Relink - Public Pitch
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We use machine learning to match people to jobs relink www.relinklabs.com
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Transcript of Relink - Public Pitch
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We have a machine learning technology, capitalising on the major trends in this large TAM We are raising syndication capital to grow faster
Lead investor: SEED Capital
“Awesome team, great execution, cool tech, huge market and innovative value prop – Relink thick all of your boxes and we’re super excited about being a part of their journey” Alexander Horten, SEED Capital
I am really impressed with the technical leadership and their stack. This is a “best in its class” company. Relink have a stack based on Spark and Mesosphere DCOS. The solution is extremely well designed and realised. From a commercial perspective I find that RelinkLabs have proven their business case quite convincingly thought the customers they already serve. Excerpt from Technical Due Diligence report
SEED Capital will open books on Due Diligence to facilitate the deal. A few take aways:
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Data Science
Our core skills
Bjarne Ø. Fruergaard PhD Machine Learning Head of Data Science
Machine Learning
(Data) engineering
Sales
Scaling
Serial entrepreneurs
Ståle F. Husby BA International Business Founder & CEO
We have experience from
Plapre
MediaPlanet
AUKA
Ad Form
Castle.io
UBER
finn.no
EY
11FTEs
7Engineering
4Operations
Our team
Check out our culture and engineering manifestos
Anders N. Bakke MA Computer Science co-founder & Strategic Partnerships
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The problem…
Manual matching of profiles to jobs is mundane, time consuming and inefficient.
And honestly - there are limitations to the speed and quality of human data processing capabilities…
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The problem, a real life example…
Lets say you run a 1000 people company. Aiming to grow 15 % and churning 15 %
…to hit that target you need to hire 300 people per year
…in average you will look at 118 profiles for each hire. Thats 35 400 CVs that you will manually asses to decide if you want to start a process or not. Adding up to 177 CV’s a day
….you will in average spend 60 seconds per CV. Thats 74 days of reading CV’s, almost four months, of reading. And thats before you get to do your real job - interacting with people.
1000
300
35 400
74
We have trained a machine to do this in seconds
Input: Job description & profile data
Output: Data products describing job, and company specific transferability of skills, education and experiences
…and the machine is constantly learning and improving based on feedback data
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Think of it as a candidate scoring and recommendation engine -
…understanding the match between job descriptions and profiles
… and constantly learning from feedback data flowing in from our partners
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Millions of structured CVs with job titles, educations and skills
We map job titles, educations and skills to "golden values" using frequencies, text matching and publicly available ontologies
Large graphs connect golden values with counts on edges. Using cluster analyses and page rank, we are able to associate entities with each other
These graphs we use to disambiguate educations, titles and skills And they are the key relationships to create augmented contexts around job descriptions and candidates
Semantic meaning and relationships power our matching capabilities
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Under the hood
I am really impressed with the technical leadership and their stack. This is a “best in its class” company. Relink have a stack based on Spark and Mesosphere DCOS. The solution is extremely well designed and realised. Excerpt from Technical Due Diligence report
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Under the hood - knowledge graphs
This engine belongs in two-sided marketplaces
…where matching people to jobs and companies to hires are core
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Job opportunities
Relink recommendation
technology (via API) Profiles
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The value of feedback data - “the magic sauce”…
As we scale our network of customers we harvest feedback data from recruiters reacting to our recommendations. This enables our models to constantly learn and improve.
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The market for HR technology is large, growing fast with increasing M&A activity…
The recruitment technology market USD 15B
…expected to grow 5x over next years
with high, increasing m&a and investment activity
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Sizing up the market - current model (top - down)
0
10
20
30
40
50
60
70
Today Expected x5
BN
EU
R
HR TECHNOLOGY - TOTAL MARKET
Workforce
Compensation
Preformance
Recruiting
Learning
Current TAM: 2, 5 bnUSD
Current SAM: 1, 25 bnUSD
Y potential revenue: mEUR 6 (0, 5 % of SAM) - 100 ATS customers with average ARR tEUR 60 - expected to grow 5X
HR chat bots and applicant interaction solutions
“AI” fuelled talent market places
Social data sourcing tools & HRM
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Competitive landscape
Pure job recommendation APIs
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We build technology, and fuel application / product companies with recommendation capabilities
What makes us different?
API distribution provides network effect and (cost) efficient scaling and feedback data
We use ML to create our own, proprietary knowledge graphs and data products. We do not sell social data or sourcing
Our augmented job context replaces tedious keyword search
