Artificial Intelligence | Machine Learning | Natural Language Processing Sleuth | Track and Improve Engineering Efficiency


Enterprise, Software Delivery Performance, Accelerate Metrics, Automation Tools, Generate Insights San Francisco, California, United States

Sleuth

Artificial Intelligence | Machine Learning | Natural Language Processing


Sleuth | Track and Improve Engineering Efficiency

Sleuth

Software Delivery Performance, Accelerate Metrics, Automation Tools, Generate Insights


San Francisco, California, United States

Sleuth is a mission control software for teams doing Continuous Delivery. It provides centralized visibility into software delivery performance and progress, plus automation that empowers developers to make frequent deploys easier and less stressful. Sleuth's metrics tracker gives managers an accurate and ongoing picture of their project's delivery performance as measured by DORA / Accelerate metrics. Sleuth's deployment tracker and automation tools help developers own, coordinate, verify, and streamline deploys, and ultimately improve on the metrics. It works by integrating with source control, CI, feature flag, monitoring tools, and more, to track entire cycle times from issues created all the way to production deploys, and generate insights on how your projects are progressing and performing.

Track team-based metrics studies have proven to impact software delivery performance: Accelerate / DORA metrics. Sleuth is designed to accurately track DORA metrics and provide the context developers can use to improve on them. Sleuth tracks DORA metrics by way of tracking deploys. Developers use Sleuth to get a complete view of current and upcoming deploys, and the impact of deployed releases - so they can understand what's shipped, shipping, and needs fixing.

Most metrics trackers can't accurately track Deploy Frequency and Change Lead Time, because they use source control data to infer when deploys happen. They also can't properly track Change Failure Rate or MTTR, because they don't collect any data on the impact and health of deployed code. In contrast, Sleuth tracks your entire cycle time, from issue creation to production deploys and rollbacks. Sleuth helps teams improve on DORA metrics by making deployments less stressful for developers. It gives developers clarity and control over deploys, which in turn gives them the confidence to ship faster. Less time spent stressing about deploys means more time for wr

 

B2B

1 to 25

N/A

N/A

Scaling Up

2016

 
 

Computer Software

N/A

Increase Productivity
Increase Efficiency

 
 

Service

Yes

Active

 
 

   Machine Learning
   Natural Language Processing
   Automated Planning and Search


Automated Root Cause Analysis

Automated Root Cause Analysis


Text

Text

Structured

Structured

 

   Software


AWS

AWS

Azure

Azure

Google GAE

Google GAE

Graph QL

Graph QL

CSS & Javascript

CSS & Javascript

API REST

API REST

NoSQL

NoSQL

GraphDB

GraphDB


Machine Learning Algorithm

Machine Learning Algorithm

Deep Learning Algorithm

Deep Learning Algorithm

 
 

3

1

$22M

Company was founded 2016 and it took almost 6 years (May 2022) to raise first external round

 
 

Date

Round

$ Raised

Investors

05/06/2022

Series A

$22M

Felicis Ventures, Menlo Ventures, CRV

Date : 05/06/2022

Round: Series A

$ Raised: $22M

Investors: Felicis Ventures, Menlo Ventures, CRV

 

Investors

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Dylan Etkin

Dylan Etkin
Founder & CEO

Michael Knighten

Michael Knighten
Founder & COO

Don Brown

Don Brown
Co-founder/CTO

Alexander Leeds

Alexander Leeds
Co-Founder

 
 

Potential Customers

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