AI agents that do the work
your team repeats every day

An agent is not a chatbot with a nicer name

An AI agent, in our build, is a program with a language model inside. It has a closed set of tools it is allowed to touch: your database, CRM, messengers, calendar. Plus a written job description that spells out its work. It reads what comes in, picks the right tool, acts, and reports back. A chatbot talks. An agent does things: writes the lead into the CRM, checks stock, drafts a reply in your brand’s voice, hands the risky case to a person.

We ran agents like these inside our own businesses before building them for clients. A multi-channel sales agent answers customers for a supplements brand. A money auditor watches margins on a digital-goods marketplace we are launching. A content pipeline makes short videos with a critic agent and a human moderator in Telegram. An analyst answers “how many orders came from each city last month” with a real SQL query, not a guess.

Where we put agents to work

Customer-facing agents. Support and sales in Telegram, WhatsApp, LINE, Instagram Direct, TikTok Shop chat and on your site. They answer from your knowledge base, qualify leads, book calls, upsell, and hand off to a person when something needs a human touch. Every channel follows that platform’s rate limits, reply windows and published policies. WhatsApp runs on the official WhatsApp Business Platform, with contacts who opted in and templates Meta already approved. The agent introduces itself as an AI assistant at the start of every conversation.

Operations agents. They route leads between managers on a round-robin and log them straight into the CRM. Every morning they send a digest with what matters most at the top. If sales dip, you hear about it within minutes, not at the end of the month. In recruitment screening, candidates are told AI assists the process, protected characteristics are excluded, and bias checks run on a schedule. No one gets rejected without a recruiter’s sign-off. These agents also read scans and photos of documents - the paperwork nobody wants to type by hand.

Content agents. Product cards, ad creatives, short videos, posts and emails, all written in your standard. A compliance gate is built in: a critic model rejects by default, a person approves the final cut. One of our own content pipelines went from 66 model calls per video down to about 3 once we rebuilt it properly.

Analytics agents. A read-only seat in your warehouse. A guarded query layer: one SELECT at a time, your reporting tables only, limits and timeouts baked in. An agent in Telegram answers business questions with numbers instead of a shrug.

How we actually get one live

1. Audit the routine. We sit with the people who do the work. We map what really happens: where messages land, what gets answered, what gets decided, where it falls apart. Half a day to two days.
2. Write the playbook. Scripts, objections, limits, things the agent must never do, rules for when to escalate. This becomes the agent’s instructions and its test set, and you read it before a single line of code gets written.
3. Build it against real conversations. The agent runs on recordings of real exchanges first, not a demo script. We check answer quality, how often it makes things up, and how often it escalates, and we fix the playbook until those numbers hold up.
4. Launch with the guardrails on. Budget caps, rate limits, an approval queue for anything risky, full logging. We watch the first week closely, because that is when the edge cases show up.
5. Keep tuning it. Anywhere from two weeks to three months of reading real conversations and sharpening the agent, depending on which package you picked.

Who actually needs this

Businesses answering more than 30 customer messages a day. Teams where a manager burns hours on routing leads and writing reports. Brands that need fresh content daily. Anyone already paying for a model subscription who wants it to do real work instead of sitting in a browser tab. If your volume is smaller than that, we will tell you so and point you to a simpler bot instead of selling you more than you need.

What it runs on

Stack: Python, FastAPI, PostgreSQL, Redis. Models: Claude and GPT through their official SDKs, with a fallback provider. Messengers: the Telegram Bot API, WhatsApp Business API, LINE Messaging API, Meta Graph API. A vector store lets the agent search your own documents. People call this RAG: it feeds the model your facts instead of letting it improvise. It all runs in Docker, on your server or ours. Your keys, your data, your account.