Stop researching prospects manually. Signal Engine scans websites, news, and job postings to find your best-fit companies and generate personalized outreach angles.
From ICP document to qualified prospect with AI-generated outreach — click each step to see what happens under the hood.
01
Signal Generation
ICP PDF → AI-extracted buying signals
→ Upload any ICP document as PDF
→ LLM extracts structured "signals"
→ Grouped into 4 tiers by detection cost
→ Each signal: name, weight, method
→ Output: machine-readable signal config
02
Tiered Scanning
Cheapest-first enrichment pipeline
→ T0: Free XLSX column checks
→ T1: $0.005 website scrape (Firecrawl)
→ T2: $0.01 web search (Tavily)
→ T3: $0.08 deep LLM analysis (Opus)
→ ~60% filtered at each tier
03
Scoring
0–100 ICP score + qualification status
→ Weighted signal match scoring
→ Status: qualified / disqualified / maybe
→ Per-signal confidence (0–1)
→ Evidence summary in plain language
→ Disqualifying signals auto-reject
04
Outreach
Personalized emails from detected signals
→ Signal data becomes email hooks
→ Choose: cold / follow-up / breakup
→ Choose: pain point / social proof / curiosity
→ One-click copy to clipboard
→ Every email references real findings
02 — The Tier System
Cost control baked in.
~60% of companies get filtered at each tier. You don't spend $0.08 on deep research for a company that a free spreadsheet filter would've caught. Click a tier to see what it detects.
Tier
Method
Cost / co.
Typical filter
T0
XLSX Filter
Checks spreadsheet columns directly
Free
Industry, size, location
T1
Website Scrape
Firecrawl · content + tech stack
$0.005
Messaging, tech, hiring
T2
Web Search
Tavily · news, funding, signals
$0.01
Funding, growth, intent
T3
Deep Enrichment
Opus LLM · full synthesis
$0.08
Strategic fit, full score
60%
avg filtered per tier before spending more
$0.08
max per company for full deep enrichment
0–100
ICP score with weighted signal matching
03 — Scoring & Results
Yes, no, maybe — with evidence.
Raw enrichment data is useless without interpretation. Every company gets a score, a status, per-signal confidence, and a plain-language evidence summary you can verify.
Acme SaaS Co.
Qualified
87
Series B · 120 employees · hiring SDRs · recently migrated to AWS · PLG motion detected on homepage
✓ Funding stage match
0.92
✓ Team size 50–200
0.88
~ Stack alignment
0.61
Widgets Inc.
Disqualified
12
On-premise only · no SaaS products · 8 employees · no engineering team signals detected
✗ Cloud infrastructure
0.08
✗ Funding stage match
0.05
✗ Team size 50–200
0.12
"Disqualifying signals auto-reject a company regardless of score. If a required trait is absent, the system stops spending — and tells you exactly why."
Scoring logic
04 — Signal Configs
Signals that get smarter.
After every scan, Signal Engine tracks which signals actually predicted qualification. Detection rate, confidence, and qualification correlation — so you know what to keep and what to cut.
Series A/B Funding
W: 0.9
Company has raised Series A or B in last 24 months — indicates growth mode and budget availability.
87% detect rate78% qual rate
SDR/BDR Hiring
W: 0.8
Active job postings for sales development roles signal investment in outbound pipeline building.
74% detect rate71% qual rate
PLG Messaging
W: 0.7
Homepage or pricing page surfaces product-led growth language — free trial, self-serve, no sales required.
61% detect rate58% qual rate
AWS/GCP Stack
W: 0.6
Cloud-native infrastructure detected via tech stack scrape — indicates modern architecture and integration compatibility.
55% detect rate52% qual rate
50–500 Employees
W: 0.85
Team size in the sweet spot — large enough to have budget, small enough to move fast on purchasing decisions.
92% detect rate81% qual rate
On-Premise Only
Disqualify
No cloud products detected. Company operates exclusively on-premise — hard incompatibility with our integration requirements.
23% detect rateauto-reject
05 — The Pipeline
Qualified leads go somewhere.
Qualifying leads into a CSV is where most tools stop. Signal Engine drops them straight into a 6-stage Kanban CRM with activity timelines, deal values, and AI-generated outreach built in.
📥
New
24 leads
📤
Contacted
18 leads
💬
Replied
11 leads
📅
Meeting
7 leads
🏆
Won
4 leads
×
Lost
3 leads
67
total leads across pipeline
57%
win rate on deals reaching Meeting stage
$124K
total pipeline value tracked
06 — Get Started
ICP in. Pipeline out. Automatically.
Upload your ICP document. Get a signal config, a scored prospect list, and AI-drafted outreach — in one scan.
Signal Engine · B2B lead qualification · built for operators
Free Resource
AI Automation: The Business Owner's Field Guide
10 key insights, core concepts, real workflow examples, and the right tools for automating your service business. Written for operators, not engineers.
What to automate first (and what not to)
How lead funnels actually work under the hood
The exact tool stack we use for clients
Mindset shifts that save you from overbuilding
No spam. We send useful stuff only.
Field Guide
AI Automation for Business Operators
The technology to build a digital assembly line for your business already exists. This guide explains what it is, how it works, and what you actually need to know to use it.
The core idea: Define your inputs and outputs clearly. Let the machine handle everything in between. You don't need to understand every technical detail -- you need to understand your own operations.
What Business Owners Need to Know
Tap each to expand
The real value isn't saving clicks. It's offloading the mental load of evaluating options, routing information, and following up consistently. Every time you manually run a process, your brain loads every possible path before choosing one. That energy compounds into exhaustion. Automation does the evaluation for you -- because you already did the thinking when you built the system.
Automation doesn't fix a broken or undefined workflow. If you can't explain the steps manually, a system can't run them for you. Start by mapping what you already do. If you can walk through it step by step, with clear branches and decisions, it can be built and offloaded.
You don't need to understand what happens in between -- that's the machine's job. But you need to be specific: What data enters the system? What result do you want on the other end? Don't ask for 30 reports you won't read. AI can process everything; the constraint is knowing what you actually need.
A weekly email summarizing new leads in your CRM. A form submission that automatically adds a contact and sends a personalized follow-up. These aren't flashy, but they run every day without you. Small systems compound into large amounts of reclaimed time and mental energy over a year.
You can collect a few answers from a prospect, have AI research them, and automatically send a response tailored to their specific situation. What used to require a dedicated person can now run on its own. The result feels personal to the recipient -- because it is, based on what they told you.
If you're an expert in your field, you can turn that knowledge into an automated funnel. Prospects answer a few questions, AI matches their answers to your best content or recommendations, and you capture their information in the process. You're using AI to automate the selection -- not replace your expertise.
If something always happens the same way, use a workflow. If it requires interpreting context or choosing between options -- like triaging a new lead or responding to a varied inquiry -- that's where an AI agent adds value. Knowing which tool fits which task saves you from building the wrong thing.
CRMs, email platforms, forms, databases, research tools, image generators -- almost anything can be connected to anything else today. The tools exist. The hard part is knowing what you want connected, why, and being specific enough about it that a system can be built to do it reliably.
Build the system, find the gaps, fix them. The goal is a machine that runs cleanly -- not a perfect machine on day one. Every iteration makes it more reliable. Error handling is part of the build, not a sign that something went wrong. Expect to refine it.
Even when a task only takes one path, your brain loads every possible option before ruling them out. A 100-branch process might only ever use one branch -- but you consider 50 before choosing. Multiply that cognitive load across a full work day and it's significant. Automation doesn't just save time. It preserves focus for things that actually need your judgment.
Core Concepts
The building blocks, in plain language
Data Layer
API
A precise, predefined connection between two software systems. You specify exactly what call you're making -- get this data, post this record. Because they're explicit, they're reliable and predictable.
Think of it as: a specific form you fill out to make a specific request. Same form every time, same result every time.
Intelligence Layer
MCP
Model Context Protocol -- what AI agents use to interact with connected tools natively. Instead of one specific call, it opens a range of possible actions. The agent decides which action fits the situation.
Think of it as: giving an employee full access to a system and trusting them to figure out the right action, rather than scripting every click.
Trigger Layer
Webhook
A push notification between platforms -- when something happens somewhere, data is immediately sent somewhere else as a JSON payload. The entry point for most automations.
Think of it as: a form submission that automatically fires a signal to your systems the moment someone hits submit -- no manual checking required.
Process Layer
Workflow
A defined, repeatable sequence. Trigger, then Action, then Action, then Output. Same path every time. Best for structured, predictable processes that don't require interpretation.
Think of it as: a checklist that runs itself. Every step is predetermined. No judgment needed.
Intelligence Layer
AI Agent
An LLM with access to tools and the ability to make decisions. It can interpret varied inputs, choose the right action from its available options, and execute across connected platforms.
Think of it as: a smart employee who has access to all your systems and can figure out what to do based on what they're given -- without needing step-by-step instructions every time.
Language Layer
LLM
Large Language Model -- the AI brain (like Claude, GPT). Exceptional at processing, interpreting, formatting, and generating text. The reasoning engine behind agents and many workflow steps.
Think of it as: the smartest intern you've ever had -- can process any information, draft anything, research anything, but needs direction on what matters to you.
How It Actually Works
A real example: form submission to personalized outreach
01
Someone fills out your form
A prospect submits a contact or inquiry form on your site. This is the trigger -- the event that starts the whole chain.
02
Webhook fires to your automation platform
The form submission immediately sends a data payload -- name, email, answers -- to a tool like Gumloop or Make. This is your entry point.
The platform extracts the relevant fields. From here, you can run parallel tracks -- one route adds them to your CRM, another begins the outreach flow.
04
Option A: Simple personalized email
Name and email go to an email tool (Resend, Gmail). A template pulls in their first name and the specific interest they mentioned. Sent within seconds of their submission.
"Hi Sarah, thanks for your interest in accounting automation. Here's what we do for firms like yours..."
05
Option B: AI-researched, fully tailored outreach
Name, email, and company get passed to an AI agent. Using tools like Perplexity or Exa via MCP, it researches them, then generates a response specific to their situation before sending.
Agent finds Sarah's firm handles 40+ clients, specializes in e-commerce. Email references this specifically.
06
You receive a summary, not the work
A simple report lands in your inbox. New lead added. Outreach sent. Anything that needs your judgment is flagged. Everything else ran without you.
The Tool Stack
What connects to what
Workflow BuilderGumloop
Visual workflow builder and agent platform. Good for connecting systems without deep coding knowledge.
Database / CRMAirtable
Flexible database that works as a CRM. Easy to connect to automations via API.
Email SendingResend
Programmatic email sending via API. Clean, reliable for automated outreach and notifications.
Research ToolPerplexity / Exa
AI-powered search and research. Agents use these via MCP to research leads or gather market data.
Web ScrapingFirecrawl
Scrapes websites at scale. Useful for competitive research, content gap analysis, SEO data.
AI BuilderClaude Code
LLM-powered coding tool for building custom internal software. Good for one-off tools tailored to your exact process.
Landing PagesFramer
Fast, design-quality landing page builder. Quick to spin up funnels and lead capture pages.
Image GenerationGoogle ImageFX
AI image generation for ad creatives, landing page visuals, and content assets.
WorkspaceNotion
Documentation and knowledge base. Can serve as a lightweight internal tool or client-facing resource.
The Knowledge Funnel
Turning expertise into qualified leads -- click each stage
You have expertise. Prospects want specific information they can't easily find elsewhere. The knowledge funnel connects these two things -- and captures what you need to convert them in the process.
Why they do it: They're getting something specific in return. Not a generic newsletter -- information tailored to their answers. The specificity of the promise is what gets them to fill it out.
You've already done the hard work: building the knowledge base from your expertise, defining what good answers look like. The agent just does the matching -- fast and at scale. It's not replacing your expertise. It's automating the selection.
The personalization isn't superficial. It's based on what they actually told you. People know when they're getting something generic. When the response reflects their specific situation, they notice -- and they're more likely to take the next step.
Their answers tell you what matters to them, what stage they're at, and how to position your offer. Your follow-up can reference this directly. Instead of a cold pitch, you're continuing a conversation they already started.
The Right Mindset
How to think about this before building anything
"Ford took every process of manufacturing a car and systematized it so it ran on its own. He couldn't do that with his accounting. Now you can -- digitally, for the back end of your entire business."
Define your assembly line before you build it. Know every step of your process. The clearer your manual process, the better your automated one will be. Vague in, vague out.
Complexity is fine. Ambiguity is not. Your process can have 100 branches. That's okay. What isn't okay is not knowing which branches exist. A complex but clearly defined process can be automated. An undefined one can't.
Start with what you already do manually. Don't try to automate something you haven't done yet. Pick one process you run regularly, map it out, and build that. Get one system running cleanly before adding another.
Build in error handling from the start. Assume things will break. Add notifications when they do. An automation that fails silently is worse than no automation. Know when your system needs your attention.
The goal is to stop thinking about things that should think for themselves. Every time you save a future version of yourself from having to load a process into working memory, you've created real leverage. That's what this is for.
