# anVendor — full LLM corpus > Complete reference corpus for anVendor (https://anvendor.com). Includes the site summary, key facts, and the full text of every published article. For the short summary alone, see /llms.txt. ## About anVendor anVendor is a technology-intelligence tool for go-to-market and research teams. Look up any company to see the services and tools it runs, or look up any service to see which companies are using it. Built for sales, product, and research workflows that need fast, accurate signals about a company's tech stack — without juggling enrichment vendors. ### Key facts - **Name:** anVendor (also: anvendor.com) - **Category:** Technology intelligence / technographics / tech-stack lookup - **Homepage:** https://anvendor.com/ - **Tagline:** Technology intelligence, distilled. - **Audience:** Sales, marketing, product, and research teams - **Two search modes:** - **Search for company** — enter a domain, get the services and tools that company runs. - **Search by service** — enter a SaaS product or technology, get a list of companies using it. - **Pricing:** Free tier (10 searches/month), Pro at $49/month (unlimited searches + CSV export), Team at $149/month (API access + collaboration + dedicated account manager). See https://anvendor.com/pricing. - **Payments:** Paddle Billing (Merchant of Record). No long-term contracts on Pro. - **Account model:** email + password. Sessions are server-side; the site is fully SSR. - **Output formats:** in-app results, CSV export (Pro+), REST API (Team). ### What anVendor does - **Domain analysis.** Given a company domain (e.g. `stripe.com`), anVendor returns the services that company uses across categories: analytics, CRM, marketing, hosting, payments, developer tools, security, productivity, and more. - **Service-to-company search.** Given a SaaS product or technology (e.g. `Segment`, `Snowflake`, `Notion`), anVendor returns a list of companies that use it — useful for prospecting, competitive research, and partner discovery. - **Lead enrichment.** Turn a bare list of domains into a structured technographic profile for outbound, account research, or CRM enrichment. - **CSV export & API.** Pull results into spreadsheets (Pro) or directly into your own pipeline (Team). ### Plans - **Starter — Free.** 10 searches per month, basic company data, email support. Best for evaluation and casual lookups. - **Pro — $49/month.** Unlimited searches, advanced company data, CSV exports, priority support. Best for individual reps, researchers, and small teams. - **Team — $149/month.** Everything in Pro, plus team collaboration, REST API access, custom integrations, and a dedicated account manager. Best for revenue and ops teams that want anVendor data inside their CRM or warehouse. ### Pages - Home / Search — https://anvendor.com/ - Pricing — https://anvendor.com/pricing - Blog — https://anvendor.com/blog - Get started — https://anvendor.com/get-started - Sign in — https://anvendor.com/sign-in - Account (signed-in users) — https://anvendor.com/account - Privacy Policy — https://anvendor.com/privacy - Terms of Service — https://anvendor.com/terms --- ## Articles ### How to map a company's tech stack in under 5 minutes *Tag: Guide · 4 min read · May 15, 2026 · https://anvendor.com/blog/map-tech-stack-in-5-minutes* Mapping a company's tech stack used to mean cobbling together Wappalyzer hits, BuiltWith exports, LinkedIn job posts, and a guess based on a press release. With anVendor, the workflow collapses to five minutes — and it's repeatable across hundreds of accounts. Here's how to do it. **Step 1 — Start with the domain.** Every research session begins at anvendor.com. Pick *Search for company* and drop in the prospect's domain (no `https://`, no path). anVendor returns a categorized list of services detected on or around that domain — analytics, marketing, sales, infrastructure, payments, security. **Step 2 — Skim by category, not by tool.** The temptation is to scan the full list. Don't. The signal you need is usually in two or three categories. Selling sales enablement? Look at CRM and engagement. Selling devtools? Hit the infrastructure and developer-tools sections. Categories tell you whether you're talking to the right buyer before any tool name matters. **Step 3 — Spot the absences.** The most actionable signal is often what's *missing*. A 200-person SaaS company with no analytics tool detected is interesting. A high-growth fintech with no fraud or compliance tooling is interesting. Absences point to a likely active vendor evaluation — or an opportunity to seed one. **Step 4 — Cross-reference adjacent tools.** The tools a company has chosen tell you about budget tolerance, integration appetite, and team maturity. A Salesforce + Outreach + Snowflake stack means they buy enterprise contracts. A HubSpot + Apollo + BigQuery stack means a different conversation entirely. Read the constellation, not the individual stars. **Step 5 — Turn it into a sentence.** Before you reach out, force yourself to write a one-sentence hypothesis. *"They use Stripe, no fraud tool, growing 40% YoY — they're probably feeling chargeback pain."* That sentence is what makes a cold outreach feel warm. Five minutes. One sentence. Ready to talk. --- ### The rise of AI-native SaaS tools in 2026 *Tag: Research · 6 min read · May 10, 2026 · https://anvendor.com/blog/ai-native-saas-tools-2026* We pulled adoption signals across the companies in anVendor's index and looked at which categories grew fastest year over year. The headline isn't subtle: AI-native tools are eating share faster than any category we've tracked. **What counts as "AI-native."** We define AI-native as a product where the core value proposition collapses without an LLM or foundation model — note-takers, agentic assistants, retrieval search, voice analyzers, code reviewers, and the rapidly evolving "AI Ops" tier. Tools that bolt AI features onto an existing product (a chatbot inside a help desk, summarization inside a CRM) are excluded; that ship sailed in 2024. **What's growing.** Five categories led the year: - *Meeting intelligence.* Fireflies, Otter, Gong's AI tier, and a long tail of next-gen tools. - *Coding agents.* GitHub Copilot has competition. We saw three breakout entrants in 2026. - *Customer support deflection.* AI-first support tools displaced live-chat in mid-market accounts. - *Voice agents.* Sales prospecting voice agents finally moved past pilots. - *Internal RAG search.* Glean and four challengers. **What's not growing.** Two surprises. First, generic AI writing tools (Jasper, Copy.ai-style) plateaued — ChatGPT subsumed the use case. Second, AI legal tools grew slower than we expected. Buyers want them; procurement is still slow. **What it means.** For sellers: every account brief should include "are they running an AI-native stack?" as a qualifier. For buyers: assume your peers are running four to seven AI tools by now, not one or two. For investors: the floor for AI-native ARR is no longer "experimental budget" — it's a line item. --- ### Building anVendor: lessons from our first year *Tag: Product · 5 min read · April 28, 2026 · https://anvendor.com/blog/lessons-from-our-first-year* A year ago we shipped the first version of anVendor. Here are the things we got wrong, the things we got right, and the things we'd tell ourselves on day one. **Lesson 1 — Pick a wedge sharper than you think you need.** Our first positioning was "technology intelligence for go-to-market and research teams." Too broad. Customers couldn't repeat it back. Six months in we tightened to "find every company using [tool]" — one search box, one answer. Demos shortened. Trial-to-paid doubled. **Lesson 2 — Trust beats coverage.** We obsessed over expanding the index. Customers cared more about whether the data was right. One wrong result lost more deals than ten missing tools. We rebuilt the confidence pipeline before we expanded coverage. **Lesson 3 — Pricing is product.** The free tier started at fifty lookups. Conversion was flat. We dropped it to ten. Conversion doubled within a month. The number doesn't matter — what matters is whether a paying customer feels they're getting value past the wall. **Lesson 4 — Say no, then say no again.** Every prospect asks for an integration. Salesforce, HubSpot, Outreach, Apollo, Clay. We built one (CSV export) and held the line. The team's energy stayed on the index. The integrations will come — after the wedge is undeniable. **Lesson 5 — Shipping matters more than strategy.** The most consequential strategic decisions came from shipping fast and watching what stuck. The customer who unblocked a meaningful MRR account did it because we shipped CSV export on a Friday. Strategy is a hypothesis. Shipping is the test. --- ### Why go-to-market teams need better tooling data *Tag: Opinion · 7 min read · April 15, 2026 · https://anvendor.com/blog/gtm-teams-need-better-tooling-data* Most go-to-market teams operate on bad tooling data. It's the open secret of B2B sales: the technographic field in your CRM is wrong half the time, and your reps know it. They learned to ignore it. Now they don't trust any data the system gives them. **The cost of stale signals.** A signal that's six months old isn't a signal — it's a guess. Companies churn vendors quarterly. Stacks shift faster than enrichment pipelines refresh. A "uses Salesforce" tag from 2024 says nothing about today's reality. Reps act on it anyway, because it's there. **What "good" looks like.** Three properties: - *Recency.* Signals refreshed in days, not months. - *Provenance.* You can trace why a signal was attributed — DNS, page asset, header, job post. - *Confidence.* A signal isn't binary. It has a probability. Treating low-confidence and high-confidence the same is how reps lose trust. **The compounding cost of bad data.** Every wrong tag is a small theft. Five minutes of rep time on a misqualified account. An apology email when the discovery question lands wrong. A pipeline meeting where the forecast leans on a phantom stack. Multiply across a thirty-rep team and you're losing more revenue to bad data than to bad reps. **What to do.** Audit your technographic field. Take a sample of fifty accounts. Compare your CRM's tags to the truth on the ground — their job posts, their site headers, anVendor. If you're under 70% accurate, you're operating on noise. The fix isn't more data. It's better data. --- ### New: export your research to CSV and API *Tag: Product · 3 min read · April 2, 2026 · https://anvendor.com/blog/csv-and-api-export-launch* Today we're shipping two of the most-requested features in our roadmap: CSV export on the Pro plan, and a REST API on the Team plan. **CSV export (Pro).** Every search result page now has an *Export* button. Hit it and you'll get a `.csv` with every company or service in the result set, plus the metadata anVendor surfaces: category, confidence, last-detected date, and source signal. Drop it into a spreadsheet, paste into your CRM, or feed it into Clay. **REST API (Team).** Team customers get a personal API key from the *Account → API* tab. Two endpoints to start: - `GET /v1/companies/{domain}` — services detected for a company. - `GET /v1/services/{name}/companies` — companies using a given service. Both return JSON with the same fields as the in-app view. Rate-limited at 1000 requests per hour; raise the limit by contacting your account manager. **What's next.** The two features customers asked for next: a webhook for stack changes (so you get pinged when a prospect adopts or drops a tool), and a Salesforce sync (so signals land directly in your CRM). Both are on the roadmap for the next two months. If you want early access to either, reach out from the Pricing page. --- ### Understanding technology signals at scale *Tag: Engineering · 8 min read · March 20, 2026 · https://anvendor.com/blog/understanding-technology-signals-at-scale* We get asked how anVendor actually detects what a company uses. Here's the short version of how the pipeline works — without the parts that would make it easy to fool. **Sources.** We pull from a wide set of publicly observable signals. Some are obvious — site headers, page assets, DNS records, public job listings. Others are less obvious — CDN footprints, third-party script attribution, security headers, public commit metadata. No single source is reliable on its own. Most signals are noisy in the same direction: they over-report. **Verification.** Every candidate signal is cross-checked against at least two independent sources before it surfaces in a result. If a tool appears once in a job post but nowhere on the site or in the headers, the signal is held back. If it appears in three independent places, it ships with high confidence. **Confidence scoring.** Each signal carries a probability. A confirmed page-asset hit on a CDN-served script is near-certain. A single mention in a careers page is a hint. We expose that confidence in the API and in the export — and we never promote a low-confidence signal into the headline categories. **The hard part.** The hard part isn't detection. It's de-duplication. A company that's been acquired, rebranded, or merged often shows three overlapping stacks. A tool that was rebranded last quarter shows up under both old and new names. The pipeline has to know that *Segment* and *Twilio Segment* are the same thing — and that *Heap* and *Mixpanel* are not. **Why this matters.** Most technographic data on the market is built on a single source — a crawl, a vendor list, a leaked dataset. anVendor is built on a verification model. It's slower to add new tools, but the signals customers act on are signals they can trust. That trade-off was the most important call we made in year one. --- ## Contact - Domain: anvendor.com - Support: via the in-app email link on the Pricing page (https://anvendor.com/pricing).