Matt Lucero of Anevo shows a useful split in his GPT-6 Astra and Jev tutorial: let a general-purpose agent help define and assemble a B2B search, then use a cheaper decision model for repeated, narrow checks. The interesting question is not whether AI can return thousands of names. It is whether a sales team can identify the right companies and roles without paying to contact, research, and annoy the wrong people.
Matt uses Apollo for the contact list, GPT-6 Astra in Codex to help set search criteria and coordinate the workflow, Spider to read company websites, and Jev through OpenRouter to decide whether each company and job title fit his brief. He reports roughly $4 in retrieval and classification charges for a run involving nearly 10,000 records. That is a creator-reported processing cost, not the price of acquiring a usable list or running a compliant campaign.
Watch Matt's Workflow
Credit: The lead-generation method, screenshots, counts, costs, and agency results are Matt Lucero's creator-reported demonstration, not an independent audit. Matt runs Anevo and offers lead-generation services and training linked from the video. Our terms, measurement, and outreach safeguards below are editorial additions.
What Each Tool Actually Does
- Apollo: finds and exports contacts matching search filters. Its export guide explains that credits can apply, depending on the plan and whether contacts were previously accessed.
- GPT-6 Astra / Codex: asks for a clearer ideal customer profile, helps configure the search, and assembles a workflow. OpenAI documents computer use as UI operation, not a guarantee that every third-party site permits automated extraction.
- Spider: retrieves company-site text so the classifier can judge what a business does. Its current pricing page bills by bandwidth and compute, so there is no universal one-price-per-site guarantee.
- Jev: returns typed choices and probabilities for predefined questions. TypeSafe's documentation says it accepts text and makes structured decisions; it does not write outreach copy or discover contacts. OpenRouter is one way to call it.
1. Define the Buyer and Get a Permitted Export
Matt starts at 2:31 by asking the agent to clarify his ideal customer profile before selecting records. His example is specific: US-based marketing agencies with roughly 11-50 employees, and owners, founders, partners, CEOs, or relevant sales leaders. The service he sells is B2B lead generation. Those choices make the later classification questions testable.
The demo shows Astra clicking through Apollo's filters and Matt exporting about 9,300 contacts already saved in his account. He says many had been paid for previously, so they were not newly free to acquire. Also, do not assume the browser automation shown is permitted for your account. Apollo's current terms prohibit bots, crawlers, and automated scraping to extract platform data unless that is expressly included in the platform or approved in writing. Use the platform's permitted export or API route under your agreement, review credit consumption, and ask Apollo about any agent-driven access you intend to automate.
2. Check Company Fit and Role Fit Separately
At 11:47, Matt demonstrates why a keyword search is not enough. A software vendor might mention that it serves marketing agencies without being an agency. A contact titled "product owner" might contain the word "owner" without owning the business. His test retrieves a company's homepage with Spider; Jev rejects the example on both company type and job-title fit.
These should be two separate decisions with explicit labels: agency, not agency, or insufficient evidence; then decision maker, not decision maker, or uncertain. Save the source URL and a short evidence excerpt with each decision. Deduplicate domains before fetching to avoid paying to crawl the same company for every contact. Recheck stale titles and businesses that changed focus. If the homepage is thin or ambiguous, route the record to review rather than inventing certainty.
TypeSafe notes that Jev's probabilities reflect uncertainty across groups of predictions; they do not make an individual prediction guaranteed correct. A displayed 0.85 is not an 85% chance of buying, and Matt's suggested cutoff is an example, not a universal rule. Label a sample yourself, then choose a threshold based on the cost of false positives and missed good prospects.
3. Read the Two Results as Two Results
The opening displays a previously prepared list of 4,691 leads. In the walkthrough, Matt exports a different list of roughly 9,300 contacts. By 19:17, he reports approximately 5,400 qualified, 500 rejected, and 3,000 needing review after processing nearly 10,000 records. These are rounded on-screen figures, and the video does not reconcile the intro list with the later run record by record. Treat them as demonstration counts, not a verified conversion funnel.
The large review group is not a failure to hide. It tells you the label definitions or website evidence need improvement, or that a human needs to inspect marginal cases. Before claiming "qualified," check a random sample from each bucket against the source pages and current roles. Measure precision (how many accepted records really fit) and the false-positive rate. Later, measure reply quality and booked meetings separately; the video does not prove that a model score causes those outcomes.
What the Low Processing Bill Omits
Matt says the live run incurred about $4 for website retrieval and decision-model processing, with Jev around $1 of that. Current OpenRouter pricing lists Jev 1.13 at $0.042 per million input tokens and zero output-token charge; that price can change, and the actual bill depends on input size and calls. Spider's own pricing varies with page bandwidth and compute.
The all-in ledger is larger: Apollo subscription and contact/export credits, Astra or Codex allowance, Spider retrieval, Jev calls, retries and errors, human labeling and review, CRM or sequencer costs, deliverability work, and compliance. Matt's prior Apollo spend is especially important: reusing already saved records makes this run look different from starting with a new account. Compare cost per verified, usable contact, not only cents per model call.
Before a List Becomes an Email Campaign
A qualified record is not permission to send unlimited email. The FTC's CAN-SPAM guide says the US rules cover B2B commercial email too: use truthful headers and subjects, identify the message as an ad where required, include a valid physical postal address and a working opt-out, and honor opt-outs. Keep a suppression list and review any additional rules that apply to recipients' locations. This is a practical checkpoint, not legal advice for a particular campaign.
Start with a small, human-reviewed batch. Confirm that the company and person still match the brief, the offer is relevant, the data was obtained and processed under applicable terms, and the message is honest. Do not hand an agent the entire export plus a sequencer key and call the campaign ready. Matt himself closes by saying the tools do not replace a good offer, safe infrastructure, or a person who knows what the outreach should say.
Turn This Into a Business Idea
The strongest small offer here may be lead-list quality control rather than another giant contact database. This prompt asks for a real buyer and a 100-record pilot before any claims about meetings or revenue.
Find the false positives
Three offers and a seven-day quality pilot.
Video Chapters
0:00 Lead-count claim · 0:42 Why Jev · 2:31 ICP and Apollo · 11:47 Two qualification checks · 19:17 Results and cost.
Source Map
- Creator test: Matt Lucero's tutorial, published 4 October 2026, and the supplied transcript. Counts, timings, costs, and agency performance are creator-reported. Anevo is Matt's business.
- Model roles: OpenAI's Astra announcement and computer-use guide; TypeSafe's Jev documentation and OpenRouter's current model page.
- Data access and cost: Apollo's export guidance, Apollo's terms, and Spider's pricing page. Plan rights, restrictions, and prices should be checked at the time of use.
- Outreach rules: the FTC's business guide to CAN-SPAM. Other jurisdictions and vendor terms can impose additional requirements.