Suppose you have the Profile URLs. Maybe it came from a webinar registration, or you have built a datasets from multiple sources. You know nothing about these person — not their job title, not their company size, and not even their skills. Looking them up one by one manually means doing unnecessary work and wasting your valuable time.
In 2026, this entire process has been squeezed into a single API call. This guide will walk you through, how modern person profile enrichment actually works, what it involves, why so many tools in the market quietly returns outdated junk, and how to get a clean, verified, and current profile back in seconds — including exactly how to do it with EnrichmentAPI’s Person Finder API by just using Profile URL.
What Does “Enriching a Person Profile” Actually Mean in 2026?
A Person Profile Enrichment is the process of taking one identifying detail you already have about someone — an email address, a name, or a Profile URL — and retrieving a complete, structured profile of that person: their current job, company, seniority level, location, skills, education, and professional history.
Before Enrichment tools, this used to be exclusively a manual task. A recruiter, salesperson, or researcher would open LinkedIn, read through a profile and note down the relevant details. It worked but it consumed time and didn’t scale.
Today, Person Profile Enrichment is almost entirely API-driven. You just send a single piece of information to an enrichment service and it returns the rest of the profile data automatically in seconds, structured as clean data(json, csv, etc..).
The “Junk Data” Problem Nobody Warns You About
Here’s the harsh truth about the most of person enrichment tools in the market: they’re often returning information that was accurate at some point of time, but isn’t anymore.
This happens because most of the enrichment service providers maintain a massive, pre-built database of profiles collected over time. Although, database gets refreshed periodically — sometimes every few months, sometimes less often — but it never provides current/real-time data. In the gap between refresh cycle, real life keeps changing. People get promoted, switch companies, or leave workforce entirely. None of that shows up in stale data until the next refresh catches up.
This is what we call “junk data” — information that looks complete and confident but might be wrong and outdated.
This isn’t a hypothetical problem. It has very real costs:
Wasted outreach. A sales rep sends a personalized email referencing someone’s role at “Company A”, only for the prospect to reply that they haven’t worked there in over a months. The credibility damage from a mistake like this is often worse than sending no email at all.
Wasted credits and budget. Many enrichment tools charge you the same price whether the data they return is accurate or stale. You’re paying for a enrichment either way — the question is just whether what you get back is actually useful on your side.
Wasted time on follow-up verification. Teams that don’t trust their enrichment tool’s freshness often build an extra manual verification step into their workflow — checking LinkedIn anyway just to be safe. At that point, the tool isn’t really saving time at all; it’s just adding an extra step before the manual work you were trying to avoid.
The fix of this problem is not stop using Enrichment tools — it’s to understand which Enrichment tools is getting you the current data.
Two Ways a Enrichment Can Get You an Answer
Broadly, every Person Enrichment tools in the market falls into 2 categories, and understanding the difference is the single most useful thing you can take away from this guide.
Database-Driven Enrichment
Here the service provider has already built and stored a lage collection of profiles. When a user makes a call, the system returns the information from existing data.
Where it shines: Speed and cost. Because nothing to needs to be fetched live, so the responses comes instantly which cost less as it just fetches just from the database
Where it struggles: Freshness. The data is only as fresh as the last time database is refreshed. which could be days, months, or may be years.
Real-Time Enrichment
Instead of pulling information from stale database, the provider fetches the information live from the web at the exact time when you make a request.
Where it shines: Accuracy. If someone has changed the job position last week, a real-time enrichment will reflect that in the response.
Where this trades off : Speed. Fetching live data from the web naturally take longer than fetching from database, since the tool has to go and check in real time.
Neither approach is universally “best” — but having access to current and up to date information is far better than using outdated data.
What a Good Person Profile Enrichment Should Return
Regardless which way you use, a quality enriched data should give you more that just a name and a job title. At least, look for:
Full Name
Current Job Title
Current Company
Location
Skills
Education
These points matters the most when it comes to Profile Enrichment. A tool that hands back with these, shows the completeness of information.
How to Enrich Person Profile using EnrichmentAPI’s Person Finder API
Here the theory becomes pratical, easy, accurate and reliable way of enrichment for your workflow.
EnrichmentAPI’s Person Finder API is built specially to solve the problem of “junk data” described earlier. Here’s exactly how it works with Profile URL as a single piece of information.
Step 1 — Send the ProfileURL to the API
You make a single API call, passing the Profile URL as the input parameter, along with your API key:
1import requests2 3resp = requests.get(4 "https://test.enrichmentapi.io/person",5 params={6 "api_key": "YOUR_API_KEY",7 "linkedin_id": "rahul-patil-a0944836",8 },9)10print(resp.json())Step 2 — Receive the Structured Profile
Within seconds, the API returns a clean JOSN response with the full enrich profile:
1{2 "fullName": "Rahul Patil",3 "linkedin_internal_id": "126504873",4 "first_name": "Rahul",5 "last_name": "Patil",6 "public_identifier": "rahul-patil-a0944836",7 "background_cover_image_url": "https://images.enrichmentapi.io/person/446859f326037205e2a0f1f1e8e12f359278f97de4329d02a8c94fb4d0bee7f9.jpg",8 "profile_photo": "https://images.enrichmentapi.io/person/3e16f79c8ed92bfac850abab9f728a6777adcf4d10986f5b5f7ee0563d29ee3c.jpg",9 "location": "Seattle, Washington, United States",10 "followers": "84K followers",11 "connections": "500+ connections",12 "experience": [13 {14 "company_image": "https://images.enrichmentapi.io/person/11e7176e910eb05b435ee6929726b0b3d96b6ca9258059756455fd6a01a17eee.jpg",15 "company_name": "Anthropic"16 },17 {18 "company_url": "https://www.linkedin.com/company/gustohq",19 "company_image": "https://images.enrichmentapi.io/person/2d9945cdfc82e352c25212069e823046c8967da0cb0fe3357be8a89d30e19a9e.jpg",20 "company_name": "gustohq"21 },22 {23 "company_url": "https://www.linkedin.com/company/stripe",24 "company_image": "https://images.enrichmentapi.io/person/e51347c6a5d56c198ad12a8dfeacc0db606ca28f5ea23931c2de68d96b2d2460.jpg",25 "company_name": "stripe"26 }27 ],28 "education": [29 {30 "college_image": "https://images.enrichmentapi.io/person/d5005fa736f32cec96ec35fb7be2062cceeba7f8eb15de2d647d73e3066ed609.jpg",31 "starts_at": "2011",32 "ends_at": "2013"33 },34 {35 "college_image": "https://images.enrichmentapi.io/person/05309642aee005a3321c34f1966d8a916dbab8306919abd5e1b0cae9ceb6356f.jpg",36 "starts_at": "2003",37 "ends_at": "2004"38 }39 ],40 "description": {41 "description1": "Anthropic",42 "description1_link": "https://www.linkedin.com/company/anthropicresearch?trk=public_profile_topcard-current-company",43 "description2": "University of Washington",44 "description2_link": "https://www.linkedin.com/school/university-of-washington/?trk=public_profile_topcard-school"45 },46 "activities": [47 {48 "author_name": "Rahul Patil",49 "author_profile": "https://www.linkedin.com/in/rahul-patil-a0944836",50 "author_image": "https://images.enrichmentapi.io/person/3e16f79c8ed92bfac850abab9f728a6777adcf4d10986f5b5f7ee0563d29ee3c.jpg",51 "date": "1w",52 "post_link": "https://www.linkedin.com/posts/rahul-patil-a0944836_claude-fable-5-is-available-today-its-a-activity-7470162421366550528--D5w",53 "activity_type": "shared",54 "post_text": "Claude Fable 5 is available today! It's a new moment for AI: a Mythos-class model, the most capable class of systems we've built, now safe for general use. It's already changed how we work internally, and I'm excited to see what you all do with it. Every request runs past safety classifiers trained to detect misuse in cybersecurity and biology. When one triggers, your request is answered by Opus 4.8 instead. More than 95% of sessions never see a fallback, and 1,000+ hours of external red-teaming produced no universal jailbreak. In terms of benchmarks, Fable 5 reached 80.3 on SWE-bench Pro (Opus 4.8 scores 69.2), 88 on Terminal-Bench 2.1. State-of-the-art on nearly every coding benchmark we tested. But the benchmarks undersell how truly capable it is. Fable holds quality deep into long, hard problems where most models degrade. It verifies its own work. It catches what others miss, things like root-cause bugs that no other model had surfaced. Base44 found it \"much deeper and better at one-shotting full apps\"; at Genspark it came out #1, winning head-to-head against every model they tested. Internally, writing code stopped being the slow part a while ago — Anthropic engineers on average shipped 8x as much code per quarter as they did compared to 2021-2025 — Fable pushes the bottleneck further toward verification and review. We're excited to make all of that available today for every use case outside bio and cyber. For API customers, here's how we've imagined fallbacks: pass a fallbacks parameter and the Messages API retries any blocked turn on Opus 4.8 server-side — even mid-stream, keeping the partial output. We think of this as a graceful handoff between models, and we'll be iterating on the design with the community. Moments like this are worth doing right. We're making sure it's safe, but the classifiers may be annoying at times. They're tuned conservatively, and false positives will keep coming down. Read more here: https://lnkd.in/giBEAAcP",55 "likes": 10451,56 "comments": 214,57 "article_url": "https://lnkd.in/giBEAAcP?trk=public_profile__posts-text"58 },59 {60 "author_name": "Rahul Patil",61 "author_profile": "https://www.linkedin.com/in/rahul-patil-a0944836",62 "author_image": "https://images.enrichmentapi.io/person/3e16f79c8ed92bfac850abab9f728a6777adcf4d10986f5b5f7ee0563d29ee3c.jpg",63 "date": "1mo",64 "post_link": "https://www.linkedin.com/posts/rahul-patil-a0944836_higher-usage-limits-for-claude-and-a-compute-activity-7457859244659470338-jPwk",65 "activity_type": "shared",66 "post_text": "A lot to share from our developer conference, Code w/ Claude, today. We launched Claude Managed Agents last month, and today we announced dreaming is a new capability, Outcomes and multiagent orchestration are moving from research preview to public beta. Together they tackle the hardest parts of running agents in production: keeping them accurate, helping them learn, and keeping them from bottlenecking on complex work. And we had some exciting news to help you get the most out of Claude. As a thank you to our users, we're doubling Claude Code's five-hour rate limits for Pro, Max, Team, and seat-based Enterprise plans, and raising API rate limits considerably for Claude Opus. The headroom comes from a new compute partnership: we're using all the capacity of SpaceX's Colossus 1 data center, and putting it directly toward individual developers and small teams. It has been an incredible year so far for Anthropic, and developers are at the center of everything we build. Thank you to everyone building with Claude. Our team is inspired every day by what you're shipping. More to come. More on Managed Agents: https://lnkd.in/gXCZDJyr More on rate limits and the SpaceX compute deal: https://lnkd.in/gj5XRiFK",67 "likes": 1757,68 "comments": 94,69 "article_url": "https://lnkd.in/gXCZDJyr?trk=public_profile__posts-text"70 },71 {72 "author_name": "Rahul Patil",73 "author_profile": "https://www.linkedin.com/in/rahul-patil-a0944836",74 "author_image": "https://images.enrichmentapi.io/person/3e16f79c8ed92bfac850abab9f728a6777adcf4d10986f5b5f7ee0563d29ee3c.jpg",75 "date": "2mo",76 "post_link": "https://www.linkedin.com/posts/rahul-patil-a0944836_introducing-claude-opus-47-activity-7450562834670960640-soCP",77 "activity_type": "shared",78 "post_text": "Claude Opus 4.7 is out today. There are some significant behavioural changes. It reasons more, reaches for tools less, follows instructions more literally, and checks its own work before reporting back. That shifts how you prompt and how much oversight the work actually needs. This is the next upgrade in the Opus line, and it also plays a specific role on the path to Mythos. Last week we announced Project Glasswing, our work on cyber safeguards. 4.7 is the first model shipping with those safeguards in production: its cyber capabilities are deliberately less advanced than Mythos Preview, and what we learn from deploying these protections at scale is how we get to a broad Mythos release responsibly. The eval movement with Opus 4.7 on coding is significant. SWE-bench Pro jumps from 53.4 to 64.2. Terminal-Bench 2 from 65.4 to 69.4. In production, Rakuten is putting 3x more tasks through it, and Cursor's internal bench cleared 70% versus 58% on Opus 4.6. On the tool use shift: in most cases more reasoning and fewer tool calls is a better outcome. When you do want heavier tool use, two things help. Raise the effort setting, since high and the new xhigh level show substantially more tool calls in agentic search and coding. And be explicit in your prompt about when and why to use a given tool, including telling the model to err on the side of using it more. Same pricing as 4.6. Available today in Claude Code, on the API, Bedrock, Vertex AI, and Microsoft Foundry. https://lnkd.in/g_km9d_8",79 "likes": 10264,80 "comments": 246,81 "article_url": "https://lnkd.in/g_km9d_8?trk=public_profile__posts-text"82 },83 {84 "author_name": "Rahul Patil",85 "author_profile": "https://www.linkedin.com/in/rahul-patil-a0944836",86 "author_image": "https://images.enrichmentapi.io/person/3e16f79c8ed92bfac850abab9f728a6777adcf4d10986f5b5f7ee0563d29ee3c.jpg",87 "date": "2mo",88 "post_link": "https://www.linkedin.com/posts/rahul-patil-a0944836_south-park-commons-bangalore-has-built-something-activity-7448037333708218369-WiyV",89 "activity_type": "shared",90 "post_text": "South Park Commons Bangalore has built something rare --- a room full of technical people who are comfortable saying \"I don't know yet.\" That's their whole thesis, the -1 to 0 stage, and you feel it immediately. Nobody's pitching. Everybody's just curious. Got pushed on safety, scaling, trust, and what \"keeping up\" actually even means anymore. Left inspired! Thanks to Ankit Chowdhary and the SPC India crew for having me. Truth: AI is advancing at a crazy speed. Everyday we wake up to new definitions of what's possible. Question: How do you keep up exponential growth, and think about safety & future possibilities...all at the same time? Chatted about this and a lot more, with Anthropic CTO Rahul Patil, at South Park Commons, Bangalore. Full episode is out now! Link in comments.",91 "likes": 364,92 "comments": 1593 },94 {95 "author_name": "Rahul Patil",96 "author_profile": "https://www.linkedin.com/in/rahul-patil-a0944836",97 "author_image": "https://images.enrichmentapi.io/person/3e16f79c8ed92bfac850abab9f728a6777adcf4d10986f5b5f7ee0563d29ee3c.jpg",98 "date": "2mo",99 "post_link": "https://www.linkedin.com/posts/rahul-patil-a0944836_thrilled-to-welcome-eric-boyd-to-anthropic-activity-7447303286514200576-da0x",100 "activity_type": "shared",101 "post_text": "Thrilled to welcome Eric Boyd to Anthropic as Head of Infrastructure! Eric will be focused on building and scaling the infrastructure Anthropic needs to continue advancing research and product development at the frontier. His experience leading infrastructure at enterprise scale will help ensure we can meet record demand from customers around the world. He joins us from Microsoft, where he built core infrastructure for foundation models—including Claude. I'm excited to join the amazing team at Anthropic today where I'll be leading the Infrastructure team! I've been privileged to have a front row seat to the explosion of LLMs, and the team at Anthropic is truly special. The combination of the absolute leading models with a culture that is committed to their mission is inspiring and I can't wait to lean in to help. AI is accelerating at an incredible pace, and the impact of Claude Code in the last 6 months, and particularly the last two months, just shows the power of what is possible. Bringing Powerful AI to the world in a way that brings the benefits to everyone will be so important, and I can't think of a better place to make this happen.",102 "likes": 636,103 "comments": 16104 },105 {106 "author_name": "Amol Avasare",107 "author_profile": "https://www.linkedin.com/in/amolavasare",108 "author_image": "https://images.enrichmentapi.io/person/a9e30d0fb98de65a0eb06d334bece5cfaf36d7afb0e78f5a2836a03b780b087f.jpg",109 "date": "1w",110 "post_link": "https://www.linkedin.com/posts/amolavasare_say-hello-to-claude-fable-5-a-mythos-class-activity-7470159874601979904-h7PR",111 "activity_type": "liked",112 "post_text": "Say hello to Claude Fable 5, a Mythos-class model that we’ve made safe for general use 🔥 This ones a doozy, I'm so glad we're finally able to share with y'all the ridiculous capabilities we rely on so heavily inside Ant. Some things to know about Fable 5: - Our smartest ever model, by a long shot 📈 - SOTA on nearly all benchmarks, really crushes it on software engineering and knowledge work - Can run for days, and it's lead over other models grows for longer tasks - Cyber + bio classifier blocks to prevent serious harms, with queries in these domains instead routed to Opus 4.8 (we make clear in product if this happens) - Launches in conjunction with Mythos 5 (only available through Glasswing), which is the exact same model as Fable 5 but without those classifier blocks. Most Fable 5 users get the exact same experience as Mythos 5, given those blocks only fire in ~5% of Fable 5 sessions - Pricing IMO is very reasonable, at $10 per million input tokens and $50 per million output tokens. So only 2x Opus 4.8, and far cheaper than Mythos Preview which was at $25 / $125. And since it's more token-efficient, cost per task is very solid (e.g. top FrontierCode score even at medium effort) I've lost track of how many \"holy shit\" moments I've had with Fable 5... First time I've really felt like a model was a true peer / coworker that I could partner with on difficult problems, and more reliably delegate real work to. Recommend really pushing the bounds of what you try to give this model, it can do far more than you may realize. We expect demand for Fable 5 to be intense, and we're still bringing online the capacity needed to serve the model at full scale. On the Claude API or consumption-based Enterprise plans, Fable 5 is fully available from today. On Claude subscription plans, we're rolling it out in stages: - From today through June 22, Fable 5 is fully included within Pro, Max, Team, and seat-based Enterprise subscription limits. - On June 22, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend this two-week window. - After this point (when sufficient capacity allows us to do so) we aim to restore Fable 5 as a standard part of subscription plans. We're sprinting hard at this, and will keep you posted as we learn more on timing here. Excited to see what you all get upto with the model! https://lnkd.in/gm63Qtgt",113 "likes": 2665,114 "comments": 128,115 "article_url": "https://lnkd.in/gm63Qtgt?trk=public_profile__reactions-text"116 },117 ],118 "people_also_viewed": [119 {120 "link": "https://www.linkedin.com/in/jeffayars",121 "name": "Jeff Ayars",122 "summary": "Caribou Financial, Inc.",123 "location": "Greater Chicago Area",124 "followers": "3K followers"125 },126 {127 "link": "https://www.linkedin.com/in/davidpsingleton",128 "name": "David Singleton",129 "summary": "Dreamer",130 "location": "San Francisco Bay Area",131 "followers": "42K followers"132 },133 {134 "link": "https://www.linkedin.com/in/mosespawar",135 "name": "Moses Pawar",136 "summary": "Apple",137 "location": "Cupertino, CA",138 "followers": "31K followers"139 },140 {141 "link": "https://www.linkedin.com/in/mthiagarajan",142 "name": "Mahesh Thiagarajan",143 "summary": "Oracle",144 "location": "Greater Seattle Area",145 "followers": "25K followers"146 },147 {148 "link": "https://www.linkedin.com/in/jinhuang11",149 "name": "Jin Huang",150 "summary": "heyC AI",151 "location": "San Francisco Bay Area",152 "followers": "17K followers"153 },154 ],155 "similar_profiles": [156 {157 "link": "https://www.linkedin.com/in/rahul-patil-86445540?trk=public_profile_samename-profile",158 "name": "Rahul Patil",159 "location": "Prosper, TX"160 },161 {162 "link": "https://www.linkedin.com/in/rahul-patil-0ab5312a?trk=public_profile_samename-profile",163 "name": "Rahul Patil",164 "location": "United States"165 },166 {167 "link": "https://www.linkedin.com/in/rahulpat13?trk=public_profile_samename-profile",168 "name": "Rahul Patil",169 "location": "Sunnyvale, CA"170 },171 {172 "link": "https://www.linkedin.com/in/rahulmpatil?trk=public_profile_samename-profile",173 "name": "Rahul Patil",174 "location": "New York City Metropolitan Area"175 }176 ]177}You’ll find almost everything that matters in the response.
Step 3 — Use the Data However You Need
As the response came clean in structured JSON, it can be plugged in directly into whatever you’re already using — a CRM field update, a personalized outreach template, an internal dashboard, or a lead-scoring. There’s no need of manual pasting, reformatting, and guessing about field names.
Why This Matter More Than Ever in 2026
Job-switching speed hasn’t slowed down —if anything, as the world changes, job roles evolve even faster. That means the half-life of any stored profile keeps getting shorter, and the cost of relying on that stale data keeps on rising.
The businesses that get the most value out of person enrich in 2026 aren’t the ones doing the most enrichments. They’re the ones being deliberate about which kind of Enrichment fits each situation — fast and broad when volume matters, fresh and precise when a single relationship is on the line.
If you want to try it yourself, EnrichmentAPI offers free credits to get started. Run a single request on a real Profile URL and see exactly what comes back.