Frequently asked questions

DINQ is an AI talent search engine built for modern recruiting. Describe the person you're looking for in plain language, and its agent searches the open web plus a dedicated talent database, then returns a ranked shortlist — each candidate with a match score and source links to their homepage, Google Scholar, GitHub, and LinkedIn for verification.

It goes beyond finding names: DINQ parses a candidate's real work across sources, enriches the profile, verifies contact details, and lets you reach out — bringing search, evaluation, enrichment, and outreach into one workflow.

In the AI era, credentials and resumes are losing signal. Anyone can generate a polished, keyword-optimized resume, so it's an increasingly unreliable measure of real ability — and the strongest evidence of someone's work is scattered across GitHub, arXiv, OpenReview, personal sites, and technical communities, where it's impossible to evaluate at scale by hand.

Recruiters told us that finding a single senior AI candidate can take 40+ hours of manually stitching sources together, and that a mis-hire is costly. We built DINQ to judge people by what they've actually built and their real influence — not a packaged resume — and to make that search fast.

DINQ reads the signals that actually show ability. It analyzes GitHub activity (tech stack, contribution history, project impact), academic quality (research influence, citation network, venue prestige), shipped work (real products, demos, open-source contributions), and research collaboration networks (co-authors, labs, community influence).

By combining these signals across sources and ranking by depth of expertise, it surfaces strong people whose ability lives in their work — not just in a well-written resume.

On LinkedIn, a profile is self-reported and static — there's no code, no research output, and no independent proof of real ability. DINQ is a talent search engine that verifies candidates against their actual public work: GitHub, papers, projects, personal sites, and technical communities.

So instead of matching keywords in a profile someone wrote about themselves, DINQ evaluates evidence of what they've done, unifies their identity across platforms, and gives you source links to check it yourself.

It's one continuous workflow. Describe who you need in natural language (or use the API), and DINQ's agent searches, verifies, and returns ranked matches with source links. You review the enriched profiles, then save the promising ones into shortlist folders to organize your pipeline and collaborate with your team.

When you're ready to reach out, DINQ verifies the candidate's email and drafts a personalized message grounded in their real work — which you edit and send, without leaving the platform.

DINQ is for recruiting and talent teams — in-house recruiters, HR, and sourcing specialists — who need to find, evaluate, and reach high-end candidates from public sources. It's especially valuable where talent is hard to assess: AI and ML, infrastructure, quantitative finance, security, biotech, robotics, research, and healthcare.

It's the strongest fit when the best candidates are passive, or better represented by their work than by a resume.

DINQ works as a search agent, not keyword matching. Describe your need in natural language and it runs a multi-step search across the open web and its talent database — searching many sources in parallel, then doing a focused round to verify the top candidates before submitting strong matches, each with a match score and source links.

There are two ways to search: an interactive agent mode at dinq.me/search, and an open retrieval mode via the API that returns results in seconds. DINQ shows its tools and reasoning as it works, so you can see how and why it found each person.

Most tools do one piece: LinkedIn shows self-reported profiles; general web search has no talent analysis; point AI-sourcing tools can't parse technical depth across platforms; an ATS only manages process; GitHub and academic sites are single, isolated sources with no unified identity or way to make contact.

DINQ combines all of it into one engine — search, expert evaluation, profile enrichment, verified outreach, and team workflow — so you go from a query to a verified, contactable shortlist in one place.

Yes — DINQ starts free. The Free plan is $0 with 500 one-time Credits and a default shortlist, so you can try talent search before you pay.

Paid plans scale with your needs: Basic ($49/mo, 5,000 Credits, 5 shortlists), Pro ($199/mo, 20,000 Credits, unlimited shortlists, early features and priority support), and Enterprise (custom Credits and workflows). Annual billing saves 15%. Credits are spent on search and actions like contact lookups (10 Credits per verified email). See the pricing page for details.