Recent Feature Updates


Some of you are already familiar with Referable, and others not. I’ve added a number of my 1st degree LinkedIn contacts to our mailing list to share this information privately rather than all over the web. At least while we are in startup phase we want to fly somewhat under the radar.

Here is a quick look at some of the more significant updates made over the Northern summer, in no particular order:

1. Referral dashboard – added columns to track employee engagement including (currently) In Review, Sent, Rejected, Recommended. The ability to filter this dashboard by individual employees has always been there, but we will add more features in time to help with management and reporting.

2. Add Candidates – reintroduced the ability to upload Candidates by their social URL and automatically match to employees. This was done by changing our data provider to one with a better API. Many users wanted to use the app for what are commonly known as “back door” checks, but we caution against doing this with applicants as there are ethical implications. Referable is designed to be far more powerful than reference checks.

3. Match to Company – Rebooted matching by specific Company on the main dashboard. This is highly technical and we wanted to delay this, but due to many requests we have fixed this so that a new tab opens to show the matched candidates.

4. Boolean Search Filters – Added ability to filter matched candidates using Boolean strings. This will greatly reduce the quantity of matches down to a manageable and more relevant few and was added as a result of user requests. This filtering is more powerful than the simple search filter and helps recruiters to zero in on their target. For example, (telco OR telecom OR 通信) or (Softbank OR ソフトバンク), etc. Eventually we will automate the translations where online candidate profiles can be in English or local language.

5. Shortlisting – We added the ability for recruiters to do a double check and Short List the high probability candidates by checking a bell icon. This feature not only enables the recruiter to sit down with hiring managers to determine who to pursue, but also helps reduce employee “churn” rates where the recruiter sends low probability profiles. In such cases employees tend to drop out and stop collaborating.

6. Japanese character bug – Fixed a bug that occurred when uploading candidates with Japanese name characters encoded in their LinkedIn URL. For example a profile URL such as this “/元-上野-5867999b/” would become 文字化け “%E5%85%83-%E4%B8%8A%E9%87%8E-5867999b/” and be rendered unworkable. Now any LinkedIn URL will work regardless of encoding.

What is coming up?

In no particular order here are some of the things we have planned:

Automating Jobs – We want to get away from users being required to manually enter alternative job titles to capture relevant candidates. We will add generic jobs by Function, and match by Seniority Level. From there our users will be able to use the Boolean filtering to narrow the search down to more specific Skills or Experience. For example, a “Sales Rep” Job will match all individual contributor “Sales Reps” and the user can then filter to a specific requirement such as “Telco” or “Manufacturing” or “NTT OR KDDI”, for example. We will need to test what works best and look forward to gathering user feedback.

AI Matching – We will be adding an AI automation to make the matching smarter. The goal is to make the matching so easy that recruiters only need to focus on collaboration. Stay tuned as our techies have a few ideas on how we should approach this.

Location (by Country) – We had strictly focused on Japan but are gathering requests to support other countries around the Asia-Pacific region. As such we are planning to build up Singapore, Australia and New Zealand, as well as South Korea, Taiwan, and other Asian countries before shifting westward toward India and Bangladesh. The US and EMEA will come later.

Profile Enrichment – As we complete core features of Referable AI we will begin adding enrichment of profiles, including any available email or phone contact details (if available), social media profiles (e.g. Facebook and Github), and any other public information that may be of value.

Save Filter – We will be allowing users to save commonly used filters and use this information to support automation.  

Talent Intelligence – While we have a basic dashboard showing age, tenure and gender diversity, we will build this out to provide more value. Based on previous experience selling “talent mapping” RPO services, we believe there is significant value to be gained for HR professionals when comparing their workforce to industry peers.

Training Videos – We will need to create “dummy” data before we do this, but user tutorials are on our mind.

Let us know if you’d like to see more!


REMINDER: For users actively working on the platform, please share your feedback or feature requests to feedback(at)

Scroll to Top