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Candidate search, better than ever.
Built to be
fastMMMMMMMMM

Convert your existing candidate database into instant best-fit shortlists. Made for fast-paced recruiters who need immediate results and fill roles.

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Candidates

Two-side Integrationswith your favorite ATS & CRM
[01]. How it works

Describe the talent you're looking for.

Senior search relevance engineers in Europe with Python, ranking infrastructure, 8+ years,and B2B SaaS discovery experience for product-led teams.
All Candidates4,822
Mixed
Job Title
Location
Skills
Years of Experience
Industry
Internal
97% Match
Maya Chen
Maya Chen
Staff Search Engineer
Berlin, GermanyLinkedInGitHub
HubSpot
Staff Search EngineerHubSpot
Technical University of Munich
Technical University of MunichQS RankingRank #28
M.Sc., Computer Science
Short summary
Last synced: 12/05/2026
97% match across 5 criteria. Currently Staff Search Engineer¹ in Berlin, Germany¹. Strong skill match for search relevance, Python, and client discovery¹. Has 9 years of experience¹ and works at HubSpot².
Perfect matchSearch relevance, Python, 8+ years, and B2B SaaS discovery all line up.
Internal
84% Match
James Okonkwo
James Okonkwo
Senior ML Engineer
London, United KingdomLinkedInGitHub
Workable
Senior ML EngineerWorkable
Imperial College London
Imperial College LondonQS RankingRank #2
M.Eng., Computing
Short summary
Last synced: 10/05/2026
84% match across 5 criteria. Currently Senior ML Engineer¹ in London, United Kingdom¹. Strong match for ranking models, Python, and search infrastructure¹. Has 7 years of experience¹, just under the 8+ target, with marketplace relevance work at Workable².
Experience noteStrong search infrastructure fit, but just under the 8+ year target.
External
78% Match
Sofia Bergstrom
Sofia Bergstrom
Principal Backend Engineer
Stockholm, SwedenLinkedInGitHub
Teamtailor
Principal Backend EngineerTeamtailor
KTH Royal Institute of Technology
KTH Royal Institute of TechnologyQS RankingRank #73
M.Sc., Computer Science
Short summary
Last synced: 08/05/2026
78% match from mixed search. Currently Principal Backend Engineer¹ in Stockholm, Sweden¹. Strong evidence for distributed systems, API design, and discovery calls¹. Has 11 years of experience¹; industry fit is Teamtailor is an adjacent SaaS talent cloud², not exact B2B SaaS search platform experience.
Adjacent fitStrong backend profile, but talent-cloud context is adjacent to B2B SaaS search.

AI sourcing first.Recruiting context right behind it.

Lope gives the search brief a real memory: candidate data, source signals, interview evidence, and fresh movement when the market changes.

[02]. Features
[02]. Features

Recruiting,without limits.

Book a demo
Portrait of enriched candidate Amira Rahman
Amira RahmanAI search lead / Shortlisted
LinkedInGitHub

Profile coverage

94%LinkedIn · CV · GitHub

Used in ranking

Structured search inputs

Lope turns raw profile data into fields AI Scout can filter and rank.
Core skillsSeniorityLocationDomain fit
Enrichment pipeline
3 / 5
  1. LinkedIn
  2. Company
  3. GitHub
  4. CV
  5. AI index
Candidate in an interview callInterview live

Speaker-aware notes

38 minTranscript ready

Recruiter

Tell me about the search platform you scaled last year.

Candidate

We cut matching latency while keeping explainability visible.

Summary pinned to candidate

Strong search leadership, crisp client communication, and clear tradeoff thinking.
Tracked candidate James Okonkwo

James Okonkwo

Past finalistTiming changed today
Joined Nori LabsNew VP Product role after 14 months tracked
Promoted to Engineering DirectorPast finalist now matches leadership brief
Warm shortlistRevisit 12 candidates
linkedin.com/in/promising-profile
LinkedIn profile being captured into Lope
Elena LiSourcing target on LinkedIn

Lope extension

Add candidateEnrich next
Next Profile
James Okonkwo

James Okonkwo

in
Senior ML Engineer
GeneralExperienceEducationTechnicalSkillsCVNotesTracking

Experiences

TOTAL EXPERIENCE9 yrs 7 mos
AVERAGE TENURE4 yrs 10 mos
CURRENT TENURE4 yrs 3 mos
Workable7 yrs 3 mos
Senior ML EngineerMar 2022 – Present · 4 yrs 3 mosLondon, United Kingdom
ML EngineerApr 2019 – Mar 2022 · 3 yrs 0 mosLondon, United Kingdom
Teamtailor2 yrs 4 mos
ML EngineerDec 2016 – Mar 2019 · 2 yrs 4 mosStockholm, Sweden

Candidate enrichment

Turn scattered signals into profiles recruiters can actually search.

Bring LinkedIn, GitHub, CV context, and structured fields together so the next search starts with richer evidence instead of manual cleanup.

Candidate enrichment

Turn scattered signals into profiles recruiters can actually search.

Bring LinkedIn, GitHub, CV context, and structured fields together so the next search starts with richer evidence instead of manual cleanup.

Portrait of enriched candidate Amira Rahman
Amira RahmanAI search lead / Shortlisted
LinkedInGitHub

Profile coverage

94%LinkedIn · CV · GitHub

Used in ranking

Structured search inputs

Lope turns raw profile data into fields AI Scout can filter and rank.
Core skillsSeniorityLocationDomain fit
Enrichment pipeline
3 / 5
  1. LinkedIn
  2. Company
  3. GitHub
  4. CV
  5. AI index

Interviews

Keep interview context with the candidate, not in someone's notes.

Lope's meeting workflow captures conversations, transcripts, and takeaways where the team can reuse them during evaluation.

Candidate in an interview callInterview live

Speaker-aware notes

38 minTranscript ready

Recruiter

Tell me about the search platform you scaled last year.

Candidate

We cut matching latency while keeping explainability visible.

Summary pinned to candidate

Strong search leadership, crisp client communication, and clear tradeoff thinking.

Candidate tracking

Know when the person worth revisiting has moved.

Track candidates you care about and surface meaningful profile changes when timing suddenly gets interesting again.

Tracked candidate James Okonkwo

James Okonkwo

Past finalistTiming changed today
Joined Nori LabsNew VP Product role after 14 months tracked
Promoted to Engineering DirectorPast finalist now matches leadership brief
Warm shortlistRevisit 12 candidates

Chrome extension

Capture promising LinkedIn profiles before the tab disappears.

The extension lets sourcing flow from browsing into Lope with less copy-paste and less tab archaeology.

linkedin.com/in/promising-profile
LinkedIn profile being captured into Lope
Elena LiSourcing target on LinkedIn

Lope extension

Add candidateEnrich next

Candidate sheet

Give every candidate one place for evidence, context, and next steps.

Open a profile and see the CV, interview notes, links, source history, and pipeline context without hunting through tabs.

Next Profile
James Okonkwo

James Okonkwo

in
Senior ML Engineer
GeneralExperienceEducationTechnicalSkillsCVNotesTracking

Experiences

TOTAL EXPERIENCE9 yrs 7 mos
AVERAGE TENURE4 yrs 10 mos
CURRENT TENURE4 yrs 3 mos
Workable7 yrs 3 mos
Senior ML EngineerMar 2022 – Present · 4 yrs 3 mosLondon, United Kingdom
ML EngineerApr 2019 – Mar 2022 · 3 yrs 0 mosLondon, United Kingdom
Teamtailor2 yrs 4 mos
ML EngineerDec 2016 – Mar 2019 · 2 yrs 4 mosStockholm, Sweden
[03]. Lope MCP

Your recruiting data, inside the AI tools your team already uses.

Lope MCP connects your candidate database, pipeline, and interview context to AI assistants that support the Model Context Protocol. Authenticate once and your workspace is reachable from any MCP-compatible tool.

Claude
Claude
ChatGPT
ChatGPT
Gemini
Gemini
Codex
Codex
Lope MCP
Cursor
Cursor
Grok
Grok
DeepSeek
DeepSeek
Antigravity
Antigravity
  • WORKS ANYWHERE

    Any AI, one connection

    Install Lope MCP once in Claude, Cursor, ChatGPT, Gemini, or any MCP-capable assistant. Candidates, pipeline, and interview context are available without switching tabs.

  • OAUTH SECURED

    Scoped access, not shared keys

    Each connection is a workspace-scoped OAuth grant. Your AI sees only what you approve, with no API keys to copy or rotate.

  • LIVE PRODUCT API

    Real data from your workspace

    Candidates, jobs, shortlists, projects, and interview transcripts surface through a single endpoint at mcp.withlope.com. Live data, not a demo dataset.

Compatible with any client that supports the Model Context Protocol. Install once, authorize your workspace.

MCP setup guide
[04]. Time back

See how many hours AI Scout gives back.

Adjust the sliders to estimate the screening time AI Scout removes from each search.

Your current workload

Today: searches × candidates per search × minutes per candidate.

With Lope: AI Scout ranks profiles by fit, so we model recruiters reviewing the top 50% of the pool at the same minutes per candidate. The other half is filtered out by ranking, not skipped on judgment.

The dollar figure multiplies hours saved by your loaded hourly cost. No claims about win rate, placement velocity, or revenue uplift.

You get back

TodayScreening every candidate by hand38 hrs / mo
With LopeFocused review of ranked candidates19 hrs / mo
19 hrshours back / month
225 hrshours back / year

~ 5.6 recruiter work-weeks reclaimed per year.

$1,125freed up per month at your hourly cost.

Directional estimate. Your recruiters still decide who moves forward.

[05]. FAQs

The honest answersto what every recruiter asks before committing.

Lope is built for recruiting agencies and talent teams that need faster candidate discovery without throwing away the data they already collected.

Yes. Lope is designed to sit alongside your existing systems, enrich candidate context, and make the database easier to search and reuse.

AI Scout can search candidates already in your database, expand to external sourcing, or combine both in a mixed search when a role needs a wider field.

Enrichment brings profile signals such as LinkedIn, GitHub, CV context, and structured fields together so searches and reviews start from better evidence.

Lope keeps interview context close to the candidate profile, including transcript and summary workflows that make team handoffs easier.

You can follow candidates worth revisiting and surface meaningful changes, such as a new role or promotion, when that timing may matter for a search.

Yes. The Chrome extension helps move a promising LinkedIn profile into Lope quickly and can hand it off to enrichment without the copy-paste detour.

[06]. Changelog

Every release,one recruiter workflow improved.

Interview bots

Lope joins Google Meet and Teams calls automatically. Shortly after the interview ends, the bot transcribes it so you can ask questions based on the conversation.

MAY 27, 2026

External & mixed search

AI Scout now searches 800M+ LinkedIn profiles. Mixed search ranks external candidates alongside your internal database in a single shortlist.

APR 30, 2026

Candidate tracking

Follow candidates and get notified about company moves, promotions, new skills, certifications, and headline changes — via in-app alerts or daily digests.

MAR 18, 2026

Candidate sheet

One place for every signal: LinkedIn experience, GitHub repositories and commits, CV data with edit capabilities, and interview context — all in a single view.

FEB 10, 2026
View all

Time to ditch every search that starts from scratch.

See how Lope turns internal, external, and mixed candidate search into explainable shortlists your team can trust.

Book a demo
Lope

Candidate search, better than ever. Lope helps recruiters turn their database into instant best-fit shortlists.

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Built for recruiter-first search.