Ai Outreach Personalizer

AI outreach personalizer generates personalized cold emails at scale using your own OpenAI or Anthropic API key. Built for sales teams, agencies, and growth marketers who want AI-written email copy without paying per-seat SaaS fees. Bring your own key, pay $0.01/lead compute, and keep 100% control over your LLM costs.

Feed in enriched lead data — name, title, company, industry, tech stack, buying signals — and get back personalized subject lines, opening lines, email bodies, and CTAs for each contact. The actor processes leads in parallel batches of 10 with automatic rate limiting, retries failed LLM calls up to 3 times, and tracks token usage per lead so you can audit your AI spend.

What data can you extract?

Data Point	Source	Example
Subject line	LLM-generated from lead context	"Quick question about Acme's React migration"
Opening line	LLM-generated, personalized to role/company	"I noticed Acme Corp recently adopted React..."
Email body	LLM-generated, references signals and context	3-4 sentence value-focused pitch
Call to action	LLM-generated with specific ask	"Would 15 minutes this week make sense?"
Tokens used	OpenAI/Anthropic usage API	342
Model used	User configuration	gpt-4o-mini
Generation timestamp	Actor runtime	2026-03-25T10:30:00.000Z
Error details	LLM failure handling	null (or error message if failed)
Original lead fields	Pass-through from input	All input fields preserved in output
Run summary	Batch completion stats	48 succeeded, 2 failed, 15,230 tokens

Why use AI Outreach Personalizer?

Writing personalized cold emails by hand takes 5-10 minutes per lead. At 200 leads, that is 16-33 hours of copywriting. Generic templates get ignored — reply rates sit below 2%. AI personalization tools like Clay, Instantly, or Smartlead charge $49-299/month in platform fees, and many add markup on top of your AI API costs.

This actor eliminates the middleman. Your API key goes directly to OpenAI or Anthropic — zero AI markup, zero per-seat pricing. You pay $0.01/lead for Apify compute and whatever your LLM provider charges (roughly $0.001-0.01/email with gpt-4o-mini). That means 1,000 personalized emails cost about $10-20 total, compared to $99-299/month with competing platforms.

• Scheduling — run daily or weekly to personalize new leads as they enter your pipeline
• API access — trigger runs from Python, JavaScript, or any HTTP client to integrate with your CRM workflow
• Budget controls — set a maximum spending limit per run so the actor stops before exceeding your budget
• Monitoring — get Slack or email alerts when runs fail or produce unexpected results
• Integrations — connect to Zapier, Make, Google Sheets, HubSpot, or webhooks for end-to-end automation

Features

• Bring your own key (BYOK) — use your existing OpenAI or Anthropic API key with zero markup on LLM costs
• Dual provider support — switch between OpenAI (gpt-4o-mini, gpt-4o, gpt-4-turbo) and Anthropic (Claude 3 Haiku, Claude 3.5 Sonnet) with one setting
• 4 output fields per lead — generates subject line, opening line, email body, and CTA individually, or select only the fields you need to save tokens
• Custom outreach templates — provide your own system prompt describing your product, value prop, and style to guide the LLM
• 4 tone presets — professional, casual, friendly, or bold/direct to match your brand voice
• Multi-language output — generate outreach copy in English, Spanish, German, French, or any language the LLM supports
• 10-lead parallel processing — batches of 10 concurrent LLM calls with 200ms inter-batch delays for rate limit compliance
• Automatic retry with exponential backoff — retries failed LLM calls up to 3 times with 1-8 second delays, handles 429 rate limits separately with 2-16 second waits
• Structured JSON output — uses OpenAI JSON mode for reliable parsing, strips markdown code fences as fallback
• Token tracking per lead — logs input + output tokens for every LLM call so you can audit costs against your API dashboard
• Graceful error handling — failed leads get null personalization fields with an error message, never crash the batch
• Spending limit enforcement — stops processing when your Apify budget limit is reached, reports partial results
• Pass-through lead data — all original lead fields (email, LinkedIn URL, company domain) are preserved in the output alongside personalization
• Run summary record — final dataset row includes total leads, success/failure counts, total tokens, and whether the spending limit was hit

Use cases for AI email personalization

Sales prospecting at scale

SDRs and BDRs building outbound sequences need personalized first touches for hundreds of prospects weekly. Feed enriched leads from LinkedIn, Apollo, or your CRM into this actor and get ready-to-send email copy that references each prospect's specific role, company size, and recent activity.

Marketing agency lead generation

Agencies managing outreach for multiple clients can set up different templates per client, run batches overnight, and deliver personalized email sequences each morning. At $0.01/lead compute, running 500 leads costs $5 plus LLM fees — no monthly platform subscription required.

Recruiting and talent sourcing

Recruiters reaching out to passive candidates need messages that demonstrate genuine interest in the candidate's background. Provide the candidate's current role, company, and tech stack as lead context, and the actor generates personalized InMail or email openers.

Startup founder outreach

Founders doing their own sales can personalize investor pitches, partnership proposals, or customer development emails. Provide company context and recent signals (funding rounds, product launches, job postings) to generate contextually relevant outreach.

Data enrichment pipeline integration

Combine with other Apify actors: scrape leads with Google Maps Email Extractor, enrich with Waterfall Contact Enrichment, then personalize outreach with this actor. Build a complete lead-to-email pipeline without leaving the platform.

How to personalize cold emails with AI

1. Prepare your lead data — Create a JSON array of lead objects. Each lead should include at minimum a name and company. Add title, industry, tech stack, and recent signals for better personalization. You can export leads from your CRM, a spreadsheet, or pipe output from another Apify actor.

2. Enter your API key and configure settings — Paste your OpenAI or Anthropic API key (stored encrypted, never logged). Choose your model (gpt-4o-mini is cheapest and works well for email copy). Optionally write a custom template describing your product and outreach style.

3. Run the actor — Click "Start" and wait. Processing 100 leads takes roughly 2-4 minutes depending on model speed and rate limits. Watch the status bar for batch progress.

4. Download personalized emails — Go to the Dataset tab and download results as JSON, CSV, or Excel. Each row contains your original lead data plus the personalized subject line, opening, body, and CTA.

Input parameters

Parameter	Type	Required	Default	Description
leads	array	Yes	—	Array of enriched lead objects with fields like firstName, lastName, title, company, industry, techStack, recentSignals
apiKey	string	Yes	—	Your OpenAI or Anthropic API key (encrypted in transit, never stored or logged)
provider	string	No	openai	LLM provider: openai or anthropic
model	string	No	gpt-4o-mini	Model name. OpenAI: gpt-4o-mini, gpt-4o, gpt-4-turbo. Anthropic: claude-3-haiku-20240307, claude-3-5-sonnet-20241022
template	string	No	Generic cold email template	System-level instructions describing your product, value prop, and email style
tone	string	No	professional	Tone preset: professional, casual, friendly, or bold
outputFields	array	No	["subject","opening","body","cta"]	Which personalization fields to generate. Omit fields you do not need to save tokens
maxLeads	integer	No	0 (all)	Maximum number of leads to personalize. 0 means process all leads in the array
language	string	No	english	Output language for generated copy (english, spanish, german, french, etc.)

Input examples

Standard sales outreach — 3 leads with full context:

{
    "leads": [
        {
            "firstName": "Sarah",
            "lastName": "Chen",
            "title": "VP of Engineering",
            "company": "Acme Corp",
            "industry": "Software / SaaS",
            "employeeCount": 250,
            "companyDescription": "B2B project management platform serving mid-market companies",
            "techStack": ["React", "Node.js", "PostgreSQL", "AWS"],
            "recentSignals": ["Recently adopted React for frontend migration", "Raised Series B in Jan 2026"],
            "email": "sarah.chen@acmecorp.com"
        },
        {
            "firstName": "Marcus",
            "lastName": "Rivera",
            "title": "Head of Sales",
            "company": "GrowthFlow Inc",
            "industry": "Sales Enablement",
            "employeeCount": 80,
            "recentSignals": ["Hired 5 SDRs in Q1 2026", "Job posting for Sales Operations Manager"],
            "email": "m.rivera@growthflow.io"
        }
    ],
    "apiKey": "sk-your-openai-key-here",
    "provider": "openai",
    "model": "gpt-4o-mini",
    "tone": "professional"
}

Anthropic with custom template and bold tone:

{
    "leads": [
        {
            "fullName": "Priya Kapoor",
            "title": "CTO",
            "company": "DataBridge Analytics",
            "industry": "Data & Analytics",
            "techStack": ["Python", "Kafka", "Spark", "Snowflake"],
            "fundingStage": "Seed"
        }
    ],
    "apiKey": "sk-ant-your-anthropic-key-here",
    "provider": "anthropic",
    "model": "claude-3-5-sonnet-20241022",
    "template": "You sell a real-time data observability platform. Your target buyer is technical leaders at data-heavy startups. Lead with a specific pain point about data pipeline reliability. Keep it under 100 words total.",
    "tone": "bold"
}

Subject lines only — save tokens for A/B testing:

{
    "leads": [
        { "firstName": "Tom", "title": "CMO", "company": "BrightPath Media", "industry": "Digital Marketing" },
        { "firstName": "Aisha", "title": "Director of Ops", "company": "LogiCore Systems", "industry": "Logistics" }
    ],
    "apiKey": "sk-your-key-here",
    "outputFields": ["subject", "opening"],
    "maxLeads": 50
}

Input tips

• Start with gpt-4o-mini — it produces high-quality email copy at roughly $0.001/email and is 10x cheaper than gpt-4o. Upgrade models only if output quality does not meet your standards.
• Write a specific template — the default generic template works, but a custom template describing your product and ideal customer profile produces noticeably better personalization.
• Include recent signals — the recentSignals array is the single biggest driver of personalization quality. Funding rounds, new hires, product launches, and job postings give the LLM concrete hooks to reference.
• Omit fields you do not need — if you only need subject lines for A/B testing, set outputFields to ["subject"] and cut token usage by 60-70%.
• Batch in one run — processing 200 leads in one run is faster and cheaper than 200 single-lead runs due to Apify startup overhead.

Output example

{
    "firstName": "Sarah",
    "lastName": "Chen",
    "title": "VP of Engineering",
    "company": "Acme Corp",
    "industry": "Software / SaaS",
    "employeeCount": 250,
    "companyDescription": "B2B project management platform serving mid-market companies",
    "techStack": ["React", "Node.js", "PostgreSQL", "AWS"],
    "recentSignals": ["Recently adopted React for frontend migration", "Raised Series B in Jan 2026"],
    "email": "sarah.chen@acmecorp.com",
    "personalization": {
        "subject": "Quick question about Acme's React migration",
        "opening": "I noticed Acme Corp recently moved to React for the frontend — we've helped 50+ engineering teams cut migration timelines by 40% while keeping velocity on feature work.",
        "body": "Most teams we work with hit the same wall around month 3: test coverage gaps slow everything down, and the old jQuery code keeps creeping back in. Our migration toolkit handles incremental adoption so your team ships features during the transition, not after. We reduced DataSync's migration from 9 months to 5 with zero production incidents.",
        "cta": "Would a 15-minute call this week make sense to see if we could help compress Acme's migration timeline?",
        "tokensUsed": 342,
        "model": "gpt-4o-mini",
        "generatedAt": "2026-03-25T10:30:00.000Z",
        "error": null
    }
}

Output fields

Field	Type	Description
firstName	string	Lead's first name (pass-through from input)
lastName	string	Lead's last name (pass-through from input)
fullName	string	Lead's full name (pass-through from input)
title	string	Lead's job title (pass-through from input)
email	string	Lead's email address (pass-through from input)
company	string	Lead's company name (pass-through from input)
industry	string	Company industry (pass-through from input)
personalization.subject	string	AI-generated email subject line (under 8 words)
personalization.opening	string	AI-generated personalized opening line (1-2 sentences)
personalization.body	string	AI-generated email body (3-4 sentences)
personalization.cta	string	AI-generated call to action
personalization.tokensUsed	number	Total tokens consumed for this lead (input + output)
personalization.model	string	LLM model used for generation
personalization.generatedAt	string	ISO 8601 timestamp of when the copy was generated
personalization.error	string	Error message if personalization failed, null otherwise

The final dataset row is a run summary with fields: type ("summary"), totalLeads, processed, succeeded, failed, totalTokensUsed, provider, model, tone, outputFields, spendingLimitReached, completedAt.

How much does it cost to personalize cold emails?

AI Outreach Personalizer uses pay-per-event pricing — you pay $0.01 per personalized email. This is Apify compute only. You pay your LLM provider separately at their standard rates (roughly $0.001-0.01/email with gpt-4o-mini).

Scenario	Leads	Apify cost	Estimated LLM cost	Total cost
Quick test	5	$0.05	~$0.005	~$0.06
Small batch	50	$0.50	~$0.05	~$0.55
Weekly campaign	200	$2.00	~$0.20	~$2.20
Large batch	1,000	$10.00	~$1.00	~$11.00
Enterprise	5,000	$50.00	~$5.00	~$55.00

You can set a maximum spending limit per run to control costs. The actor stops processing when your budget is reached and outputs all leads completed so far.

Compare this to Clay at $149-720/month (with AI credits that run out), Instantly at $77/month, or Smartlead at $79/month. With AI Outreach Personalizer, most sales teams spend $10-50/month with no subscription, no seat limits, and no AI markup.

Personalize cold emails using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/ai-outreach-personalizer").call(run_input={
    "leads": [
        {
            "firstName": "Sarah",
            "lastName": "Chen",
            "title": "VP of Engineering",
            "company": "Acme Corp",
            "industry": "Software / SaaS",
            "techStack": ["React", "Node.js", "AWS"],
            "recentSignals": ["Raised Series B in Jan 2026"],
            "email": "sarah.chen@acmecorp.com"
        }
    ],
    "apiKey": "sk-your-openai-key",
    "provider": "openai",
    "model": "gpt-4o-mini",
    "tone": "professional"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("personalization"):
        print(f"{item['firstName']} {item['lastName']} @ {item['company']}")
        print(f"  Subject: {item['personalization']['subject']}")
        print(f"  Opening: {item['personalization']['opening']}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/ai-outreach-personalizer").call({
    leads: [
        {
            firstName: "Sarah",
            lastName: "Chen",
            title: "VP of Engineering",
            company: "Acme Corp",
            industry: "Software / SaaS",
            techStack: ["React", "Node.js", "AWS"],
            recentSignals: ["Raised Series B in Jan 2026"],
            email: "sarah.chen@acmecorp.com"
        }
    ],
    apiKey: "sk-your-openai-key",
    provider: "openai",
    model: "gpt-4o-mini",
    tone: "professional"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    if (item.personalization) {
        console.log(`${item.firstName} ${item.lastName} @ ${item.company}`);
        console.log(`  Subject: ${item.personalization.subject}`);
        console.log(`  Opening: ${item.personalization.opening}`);
    }
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~ai-outreach-personalizer/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "leads": [{"firstName":"Sarah","lastName":"Chen","title":"VP of Engineering","company":"Acme Corp","email":"sarah.chen@acmecorp.com"}],
    "apiKey": "sk-your-openai-key",
    "provider": "openai",
    "model": "gpt-4o-mini"
  }'

# Fetch results (replace DATASET_ID from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

How AI Outreach Personalizer works

Lead context assembly

For each lead, the actor builds a structured context string from all available fields: name, job title, company name, industry, employee count, location, company description, funding stage, revenue, tech stack, and recent signals. Fields that are null or missing are silently skipped. This context string becomes the foundation of the LLM prompt.

LLM prompt construction

The actor constructs a two-part prompt. The system prompt combines the user's template (or the built-in B2B copywriter template) with the selected tone and language, plus strict JSON output formatting instructions. The user prompt includes the assembled lead context and specifies which output fields to generate. OpenAI calls use response_format: { type: "json_object" } for structured output. Anthropic calls use the standard messages API with explicit JSON formatting instructions.

Batch processing with bounded concurrency

Leads are processed in parallel batches of 10 (CONCURRENCY = 10) to maximize throughput while respecting API rate limits. A 200ms delay separates each batch. Each LLM call has a 60-second timeout. If a call fails, the actor retries up to 3 times with exponential backoff (1s, 2s, 4s). Rate limit responses (HTTP 429) get longer backoff delays (2s, 4s, 8s). If all retries fail, the lead is pushed to the dataset with null personalization fields and an error message — the batch continues processing.

Output assembly and charging

After each successful LLM response, the actor parses the JSON output (stripping markdown code fences if present), merges the personalization object with the original lead data, and pushes the combined record to the Apify dataset. In pay-per-event mode, the actor charges $0.01 per successfully processed lead and checks if the spending limit has been reached. The final dataset record is a summary containing total leads processed, success/failure counts, total tokens used, and whether the spending limit was hit.

Tips for best results

1. Provide company descriptions, not just names. The LLM cannot look up what "Acme Corp" does. A one-sentence company description dramatically improves output quality.

2. Add 2-3 recent signals per lead. Funding rounds, new hires, product launches, and job postings are the highest-value context for personalization. These become the specific hooks the LLM references in the opening line.

3. Write a product-specific template. Replace the default template with instructions that describe your product, your ideal customer, and what a good email looks like. The more specific your template, the more relevant the output.

4. Use gpt-4o-mini for volume, gpt-4o for quality. gpt-4o-mini handles email personalization well at 10x lower cost. Reserve gpt-4o or Claude 3.5 Sonnet for high-value accounts where every word matters.

5. Test with 5 leads before running thousands. Run a small batch first, review the output, and tune your template before scaling up.

6. Combine with lead enrichment actors. Raw CRM exports often lack the context needed for good personalization. Run leads through Waterfall Contact Enrichment or Website Contact Scraper first.

7. Generate subject lines separately for A/B testing. Set outputFields to ["subject"] and run the same leads multiple times with different tones to generate subject line variants for testing.

Combine with other Apify actors

Actor	How to combine
Website Contact Scraper	Scrape emails and contacts from company websites, then feed into AI Outreach Personalizer for personalized cold emails
Google Maps Email Extractor	Extract local business leads with emails from Google Maps, then personalize outreach for each business
Waterfall Contact Enrichment	Enrich raw leads with company data, tech stack, and signals before personalizing outreach
Email Pattern Finder	Discover company email patterns to guess prospect emails, then personalize messages for each contact
Bulk Email Verifier	Verify email addresses after personalization, before sending — remove bounces from your outreach list
B2B Lead Gen Suite	Run the full pipeline: find websites, extract contacts, score leads, then personalize outreach for top-scored prospects
Website Tech Stack Detector	Detect prospect tech stacks to add as lead context — the LLM references specific technologies in outreach copy

Limitations

• No built-in lead sourcing — this actor personalizes outreach for leads you already have. You need to provide enriched lead data as input. Use Google Maps Email Extractor or Website Contact Scraper to source leads.
• No email sending — this actor generates email copy only. You need a separate tool (Instantly, Smartlead, Lemlist, or your email client) to send the messages.
• LLM quality varies by context — leads with minimal data (just name and company) produce generic output. Provide company descriptions, tech stacks, and recent signals for best results.
• API key required — you must have an active OpenAI or Anthropic API key with sufficient credits. The actor does not include LLM credits.
• Rate limits depend on your API plan — the actor processes 10 leads concurrently. If your OpenAI/Anthropic account has low rate limits, some calls may retry or fail. The actor handles this automatically with exponential backoff.
• No real-time web research — the LLM generates copy based on the lead context you provide. It does not browse the web or look up company information during generation.
• JSON parsing edge cases — the actor strips markdown code fences and parses JSON from LLM responses. Occasionally an LLM response may not parse correctly, in which case the lead is flagged with an error and the batch continues.
• Token limits — each lead's LLM call is capped at 1,024 output tokens. This is sufficient for email copy but would truncate longer content types.

Integrations

• Zapier — trigger personalization runs when new leads enter your CRM or spreadsheet, push results to your email sequencer
• Make — build multi-step workflows: scrape leads, enrich, personalize, then load into Outreach or Salesloft
• Google Sheets — export personalized emails directly to a Google Sheet for review before sending
• Apify API — trigger runs programmatically from your sales ops platform or custom CRM integration
• Webhooks — get notified when personalization completes, auto-download results to your email tool
• LangChain / LlamaIndex — use personalized output as training data or context for AI sales agents

Troubleshooting

• "apiKey is required" error — make sure your OpenAI or Anthropic API key is entered in the LLM API Key field. The key must be a non-empty string. Double-check for leading/trailing spaces.

• All leads return errors with "HTTP 401" — your API key is invalid or expired. Verify it works by making a test call directly to the OpenAI or Anthropic API. For OpenAI, keys start with sk-. For Anthropic, keys start with sk-ant-.

• Rate limit errors on large batches — the actor handles 429 responses automatically with exponential backoff, but very large batches (1,000+ leads) on low-tier API accounts may hit persistent limits. Reduce batch size with maxLeads or upgrade your OpenAI/Anthropic plan.

• Generic/low-quality personalization output — the most common cause is sparse lead data. Add companyDescription, techStack, and recentSignals fields to your leads. Also write a custom template that describes your product and outreach style.

• Run stopped early with "spending limit reached" — the actor respects your Apify spending limit. All leads processed before the limit are in the dataset. Increase your budget or reduce the lead count for the next run.

Responsible use

• This actor generates outreach copy using your own LLM API key and the lead data you provide.
• Respect anti-spam laws (CAN-SPAM, GDPR, CASL) when sending emails generated by this actor.
• Do not use generated content for spam, deception, impersonation, or harassment.
• Include unsubscribe links and sender identification in all outbound emails as required by law.
• For guidance on email outreach compliance, see Apify's guide.

FAQ

How many leads can I personalize in one run? There is no hard limit. The actor processes leads in batches of 10 with automatic rate limiting. Practical limits depend on your LLM provider's rate limits and your Apify spending budget. Most users run batches of 50-500 leads without issues.

How much does AI email personalization cost per lead? Apify charges $0.01/lead for compute. Your LLM provider charges separately: roughly $0.001/email with gpt-4o-mini, $0.01/email with gpt-4o, or $0.003/email with Claude 3 Haiku. Total cost per lead is typically $0.011-0.02.

Can I use my own OpenAI or Anthropic API key? Yes — that is the core design. Your API key goes directly to OpenAI or Anthropic via their official API endpoints. The actor never stores, logs, or proxies your key. It is encrypted in transit using Apify's isSecret field handling.

How is AI Outreach Personalizer different from Clay? Clay charges $149-720/month for platform access and uses AI credits (Actions) for BYOK calls. This actor charges $0.01/lead compute with no monthly fee, no seat limits, and zero markup on your LLM costs. You pay OpenAI/Anthropic directly at their published rates.

Can I customize the email tone and style? Yes. Choose from 4 tone presets (professional, casual, friendly, bold) and write a custom template with specific instructions about your product, value proposition, and style preferences. The template is the system prompt sent to the LLM.

Does this actor send emails? No. This actor generates personalized email copy only. You download the output and import it into your email sending tool (Instantly, Smartlead, Lemlist, Outreach, or any SMTP-based tool).

Can I generate personalized emails in languages other than English? Yes. Set the language parameter to any language (spanish, german, french, portuguese, japanese, etc.). The LLM generates all output fields in the specified language.

How long does a typical run take? Processing speed depends on LLM response times. With gpt-4o-mini, expect roughly 50-100 leads per minute. A batch of 200 leads typically completes in 2-4 minutes.

Is it legal to send AI-generated cold emails? AI-generated content is legal to send, but you must comply with anti-spam laws. In the US, CAN-SPAM requires a physical address, an unsubscribe mechanism, and accurate sender information. In the EU, GDPR requires a lawful basis for processing personal data. This actor generates the copy — compliance with sending regulations is your responsibility.

What happens if some leads fail during a run? Failed leads are included in the output with null personalization fields and an error message explaining what went wrong. The batch continues processing remaining leads. The run summary at the end reports how many succeeded and failed.

Can I schedule this actor to run periodically? Yes. Use Apify's built-in scheduling to run daily, weekly, or at custom intervals. This is useful when new leads are added to a dataset on a recurring basis — the actor personalizes each batch as it arrives.

What lead fields produce the best personalization quality? Company description, recent signals, and tech stack have the highest impact on output quality. A lead with just a name and company produces generic copy. A lead with a company description, 2-3 recent signals, and a tech stack produces specific, reference-heavy personalization that feels hand-written.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.