You Can't Find Russian Founders on LinkedIn. Here's Where They Actually Are

You've got a target list of Russian companies you want to sell into. You open LinkedIn to find the founder or CEO. Half the profiles haven't been touched since 2021. The other half belong to people who left the company years ago. LinkedIn's penetration in Russia is nowhere near what it is in the US or Europe — Sales Navigator is basically useless there.

So how do you actually find the person who signs the check?

How do you find decision-maker contacts in Russia without LinkedIn?

You use a six-step workflow that chains together a business registry provider, a contact-lookup tool, and an AI assistant that orchestrates both. Instead of searching a professional network that barely exists in the region, you pull companies straight from registry data (filtered by industry code and revenue), extract the founder's name and phone number, then convert that phone number into a Telegram username you can message directly. The whole thing can be automated by an AI assistant like Claude or ChatGPT once it's connected to both data sources via API.

This works because Russia and the CIS never consolidated around one professional network. Decision-maker data lives scattered across corporate registries, phone databases, and — critically — Telegram, which is where most business communication actually happens.

Why doesn't LinkedIn work for prospecting in Russia?

LinkedIn has been restricted in Russia since 2016, and most active business communication moved to Telegram years ago. You won't find reliable job-title data, you won't find recent activity, and cold InMail requests mostly sit unread. If your entire prospecting motion depends on LinkedIn's graph, you're working from a dataset that's structurally incomplete for this region — not just thin.

That's also why cold email is dying for B2B sales in Russia too — the inboxes people check are Telegram, not Gmail or Outlook.

What's the actual step-by-step workflow?

Here's the sequence, using DataNewton as the registry provider and CRMChat's contact-lookup tools as the phone-to-Telegram bridge:

1. Get access to a business registry provider. Use a service like DataNewton to filter companies by OKVED industry code and revenue range. Grab an API key from their developer docs.

2. Connect your AI assistant to DataNewton. Give Claude or ChatGPT your DataNewton API key so it can query the company list on your behalf.

3. Connect your AI assistant to CRMChat. Find your CRMChat API key under Settings → API Keys, and feed it to the assistant so it can drive CRMChat's contact-lookup tools programmatically. Full details are in the CRMChat Help Center.

4. Instruct the AI to find the decision-maker for each company. The assistant runs a search sequence through CRMChat's contact-lookup bot to identify the founder or listed executive behind each registry match.

5. Retrieve the founder's phone number and convert it to a Telegram username. This is the step that actually solves the "no LinkedIn" problem — you go from a company name to a person you can message on the platform they check daily.

6. Export the results into a ready-to-use CSV for outreach. Structured, filtered, and attached to a real name and a live Telegram handle.

CRMChat automates the phone-to-Telegram lookup step through the @crmchatcontactbot contact-lookup tool, orchestrated via API by your AI assistant — turning a list of company registrations into verified Telegram contacts without you touching a single profile manually. If you want the full walkthrough with screenshots, it's in the help center guide on finding decision-makers in Russia/CIS.

Why filter by industry code and revenue instead of just scraping everything?

Because a raw company list without qualification criteria wastes your outreach budget on prospects who can't buy. Filtering by OKVED code narrows you to companies actually operating in your target vertical, and filtering by revenue range acts as a rough proxy for deal size — you're not messaging a five-person shop the same way you'd message a company doing $50M a year. That qualification happens before a single message goes out, which is the whole point.

What do you do once you have the Telegram usernames?

A list of verified contacts is only useful if it turns into pipeline. Once you've got your CSV:

• Import the list into your outreach sequence tool — CSV imports are the standard path for cold outreach lists where only replies convert into CRM leads.

• Segment by industry and revenue band so your first message actually references something specific about their business, not a generic pitch.

• Warm up your sending account first. A fresh Telegram account blasting 100 cold messages a day gets flagged fast — check out Telegram account warmup before you run volume.

• Set up auto-follow-ups for the contacts who open but don't reply on message one — most decision-makers respond on the second or third touch, not the first.

• Route replies straight into your CRM so a warm lead doesn't sit in a chat thread waiting for someone to notice it.

CRMChat is built specifically for this last part — it's a Telegram-native CRM that turns replies from your outreach sequences into structured leads automatically, so nothing you find through this workflow gets lost in a DM thread. For teams running high-volume campaigns, this is also where automated follow-up sequences and syncing Telegram folders into your sales CRM start paying off.

Is group parsing a substitute for this workflow?

No — treat it as a secondary tactic, not your primary source. Parsing public Telegram groups can surface engaged members and warm leads for broader audience-building, and it's genuinely useful for community-driven sales motions. But it targets whoever happens to be active in a group, not the specific founder or CIO you're trying to reach. If your goal is a named decision-maker at a specific company, the registry-to-phone-to-Telegram workflow gets you there directly; group parsing is what you layer on top once you're also trying to reach a wider audience beyond your named target list.

One B2B growth agency, LeadBridge, generated 90 qualified SQLs across three enterprise campaigns using Telegram as their primary channel alongside email and LinkedIn — with Telegram driving 60-80% of all leads. You can read the full case study here.

What if you're prospecting Web3 companies instead of traditional B2B?

The registry approach works for companies with formal legal filings, but Web3 teams often don't have that paper trail. For that audience, CRMChat maintains a Web3 decision-makers database with 7,000+ verified Telegram contacts pulled from real attendees at conferences like Token2049, Devconnect, and Korea Blockchain Week — organized by role and niche, delivered as CSV or Google Sheets, ready to drop into an outreach sequence.

Key Takeaways

• LinkedIn's data on Russian and CIS decision-makers is thin and stale — don't build your prospecting motion around it.

• The reliable path is registry data → founder phone number → phone-to-Telegram username, orchestrated by an AI assistant connected to DataNewton and CRMChat.

• Filter companies by industry code and revenue before you look up contacts, not after — it's cheaper to qualify early.

• Group parsing is a useful secondary tactic for broad audience-building, not a substitute for targeting named decision-makers.

• Once you have the contacts, route them through a Telegram-native CRM so replies become pipeline instead of getting lost in DMs.