Your cold emails and LinkedIn messages are being ignored. It is not because your subject line is bad or your pitch is weak. It is because your message is irrelevant.
Founders and sales leaders are seeing their reply rates plummet. The reason is simple. Everyone has access to the same databases. Everyone downloads the same lists. Everyone uses the same "personalized" templates that insert a first name and a company name.
When you act like everyone else you get treated like spam.
The top 1% of outbound teams have stopped "spraying and praying." They are building Signal-Based Outbound Engines. They do not reach out just because a lead exists on a list. They reach out because the lead showed a specific signal that they are ready to buy.
Here is how you can use AI agents for outbound sales to target these windows of opportunity using a simple chat interface.
The Problem with Static Lists
The traditional outbound workflow kills your domain reputation and wastes your LinkedIn connection limits.
- You go to a database.
- You filter for "CEO" in "SaaS companies."
- You download 1,000 contacts.
- You launch a generic sequence.
This fails because it relies on static data. You assume that just because a company fits your Ideal Customer Profile (ICP) they are ready to buy. They usually are not. You burn through your total addressable market with messages that land at the wrong time.
Automated account research for sales must go deeper than company size or location. You need to know why they need you right now.
The Solution: Signal-Based Selling
A Signal is a specific event or data point that creates a natural opening for a business conversation.
- Static Data: "They are a B2B software company."
- Signal Data: "They just hired a VP of Sales and installed a new CRM yesterday."
Monitoring these signals used to require a team of human researchers manually checking websites, news, and LinkedIn. Now you can use signal-based selling automation to do this analysis simply by chatting with your AI coworker.
How to Delegate Deep Analysis to Your AI Coworker
The shift here is about agency. You do not need to configure complex settings or manually upload CSVs. With Starnus you interact with the AI just like you would with a smart human employee.
You define your ICP once and then you simply chat with Starnus to execute the workflow.
The Workflow: From Chat to Analysis
Instead of spending hours on LinkedIn Sales Navigator you just give your AI coworker a goal.
You might say:
> "Find 5 companies that match my ICP and analyze them and score them, then find 1-2 leads per company with enriched contact info and analysis and messaging tips."
Or if you want to focus purely on people:
> "Find 10 leads that match your ICP, enrich with contact info, analysis and messaging tips."
That is it. You do not need to code. You do not need to build filters.
What Happens Behind the Scenes?
When you give that command Starnus does not just "search." It deploys agents to perform automated account research for sales. It looks for the signals that matter.
1. Tech Stack Analysis & Scoring The agents visit company websites and analyze their infrastructure. This is tech stack analysis for lead generation on autopilot. If the agents see the company uses specific tools that match your ICP criteria they increase the lead score. They provide reasoning so you know why a company was selected.
2. Hiring & Growth Signals The agents check career pages. This is how to detect hiring signals for sales without reading 50 job descriptions yourself. The AI sees they are hiring for a specific role and analyzes the job description to find the pain points they are trying to solve.
3. Deep Prospect Profiling Once the company is scored the agents find the people. They run deep prospect analysis for cold email and LinkedIn. They analyze the leads' recent posts, social activities, and professional history.
The Result: Hyper-Personalized Messaging Tips
The most valuable output from this workflow is the "Messaging Tip."
Because the AI analyzed the tech stack, the hiring data, and the lead's social activity, it does not just give you an email address. It gives you an angle.
It might tell you:
- "Tip: Mention they are using HubSpot and just hired a VP of Sales. Connect this to their likely need for better reporting."
- "Tip: Reference their recent LinkedIn post about Q3 planning."
This allows you to use hyper-personalized cold outreach tools to send messages that actually make sense.
Execution: Email and LinkedIn on Autopilot
The analysis is only half the battle. You need to deliver the message.
Because Starnus is an end-to-end platform you can tell your AI agent to take these enriched lists, complete with their custom messaging tips, and launch the campaign.
- For Email: The agent drafts emails that reference the specific insights found during analysis.
- For LinkedIn: The agent handles connection requests and follow-ups using the context from the lead's profile.
The New Math of Sales
When you use an AI coworker to execute this level of research your math changes.
- Old Way: Send 1,000 generic emails. Get a 0.5% reply rate. Book 5 meetings.
- Signal Way: Chat with Starnus to find and analyze 1,000 leads. Filter for the high scores. Send hyper-relevant messages via Email and LinkedIn. Get an 8% reply rate. Book 12 meetings.
You send fewer messages. You protect your domain reputation. You book more qualified meetings because you are actually being helpful.
Start Delegating Today
You do not need to do this research manually. You just need to know what to ask. Starnus acts as your AI employee.
- Chat: Ask it to find companies or leads that match your ICP.
- Analyze: Watch it score them based on tech stack and hiring signals.
- Execute: Use the generated messaging tips to launch relevance-based campaigns.
Stop personalizing. Start being relevant.
Ready to hire your AI sales coworker? Starnus handles the research, scoring, and outreach so you can focus on closing deals.