DiscoLike

Most GTM teams don't even know they're prospecting with blinders on, missing two-thirds of the market in plain sight. GTM Engineering is humanity's next leap here on Earth, as the bottleneck shifts from product creation to distribution. DiscoLike is your reusable space rocket instead of use-once automations. We mine the entire Internet and let you search target accounts in vector space for precise matches over traditional LinkedIn-scraped based source like Apollo, Lusha, Clay, and Ocean that cover only one-third of the market and operate with keywords and categories. Raving about Claygent? It defaults to passing just the company URL, forcing you to build extra columns and tweak templates for anything more. Endless articles preach context engineering, then pass a company URL into a complex prompt and hope for the best? Disco Gen, a second-generation research agent for GTM, anchors your prompts with a full firmographics record and the company home page text. This maximizes model recall and prevents drift. Still waiting for hours for BuiltWith to compile your dataset, only to realize you need to tweak and redo? Sampling records and seeing one-year-old data from common crawl? We give you the same website tech stack targeting with fresh data and double it with visibility into SaaS tools that only we can see. View results in seconds. Three times the coverage. Context-aware prompts. Technographics your competitors will never have. Feels like it can't be true? Grab a demo or test drive DiscoLike. You'll be blown away. Your pipeline will thank you.

🚀 Meet Disco Gen, a Second-Generation AI Research Agent for GTM. Create new columns with any prompt for each company in search results 👇 ⭐ We automatically pass the full company profile along with its home-page text, anchoring your prompts with the richest context on the market. No extra cost if you use profile data only, and just our low standard price per record to add home-page text. ⚠️ Our competition sends only manually defined fields, usually just a company name or a URL. Disco Gen structures every prompt with an engineered context package including name, domain, contact information, address, top keywords and business categories, summary, and the entire home-page text. ✅ Why this matters: LLMs identify companies by correlating multiple signals rather than relying on a single string match. Feeding several data points greatly improves recall as the model leans on the added data points that a bare name would miss. This also reduces false negatives and minimizes drift. 1️⃣ We run one prompt per company record to guarantee each query has an isolated, unpolluted context window for the model to focus on. We also match content size with model memory to prevent overwrites and data loss. 📴 Disco Gen lets you turn off open-web search so the AI works only with the data we provide, removing the need for elaborate exclusion prompting. This scope control prevents outside data from distorting company-reported facts. 👉 We support all major models, just bring your key. Try us out and be prepared to be blown away by the consistency and accuracy of the answers.

🫠 GTM AI workslop: why engineering scraping bots are like burning MP3 playlists. Not long ago we chased songs like contraband 👇 💿 Ripping CDs, swapping MP3s and burning mixes. 🎵 Then Spotify showed up with every song in one place. Nobody in their right mind still calls friends to copy tracks. 🤖 GTM teams are vibe-coding, posting and exchanging scraping code and AI automations with the fever of the MP3 era, stitching together data from LinkedIn, vendor directories, Google Maps, trade-org listings... ⚙️ Then comes the grind: deduping, cleaning, validating semi-working automations for hours just to get an incomplete list with already aged data. That’s where B2B business-domain directories stand today with DiscoLike: • We’ve mapped every single business site worldwide, 60M+ websites in 48 languages. • We let you search businesses by word or phrase matches on their websites the same way Spotify lets you search by lyrics. • And just like Spotify can be tuned to reflect your music style exactly, we offer natural-language search for your ideal company profile in any vertical, no matter how verbose or obscure. 👉 Don’t get pulled into the AI workslop Vibe-coded directory scraping and validating may feel clever, but it’s as outdated as swapping MP3s from a friend’s hard drive.

3 common problems that make GTM engineers race to spin up new Clay automations or 8n8 workflows 👇 1️⃣ Huge inbound list mixed with public email providers that needs filtering and ranking. 2️⃣ Client wants to review the pipeline, see what’s working, what’s not, and get more of what is. 3️⃣ The “impossible” ask: find all mining and construction companies using Motorola long-range radios. All share one thing: none are easy. If they were, SaaS companies would have built them in already. 🤔 Before building your own automation and scoring logic, ask: how many corner cases will you miss? Will results be reliable? Is it worth the time? We cannot answer that, but we can take them off your plate in minutes, freeing days for your next client. 1️⃣ Upload your list. We will append domain status (biz, non-biz, parked, dead, redirect, new), remove duplicates, then let you rank the remaining active businesses by ICP similarity, keywords, geo, industry, time in biz, vendors in use, and more. 2️⃣ Save your client thousands on questionable AI ICP tools. Export pipeline accounts with stage data, run segmentation, and we will return ICPs with descriptive labels and accounts plus close/won ratios per segment. Just target the top performers and guarantee product market fit results. 3️⃣ Radios are just one example. FCC databases exist since companies must register, but many gov and org datasets return only company names. Our name-to-domain match solves this with support for DBA and the newest matching algo. 👉 When GTM crosses from smart automation into full-on product development, not every problem needs to be solved in-house. Some are better outsourced. Try us out at http://www.discolike.com

🚀 Meet Disco Gen, a Second-Generation AI Research Agent for GTM. Create new columns with any prompt for each company in search results 👇 ⭐ We automatically pass the full company profile along with its home-page text, anchoring your prompts with the richest context on the market. No extra cost if you use profile data only, and just our low standard price per record to add home-page text. ⚠️ Our competition sends only manually defined fields, usually just a company name or a URL. Disco Gen structures every prompt with an engineered context package including name, domain, contact information, address, top keywords and business categories, summary, and the entire home-page text. ✅ Why this matters: LLMs identify companies by correlating multiple signals rather than relying on a single string match. Feeding several data points greatly improves recall as the model leans on the added data points that a bare name would miss. This also reduces false negatives and minimizes drift. 1️⃣ We run one prompt per company record to guarantee each query has an isolated, unpolluted context window for the model to focus on. We also match content size with model memory to prevent overwrites and data loss. 📴 Disco Gen lets you turn off open-web search so the AI works only with the data we provide, removing the need for elaborate exclusion prompting. This scope control prevents outside data from distorting company-reported facts. 👉 We support all major models, just bring your key. Try us out and be prepared to be blown away by the consistency and accuracy of the answers. Find out more at https://discolike.com

💰 Usage-based pricing sounds sexy… and just as risky, but what’s the alternative? Pure pay-per-use? CFOs can’t forecast it, vendors can’t plan capacity. Even hybrids with overages can backfire, like the time a client’s sloppy code torched thousands in API credits and refused to pay. And rigid pricing plans with locked-in yearly commitments are totally outdated. Instead there is a proven model hiding in plain sight: 📀 Decades ago, databases mastered space management: instead of running out, they quietly grow in steps that double each time. It’s predictable, smooth, and remarkably effective. Here’s the SaaS equivalent we created: • Start on an entry-level subscription tier. • Run out of credits? Step up to the next plan that simply doubles your credits. • Each step-up unlocks a rate discount and new features, rewarding growth. • Not using it next month? Scale back down, no penalties. ✅ It’s simple for customers, friendly for finance, and keeps vendors sane on capacity planning. A “step-up, step-down” subscription is the most balanced usage-based model, based on years of database engineering that taught us how predictable scaling should work. ✨ Let pricing feel like progress: a clear next level when you grow, and the freedom to step back when you don’t need the extra room. Flexible growth at any time and no shocker bill when you make a mistake. Try us at http://www.discolike.com

Targeting Local Businesses with Serper and Google Maps: GTM Gold Standard or Missed Opportunity? What If I Could Get You 50% More Coverage?👇 Many owners deliberately stay off Google Maps. Below are key reasons businesses choose not to list: • Limited recourse on unfair reviews makes owners feel powerless. • Drive-to-location businesses prefer service area and lose Maps pin. • Fake reviews and review-bombing can damage reputations overnight. • Privacy or safety worries deter home-based or sensitive businesses. • Public phone numbers invite spam calls and scams. • Crowdsourced edits can change details without owner approval. Serper.dev is the best there is for reading Google Maps, but it is not perfect either: • Using only county or city coordinates instead of a fine 14x grid misses listings. • Maps shows limited results per page and incomplete pagination leaves out additional listings. • Google ranking factors and frequent algorithm updates can suppress or reshuffle businesses. ✅ We scanned three Bay Area cities at 14x fine grid and compared results with DiscoLike dentist data for businesses with websites, with dead and parked domains removed: • San Francisco: 182 vs 501 • San Mateo: 51 vs 58 • Palo Alto: 44 vs 47 Average: 92 vs 202 👉 Unexpected? You’re missing half the businesses. Restaurants do slightly better, most others worse. Scan any city and vertical, send me the data, and I’ll return the DiscoLike list to compare. Sign up to run your test at http://www.discolike.com

Scraping Google Maps is a demo trap. Let’s talk dentists in the US. 🦷 Want to find dentists who carry Invisalign? Sounds simple 👇 📍 Clay plus Google Maps: • Places API caps at 60 results per query, the consumer app shows limited, viewport-bounded results, while there are over 100K dentist offices nationwide • Radial search only, so you need to grid the whole country with tiny overlapping circles and dedupe results 🤯 • Outdated or missing data, plenty of dentists do not even list on Maps, mine is not, check yours • Then comes scraping 100K sites, with cookies, JavaScript, and bot blocks breaking many crawls • Add fraud, Google says it removed or blocked millions of fake business profiles and has sued operators • Result, maybe half the market at best, plus a lot of fake or obsolete profiles 🏆 Now compare with the DiscoLike business directory: • Built from active SSL certificates and full site text, refreshed every 1–3 months • Fake AI sites detected and removed • Full homepage text + all public contact info ready for targeting • National coverage, no caps, no circles, no gymnastics • One query, surface tens of thousands of Invisalign providers 👉 Takeaway: Maps scraping might look clever in a demo, but it collapses at scale. A real business directory gives you coverage and quality, and saves your time and $$$ to invest elsewhere. Build your list at http://www.discolike.com

🤔 Why do AI prompts inside GTM automations break or yield unpredictable results? Here’s a GTM Engineer Cue Card to navigate the randomness👇 ⚙️ Generative models are amazing, but their answers vary. The reason is the decoding step. Transformers and their attention heads encode a fixed set of next-word probabilities from the training data. That part is deterministic. 🎲 The response to the prompt is generated during the decoding step, where variance appears. The system samples from that probability spread learned during encoding, using a mix of temperature, top-k, top-p, or beam search approaches. 🧬 This sampling was inherited from early language translation models to try alternate phrasings when no exact translation exists, and reused for possible answer generation when no good answer is found: often credited as model creativity, but truly a band-aid for missing data. 🎯 Similarity search is different. Given the same embedding model, index, and query, similarity scores and the ranking are stable and auditable. You still harness deep learning through embeddings, but you avoid the randomness introduced by generative decoding. ✅ Why this matters for DiscoLike: our product leans on embeddings-based company search not open-ended generation. That gives teams reproducible, explainable results for targeting and segmentation, with the quality of deep learning and the stability of classic search. 👉 Takeaway: if your use case needs consistent decisions and easy QA, use embeddings + similarity search. Save stochastic generation for tasks where variation is a feature, not a bug. 📚 Sources: deterministic logits vs. stochastic decoding with temperature, top-k, top-p, decoding methods overview, similarity ranking for dense retrieval, DiscoLike’s embeddings-based search.

Dear clients, partners, and prospects, If you are attending the Hubspot INBOUND event on Sep 3 in SF or just happen to be in town, we’d love for you to join us for a fun rooftop party. We’re sponsoring the event and have a few spots available, so don’t miss it. It’s a great chance to meet in person and connect with other senior leaders in the space. Please PM me and I can register you, or use the link in the comments and select Discolike.