Impedance Matching for DevRev

 

New, innovative products like DevRev are fascinating. They involve immeasurable quantities of hard work by lots and lots of people to get to market. But once you get there, how do you make it Super Easy(™) to communicate and make your audience understand the fruits of all that work? That. Is. Not. Easy.

This past week I have spoken to so many people, friends and contacts old and new, about this fascinating new adventure that I have embarked on. And I have felt like I really had to iterate multiple times to better tune the message of what it is that we provide to our customers. Communicate. Fail. Rinse and repeat. Until it works. Until it clicks.

In order to find that “click”, I was thinking of the idea of the Impedance Matching. Those of you that have an engineering background immediately understand: you need to match your messages to the audience that will be receiving it, or else … stuff will get lost :) … Too little detail and people will be frustrated - too much detail and they will be overwhelmed.

So that’s why I started to think about different “levels of communication” for different “levels of audiences” that  would understand different “levels of messages” for our different DevRev offerings. Here’s what I came up with.

Industry level - We want to make work matter. We want to connect builders to customers. We want to help build the world’s most customer centric organisations.

These may sound like different objectives - but they aren’t. Especially for people that have seen the complexities of building digital products in today’s day and age, it will probably ring true. How many software engineers never see the fruits of their work in the hands of a customer? How many of them have actually never seen or heard the voice of their customer, literally? That’s not a very satisfying place to be. What if we could shrink that distance between builders and customers? What if we could give builders and buyers, dev’s and rev’s, a true voice in the conversation?

Company level - We want to solve the problem of Information Asymmetry in digital product building organisations: different teams have different access to different information. This problem is the root cause for many Customer Experience problems: siloed teams lead to a frustrating client experience that effectively limits growth.

Great companies excel at customer focus. They are obsessed with their customers’ success, with the value that they derive from the product - and will walk through fire to help the customer get there. There is no substitute for that - but there are lots of barriers to get there. Information siloes are real, in fact they have gotten worse since the moment SaaS 1.0 made it dead easy for every department to automate their departmental processes with yet-another-cloud-platform. Where did the holistic view of the customer go? That’s right - it disappeared. And with it, so did the truly exceptional customer delight. 

CxO level - We want to offer new growth opportunities, by enhancing the customer experience at a lower cost. This means breaking down silos between tools and teams, bringing the data together, and using the latest Agentic AI technology to automate the automatable.

At DevRev, we make this a reality, today, by integrating the different tools in your different departments in a comprehensive Knowledge Graph that connects all the dots. Using that data, we can offer holistic search that reduces the information asymmetry, automated workflows and analytical capabilities on top of that. Using AI, we automate the time-consuming tasks, and make the cross-cutting information accessible through conversational interfaces. 

Customer Support - we want you to be able to help more customers quickly and efficiently, using the full information that is needed to do so, and leveraging AI assistance whenever possible. 

Leveraging DevRev, customers have seen significant drops in resolution times, much higher call deflection rates, faster customer service and as a consequence, a higher net promoter score. As a result, the company can turn support from a cost into a revenue generator.

Product Management - we want to break down the barriers between devs and revs, and make sure that you have all the information to better tune your development and support resources to your most valuable product parts. 

Understanding what is wanted and needed by your customers is not trivial, especially when you have layers of Chinese whispers standing between the engineers and their customers. With DevRev’s knowledge graph, a holistic customer view becomes accessible and actionable. With AI, we can aggregate requirements and align your resources. We can tune in to the customer voice, and foster long term success.

Head of data - as digital product organisations become successful, as their departments grow, they become more complex. To deal with that complexity, many organisations have implemented departmental tools to optimize departmental processes - and by doing so we have lost the overall picture. SaaS 1.0 has created data silos - we now face a real data integration challenge.

Using patented “Airdrop” technology, DevRev has successfully implemented a bidirectional syncing system for most sources of enterprise data in the cloud. CRM data from Hubspot or SalesForce, Customer Support data from Zendesk, Freshdesk or ServiceNow, Product data from Jira / Github, it all comes together in a fully synced up Knowledge Graph. This repository is searchable and actionable, and can drive new business processes in real time using AI and AI Agents. This will allow us to lever the holistic view on the  data as additional context for better human and AI decision making.

Head of AI - leveraging the potential of AI is on everyone’s radar. Not doing AI is not an option - you do NOT want to fall behind. But how does one operationalise this amazing technology, without spending an arm and a leg and months/years of development time? How do you limit the risk, and ensure compliance? How do you prevent hallucinations and reputational damage? 

Turns out you don’t have to do it all yourself. DevRev has spent hundreds of person-years in design and engineering time to build a product offering that does it for you, fast, and at a much lower cost. Leverage the benefits, but don’t run the risks. We help you implement AI efficiently and effectively, and together we will unlock its potential for your organization.

I am hoping that these messages are a bit clearer. We have an incredible story to tell, but it’s like so many beautiful stories: there is more than one storyline. By tuning the story to the listener, by matching the impedance, I have been trying to make it easier to understand - whatever your background.

Looking forward to many more discussions in the next couple of days, weeks, months to come. It’s going to be an incredible journey.

Rik

Making sense of the news with Neo4j, APOC and Google Cloud NLP

Recently I was talking to one of our clients who was looking to put in place a knowledge graph for their organisation. They were specifically interested in better monitoring and making sense of the industry news for their organisation. There's a ton of solutions to this problem, and some of them seem like a really simple and out of the box toolset that you could just implement by giving them your credit card details - off the shelf stuff. No doubt, that could be an interesting approach, but I wanted to demonstrate to them that it could be really much more interesting to build something - on top of Neo4j. I figured that it really could not be too hard to create something meaningful and interesting - and whipped out my cypher skills and started cracking to see what I could do. Let me take you through that.

The idea and setup

I wanted to find an easy way to aggregate data from a particular company or topic, and import that into Neo4j. Sounds easy enough, and there are actually a ton of commercial providers out there that can help with that. I ended up looking at Eventregistry.org, a very simple tool - that includes some out of the box graphyness, actually - that allows me to search for news articles and events on a particular topic.

So I went ahead and created a search phrase for specific article topics (in this case "Database", "NoSQL", and "Neo4j") on the Eventregistry site, and got a huge number of articles (46k!) back. 

Podcast Interview with Irene Iriarte Carretero, Gousto

This week's guest on our podcast is someone that has been writing and speaking about their use of Neo4j quite a bit. Irene Iriarte Carretero, from Gousto has been writing really cool blogposts,  (like this one) and presenting the story at GraphConnect as well. Here's a recording of her presentation:


Some of her excellent slides are over here:

So it goes without saying that I wanted to interview Irene for the podcast, and at the London GraphTour event, I finally had the opportunity. Here's our chat:

Here's the transcript of our conversation:

RVB: 00:00:03.459 Hello, everyone. My name is Rik, Rik Van Bruggen from Neo4j. And here I am recording a face-to-face podcast, which is the first in a long time. We're at our GraphTour conference in London. And I'm actually here joined by Irene, Irene, from Gousto. And Irene just did a presentation here at the GraphTour about how you guys have been using Neo4j, right?

Podcast Interview with Nicolas Mervaillie, GraphAware

Here's another great interview with a long time Graphista that has done a lot of really interesting work in our French community, and is now having lots of graph-fun at GraphAware: Nicolas Mervaillie. Nicolas has been and still is working on some really cool stuff - so a chat was long overdue! Here's our recording - including a fancy new jingle from PremiumBeat ("Fantastic Voyage", by Olive Musique):

Here's the transcript of our conversation:

RVB: 00:00:03.275 Hello everyone, my name is Rik, Rik Van Bruggen from Neo and here I am again on a Skype call, recording the next episode in our Graphistania podcast. And today I have a-- I would say an oldtimer in our Neo4j community on the other side of this Skype call, all the way from Lille in France, and that's Nicolas Mervaillie. Hey Nicolas, how are you? 

NM: 00:00:26.083 Hey, good morning Rik. Thanks for inviting me.  

The Neo4j Knowledge Graph

A couple of days ago, I wrote a graphgist about creating a true Knowledge Graph for the Neo4j ecosystem. Based on the fantastic Awesome Neo4j resource created by our friends at Neueda/Neueda4j. You can access it in a separate window over here.

In this post however, I will go into a bit more detail about how I went about creating that graph.

Google Spreadsheet is my friend

I mentioned already that I started from the awesome Awesome Neo4j github resource. And while it's a great idea to manage pages etc collaboratively on Github, I can't help but feel like there should be other and nicer ways of structuring that information. So I spent a couple of hours converting that information into a spreadsheet (which is publicly accessible over here):

This sheet contains 

• info about the resource (name and comments)
• the URL where you can find the resource
• info about the author (individual or organisation) that created/manages the resource

So it's a very, very easy graph model:

So all I needed to do was import that sheet into Neo4j. Easy...

Importing the Google Spreadsheet with Load CSV

As we know by know, it's really easy to download a Google spreadsheet as a CSV file, and then it is pretty darn easy to import that CSV into Neo4j with Load CSV. I have two versions of that load script:

• one with different statements that you can execute one by one
• one bigger statement that you can execute in one go.

The result is not a very big graph of course:

And now we can do some nice querying on it - just for fun!

Querying the Neo4j KnowledgeGraph

Obviously there are many different queries that we could run on an interesting graph like this. I have put a couple of them on Github as well. Here they are:

//Find some Authors, Resources and Tags
MATCH p = ((a:Author)--(r:Resource)--(t:Tag))
return p
limit 25

Gives you an initial sample of the graph:

Then we can explore a couple of specific graph neighborhoods:

//Find some Authors, Resources and Tags connected to Rik or Max

MATCH (t:Tag)--(r:Resource)--(a:Author)

where a.name contains "Rik" or a.name contains "Max"

return t,r,a

this gets us this one:

And then we can also "recreate" a spreadsheet-like view of the graph:

//find some resources and authors
MATCH (r:Resource)--(a:Author)
where a.name contains "Rik" or a.name contains "Max"
return distinct a.name as Author, r.name as Resource, r.url as URL, r.comments as Description
order by Author;

This gets us (pitty that the url's don't get hyperlinked like they do on the graphgist):

And then finally, let's look at some pathfinding - always interesting:

//find some paths between books and blogs
match (t1:Tag {name:"book"}), (t2:Tag {name:"blog"}),
p = allshortestpaths ( (t1)-[*]-(t2))
return p

limit 10

As usual, we end up with Michael Hunger again :)) 

So there you go. A first attempt at creating another graph-based knowledge repository for all things Neo4j.  Hope you guys enjoyed that. I know I did :))

Cheers

Rik