Accompany creates AI driven products for strengthening your relationships and finding new prospects, built on the largest database of senior decision makers in the world.
Our Relationship Manager app consists of a news feed, a calendar, and hundreds of millions of people and company profiles. We layer your contact graph onto all of this in order to provide you with constant, contextual insights into the relationships that matter the most to you. Our users use it to prepare for meetings, to reach out to contacts at just the right time, and to research prospective companies and people that they should get to know.
Our Prospector service provides curated recommendations for hidden high-potential prospects within any demographic, along with guidance on when and how to connect with them. Our enterprise customers use it to sell more effectively, retain customers longer, and recruit new employees more successfully.
The Team. We have a small but mighty team of engineers who are exceptionally talented and all low ego. Many of our engineers have founded their own startups prior to joining Accompany, in domains ranging from crowdfunding to dynamic pricing to real time document editing. Others have built significant infrastructure at larger companies like Google and Amazon. We draw on this well of experiences every day as we collaborate on our own product, tech stack, and business strategy.
We're all highly collaborative, committed to building great things, and consider it a privilege to learn from each other every day.
Ownership and Autonomy. In order for our small team to accomplish so much, every engineer is entrusted with significant ownership of their work and the autonomy to deliver it as they deem fit. You'll never feel isolated or alone, but you will have significant independence and creative room to build your features and chart their course to production.
Revenue and Growth. We are in a very exciting point in our trajectory where we have identified product market fit and something our customers truly want, but there are still significant unanswered questions and exciting discoveries to be made as we race to serve our growing list of customers. We think the biggest problems we’ll solve are in front of us, not behind us.
Within 3 months of starting to sell, we’re already closing 7 figure deals with Fortune 100 companies, and are doing everything we can to scale and automate our system in order to meet customer demand.
Our engineering team is fairly flat, and we optimize for both personal productivity and team cohesion. In practice, this means that ad hoc subteams form and disband as needed. Each member of the team develops areas of significant ownership in our codebase and knows that code inside and out — we've found this maximizes productivity and minimizes coordination time. That said, no part of the codebase is off limits to anyone, and we each maintain breadth by working on code outside our focus area.
We hold a daily standup where each member of the team shares their status. This helps keep everyone on the same page, surfaces issues quickly, prompts collaboration, and enforces accountability.
Our code is on Github and all work is submitted as pull requests which are code reviewed by somebody else on the team prior to merging. We deploy code multiple times per day and rely on comprehensive test coverage to ensure this goes smoothly.
Communication across the team is done over Slack and of course in person, which is fairly easy as we all sit (or stand!) in the same space.
We are building a proprietary data platform that scours billions of pages on the web and uses intelligence and machine learning to create the richest, most real-time profiles in the world for hundreds of millions of people and tens of millions of companies. How do you decide which parts of the web to crawl? How do you extract structured data from unstructured text? How do you efficiently keep this data up to date?
With this people and company data, we're building services that can operate at scale to deliver personalized data and competitive insights. How do you store and query all this data efficiently? What unique deductions and insights might you make from it?
And to display this personalized data and these competitive insights, we're building fast, powerful apps on mobile and web that are a delight for our customers to use. How do you show so much data while still keeping a UI simple and easy to use? How do you build powerful functionality while staying extremely performant?
Accompany is rich with exciting technical challenges, most of which have yet to be solved here and some of which have yet to be solved anywhere!
We have built a system that extracts people information from the web in a fully automated way. Crawling websites, we detect common patterns among pages, and use that to reliably extract people information.
We gather information about people from many different sources (e.g. the web, and various structured sources). Before showing it to users, we need to cluster our raw data to combine them into the description for individual people. This is challenging both as a data problem (are two John Smiths the same?) and as a scalability problem dealing with over 250m clusters.
Part of our system is a news feeds that is customized to contain mentions of our user’s contacts. For this, we needed to build a system that determines which people and companies are mentioned in news articles. A special-purpose-built C++ solution allows us to do this efficiently for millions of articles a day and hundreds of millions of people to match.
- No assholes. If you are one, even a really smart one, don't bother. We can sense you a mile away.
- True empathy and passion for the user. Users are the reason why we get up, go to work, and drink so much coffee. We never forget it for a minute.
- Move fast, fix fast. We're building a rocketship. Be the fuel.
- Take genuinely good care of each other. Everyone on Team Accompany is essential. Every. Single. Person.
- Take responsibility—it's your company. Make us better. Push us harder. Tell us what we're doing right and, better yet, what we're doing wrong.
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