automation for the predictable steps and an agent for the parts that need reading, writing or judgment.
Technology
We build with Anthropic Claude, OpenAI, Google Gemini and open-source models, using tool calling, the Model Context Protocol (MCP), vector search for retrieval, and orchestration in Python or TypeScript. Agents can run in our cloud, yours, or on-premise.
Start with one workflow
Pick one process that costs your team hours every week. We will scope an agent for it, show it working on your real data, and agree the metrics for rollout. Talk to our AI team.