DEV Community: forlazin-debug

Client leads from Telegram, MAX and VK: where requests live in each messenger

forlazin-debug — Tue, 27 Oct 2026 05:54:34 +0000

I run Lidolovets (mpleads.ru), a service that finds people asking for a contractor in open chats and channels. We catch leads for businesses, private contractors, freelancers, sole proprietors and sales teams, and we work both as a subscription service and as a lead generation agency. Here is how requests are distributed across three messengers.

Requests live in open chats and channels: city chats, residential complex chats, topic groups. A person writes "need a plumber, any recommendations" and everyone in the group sees it. This is the main source and it works in the self-service dashboard.

MAX

MAX is different. In our testing there are no open group chats in public search, group chats are available by invitation. Requests live in announcement channels by city and niche and in comments under channel posts. The history of a public channel can be read without joining, but there is no global search by message text, so channels are found by name and read one by one.

Requests live in city communities, residential complex communities and in discussions and comments under posts of topic groups. We read public content only and do not message group members from a list.

How it connects

Telegram connects in the dashboard. MAX and VK we connect per client request, together with the team, as part of the managed plan or the setup with an expert.

The key number

Across all sources, 542,795 messages matched keywords and only 14,774 were real requests, that is 2.7%. The remaining 97.3% was ads, vacancies and chatter, so the filter matters more than the search.

5 days free, no card. We do not promise a number of leads.

More (in Russian): mpleads.ru/poisk-klientov-v-telegram-max-i-vk/

Where Telegram chats actually produce client leads: data by city from 14,000 detected requests

forlazin-debug — Tue, 27 Oct 2026 05:27:48 +0000

I run Lidolovets (mpleads.ru), a service that reads open Telegram chats and finds messages from people looking for a contractor. Here is what our database shows as of October 27, 2026, split by city. It is a good reality check if you plan to find clients in Telegram chats, monitor Telegram groups for keywords or build a Telegram lead generation tool.

Moscow: 655 leads. Nizhny Novgorod: 0. Same service, same database.

The numbers

We counted chats that clients of the service connected and that have the city name in the title, plus leads from them (demo data excluded). A hot lead is a message where the person asks to do the task right now.

Moscow and region: 1,177 chats, 104 of them produced leads, 655 leads, 285 hot (43%).

Kazan: 105 chats, 37 leads, 14 hot.

Saint Petersburg: 185 chats, 30 leads, 10 hot.

Novosibirsk: 55 chats, 26 leads, 11 hot.

Krasnodar: 77 chats, 23 leads, 4 hot.

Smaller cities (Perm, Krasnoyarsk, Voronezh, Rostov, Yekaterinburg): 8 to 16 leads each.

How to read it

It is a lower bound, because many city chats do not have the city in the title. Samples under 30 leads are small samples. No leads does not mean no demand, it means few chats of that city were connected.

Why keyword matching alone fails

Across all cities, 542,795 messages matched keywords and only 14,774 were real requests. That is 2.7%, and 97.3% was noise: ads from competitors, job posts, reposts. Roughly 37 irrelevant messages per real lead. A keyword bot forwards all of them, an AI filter reads the message and decides if the author is looking for a contractor.

What to take away

Data from one big market does not transfer to a small one. Test on your own city and your own service before you trust an average.

Full table for 20 cities (in Russian): mpleads.ru/telegram-chaty-po-gorodam/

Comparison of Telegram chat monitoring services and prices: mpleads.ru/servisy-dlya-poiska-zakazov-v-telegram-chatah/

The trial is 5 days with no card, because nobody can promise you a number of leads in advance.

Do you have a market where the numbers look very different from the capital? I would like to compare.

97.3% of keyword matches are junk: lessons from 542,795 Telegram messages

forlazin-debug — Mon, 26 Oct 2026 17:11:43 +0000

We built a service that watches open Telegram chats and surfaces people who are actively looking
to buy something. After processing 542,795 keyword-matched messages in production, here is the
number that defines the whole problem:

97.3% of them were junk. Only 14,774 were real purchase requests.

That is roughly 36 useless messages per 1 real lead. Last 30 days alone: 7,945 real requests
against 363,508 rejected.

If you are thinking about building keyword monitoring for lead generation, this post is the thing
I wish someone had told me before we started.

Why keyword matching fails so badly

The naive approach is obvious: pick keywords, match messages, get leads. It works for about a week.

The noise is not random garbage. It is specifically the stuff that looks exactly like a lead:

Competitor ads. They use the same words a buyer would. "Ищу клининг" (looking for cleaning) and a cleaning company's ad both contain "клининг".
Job postings. "Требуется мастер" (master needed) matches every keyword a repair service has.
Reposts and cross-promotion. Same text, many chats, multiplied by the number of chats you watch.
Discussion without intent. People talk about renovation constantly. Almost none of them are hiring a contractor right now.

A keyword is a subject signal. It carries no intent signal at all. "ищу риелтора"
(looking for a realtor) and "ищу приключений" (looking for adventure) share a token and mean
nothing alike.

The architecture that actually works

Telegram open chats
        │
        ▼
 [1] keyword / stop-word prefilter   ← cheap, high recall, ~97% false positives
        │
        ▼
 [2] LLM intent classifier           ← "is this a real request to buy?"
        │
        ├─ rejected → log
        └─ passed   → lead (hot / warm / cold) + draft reply

Stage 1 must stay broad. Every attempt we made to encode intent into the keyword list itself
made recall collapse: you start losing real requests because people phrase them in ways you did
not predict. Keep it dumb and permissive, let stage 2 do the thinking.

Stage 2 is where the product lives. Our classifier returns three classes:

hot — explicit, ready now: "ищу клининг после ремонта, когда можете подъехать?"
warm — asking for advice, no deadline or budget yet
cold — topic-adjacent, low intent

Noise rate varies wildly by niche

This surprised us. Real data from client accounts, 60-day window, anonymised to niche + region:

Niche	Region	Chats	Leads	Days	Hot	Noise
Price monitoring SaaS	Russia	448	402	40	65	67.3%
White imports from China	Russia	482	1,174	34	525	80.0%
Product certification	Russia	165	751	57	262	87.3%
Apartment renovation	Moscow reg.	49	161	11	44	88.5%
Debt relief lawyers	Moscow reg.	811	174	24	56	92.0%
Marketplace fulfilment	Russia	190	415	39	98	93.1%
Realtor	Blagoveshchensk	189	166	46	33	95.4%
Cleaning company	Moscow	272	201	19	159	97.3%

The pattern: the more common the vocabulary, the worse the noise. "Уборка" (cleaning) appears
in chats constantly and almost never means "I want to order cleaning". Meanwhile a narrow B2B term
like "репрайсер" (repricer) is almost self-filtering.

Practical consequence: niche determines your infrastructure cost. A cleaning client needs ~36x more
classifier calls per delivered lead than a narrow B2B client.

A measurement trap that will fool you

When a new account connects, the system scans chat history. That first day produces a huge
batch of leads at once.

In one account, 417 of 1,174 leads arrived on day one. If you compute "leads per day" naively,
you report 34.5/day when the real steady-state is much lower. Two of our accounts had all their
leads on a single day — pure backfill, zero ongoing flow.

Always exclude the first active day before computing rate, and drop accounts with a single
active day entirely. We nearly published inflated numbers because of this.

Stop-words carry more weight than keywords

Generic set that applies to nearly any niche (Russian, but the categories translate):

# hiring, not buying
вакансия, ищем сотрудника, требуется, резюме, зарплата, оклад, на работу

# someone selling, not buying
предлагаю услуги, оказываю услуги, наша компания, обращайтесь, прайс,
портфолио, скидка, акция, подписывайтесь, реклама, промокод

# channel noise
переслано, репост, кто знает чат, админ, правила группы

Niche-specific additions matter more. For cleaning: уборщица, пылесос купить,
генеральная уборка сама.

Speed beats price

Lead temperature decays fast. In a 500-flat residential chat, a cleaning request collects several
competing replies within the hour. The first clear answer with a price and a time slot wins far
more often than the cheapest one sent next morning.

This changes the product requirement: it is not enough to find the lead, you have to deliver it
while it is still warm, and ideally hand over a draft reply.

Full dataset and per-niche breakdown:
mpleads.ru/kejsy-po-nisham

The service itself is Lidolovets — commercial, Russian market, 5-day free
trial. The documentation and stop-word recipes are public on
GitHub.

Happy to answer questions about the classifier setup in the comments.