AI Agents & Chatbots

Digital assistants that work for you 24/7
I build bots and AI agents that conduct natural language conversations, collect data, classify leads, and execute actions — all without human intervention.

Who is it for

  • ✓
    Businesses receiving many inquiries wanting automatic filtering
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    Consultants and service providers wanting 24/7 availability
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    Companies wanting to improve customer service without growing the team

How it works

1

Define the goal

Define what the bot needs to do — filtering, service, sales, or operations.
2

Build and train

Build the bot, define conversation flow, connect to channels (WhatsApp, website).
3

Launch and improve

Go live, monitor performance, and improve based on real data.

Examples

WhatsApp bot for lead qualification and appointment scheduling
Website chat for customer service and FAQ
Internal AI agent for task and process management
AI assistant that monitors systems and reports findings

Related Projects

AI WhatsApp Chatbot →Personal AI Assistant →

Ready to get started?

Let's talk about how this can work for your business.

AI agents and chatbots are the fastest way for a small or mid-sized business to handle customer conversations at scale without growing the support team. A well-built agent qualifies leads, answers product questions, schedules meetings, updates your CRM, and escalates only the conversations that actually need a human. Below is a deeper look at how we design these systems, what they cost to run, and when a chatbot stops being a toy and starts being a real operator inside your business.

Where AI agents replace repetitive work

The bots we build most often live in three places: a WhatsApp Business line, a widget on the company website, and an internal Slack or Teams channel for staff. On WhatsApp the agent greets every inbound lead within seconds, asks the three or four qualifying questions the sales team would normally ask, and either books a call in the calendar or drops a clean record into the CRM. On the website the same engine runs as a chat widget that can recommend services, explain pricing, and hand warm leads off to a human with the full conversation history. Internally, staff-facing agents lookup SOPs, summarize long email threads, and turn free-form requests into structured tickets.

Why custom agents beat generic chatbot builders

Drag-and-drop chatbot platforms are fine for FAQ pages, but they break the moment a customer asks a question that isn't in the script. Our agents are built on top of modern large language models with a retrieval layer pointed at your own documents, CRM entries, and pricing sheets. That means the bot can say "based on your contract from March we already covered that integration" instead of sending the customer to a 404 help page. Custom agents also let us enforce tone, guardrails and escalation rules — so the bot sounds like your brand, refuses to quote prices it shouldn't, and hands off to humans on exactly the signals you care about.

What a real AI agent project looks like

Most projects run two to six weeks. Week one is discovery: we read the last few hundred real conversations, map the top intents, and agree on success metrics (response time, qualification rate, percentage of conversations resolved without a human). Weeks two and three we build — prompt design, tool calls into the CRM and calendar, evaluation suite, safety filters. Weeks four and five we shadow-deploy, compare the agent's draft replies to what a human would send, and tune. By the final week the agent is answering real customers with a human reviewing a small random sample for quality. After launch we keep a monthly retainer to review transcripts, add new intents, and improve the retrieval layer as your product changes.

Data, privacy and compliance

Every agent we ship runs with a strict data contract: conversation logs stay in your infrastructure or in a region you choose, personally identifiable information is redacted before it reaches the language model, and we configure per-environment API keys so a staging agent can never touch production data. For regulated industries we run on models with no-retention agreements, and we can operate the whole stack inside your own cloud account when required. This is the same compliance posture that has let our customers ship AI agents into fintech, legal and healthcare workflows.

AI agents & chatbots — frequently asked questions

How is an AI agent different from a regular chatbot?
A classic chatbot follows a decision tree — you click a button, it gives a canned reply. An AI agent understands free-form natural language, can look up live data from your systems, decide when to escalate to a human, and take real actions like booking a meeting or updating a record. In practice that means users can type the way they actually talk, and the bot keeps working instead of hitting a dead end.
How long does it take to launch an AI chatbot for my business?
A focused WhatsApp or website chatbot typically ships in two to four weeks. Larger multi-channel agents with CRM and calendar integrations usually land in four to six weeks. We always start with a shadow-deployment phase where the agent drafts replies that a human reviews before sending, so you can tune tone and accuracy before turning it loose on real customers.
Can the agent integrate with our CRM, WhatsApp and calendar?
Yes — every agent we build is designed around your existing stack. We routinely integrate with HubSpot, Salesforce, Pipedrive, Monday, Google Calendar, Outlook, WhatsApp Business Cloud API, Telegram, Intercom, and custom internal APIs. If you have a tool with a public API, we can give the agent a controlled tool call for it.
Will the AI give wrong answers or hallucinate?
We engineer against that specifically. Every answer is grounded in a retrieval layer pointed at your own approved documents, and the agent is instructed to say "I don't know" and hand off to a human when confidence is low. We also run an evaluation suite on every prompt change so regressions are caught before they hit customers.
What does it cost to run an AI chatbot in production?
Running cost has three components: model inference (cents per conversation on modern models), messaging channel fees (WhatsApp session fees, for example), and maintenance. For a typical small-business agent handling a few thousand conversations a month, total operating cost usually lands between $80 and $400 per month. We share real cost projections during the discovery phase before you commit.
Who owns the data and the prompts?
You do. The prompts, fine-tuning data, evaluation suite, retrieval index and conversation logs all live in your infrastructure or in a store you own. If we ever stop working together, you keep everything and can continue to run the agent yourself or with another team.

If you're evaluating whether an AI agent makes sense for your team, the fastest way to find out is to look at one week of your real customer conversations and see how many could have been handled automatically. That's the first thing we do in discovery — and it usually answers the ROI question before we write a line of code.