AI agents that do the work
your team repeats every day

What an AI agent is, in our definition

An AI agent is a program with a language model inside, a closed set of tools (your database, CRM, messengers, calendar) and a job description. It reads what comes in, decides which tool to use, acts, and reports. The difference from a chatbot is that it does things: writes the lead into the CRM, checks stock, drafts the reply in the brand’s voice, flags the risky case to a human.

We have built and run these agents for our own businesses before offering them: a multi-channel sales agent answering customers in LINE for a sports-nutrition brand, a money auditor watching margins on a live digital-goods marketplace, a content pipeline producing short videos with a critic agent and a human moderator in Telegram, an analyst that answers “how many orders from Kyiv last month” with real SQL.

What we build

Customer-facing agents. Support and sales in Telegram, WhatsApp, LINE, Instagram Direct, TikTok Shop chat and on the site. They answer from your knowledge base, qualify, book, upsell, and hand off to a person when needed. Every channel has its own anti-ban limits on reply windows and message rate.

Operations agents. Lead routing between managers with round-robin and CRM assignment, daily digests with priorities, sales monitors that alert within minutes when money moves, recruitment screening with scoring, document OCR with a vision model.

Content agents. Product cards, ad creatives, short videos, posts and emails generated in your standard with a compliance gate: a critic model rejects by default, a human approves the final. One of our pipelines cut model calls per video from 66 to about 3 after a redesign.

Analytics agents. A read-only role in your warehouse, a guarded SQL layer (one SELECT, your mart only, forced limits and timeouts), and an agent in Telegram that answers business questions with numbers instead of guesses.

How a project runs

1. Audit of the routine. We sit with the people who do the work and map the real process: where messages arrive, what is answered, what is decided, where it breaks. Half a day to two days.
2. Playbook. Scripts, objections, limits, forbidden actions, escalation rules. This becomes the agent’s system prompt and its test set. You review it before any code.
3. Build and test. The agent runs on recorded real conversations first. We measure answer quality, hallucinations and escalation rate, and fix the playbook until the numbers are right.
4. Launch with guards. Budget caps, rate limits, approval queue for risky steps, full logging. Humans watch the first week closely.
5. Tuning. Two weeks to three months of reviewing conversations and improving, depending on the package.

Who this is for

Businesses that answer more than 30 customer messages a day, teams where a manager spends hours on routing and reports, brands that need content every day, and anyone who already pays for a model subscription and wants it to actually do something. If your volume is lower, we will say so and recommend a simpler bot.

Stack

Python, FastAPI, Claude and GPT via official SDKs with provider fallback, PostgreSQL, Redis, Telegram Bot API, WhatsApp Business API, LINE Messaging API, Meta Graph API, vector stores for RAG, Docker on your server or ours. Keys and data stay in your account.