At Relink, we use machine learning to match people to jobs. Learn more about the market for HR Tech and how we're building our candidate scoring and recommendation engine to transform the recruitment process!

1. We use machine
learning to match
people to jobs
relink
www.relinklabs.com

2. relink
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:

3. relink
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
11
FTEs
7
Engineering
4
Operations
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

6. 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
relink

7. 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
relink

8. 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
relink
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

10. This engine
belongs in two-
sided
marketplaces
…where matching people to
jobs and companies to hires
are core
relink
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
BNEUR
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

14. HR chat bots and
applicant
interaction
solutions
“AI” fuelled talent
market places
Social data sourcing
tools & HRM
relink
Competitive landscape
Pure job
recommendation
APIs

15. relink
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

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Contact us:
Relink: sh@relinklabs.com
SEED Capital: ah@seedcapital.dk
www.relinklabs.com