Serving as Chief Technology Officer (CTO) and co-founder at Tracer.
This AI-driven software consolidates, governs, and presents complex information clusters to accelerate access to more tangible business insights. Prior to taking on the role of Chief Expertise Officer at Tracer, Leighton served as Director of Client Insights at SocialCode, as well as Vice President of Engineering at VaynerMedia. With a career marked by innovation, he has been at the forefront of shaping the advertising technology landscape, boasting credits that include developing the industry’s first-ever Snapchat ad and providing strategic counsel on business APIs to several of the world’s leading platforms. Leighton earned a degree from Harvard University in 2013, with majors in Computer Science and Economics.
A breakthrough idea emerged a decade ago. My childhood friend suddenly picked up the phone and called me one Friday evening. While working on a project for one of his clients, he diligently gathered diverse information across multiple social media channels. With that possibility in mind, he sought my expertise, given my experience in software development. When I was first introduced to my current business partner, Jeff Nicholson.
The expenditure on these campaigns was increasingly surpassing the capacity of the software tracking the funds. The burgeoning field of knowledge science boasted an impressive array of applications.
We developed cutting-edge analytics software capable of catering to the complex demands of increasingly large-scale and sophisticated media initiatives. As we navigated the development process for the downside, we crafted a cohesive framework comprising crystal-clear steps to facilitate the integration of disparate knowledge and its contextualization. Here’s the improved text:
We discovered our method could be applied to any knowledge base, beyond just marketing, and thus Tracer was born – a cutting-edge AI-powered tool that streamlines complex data sets, providing faster and more actionable insights for informed business decision-making.
By leveraging automation, we’re empowering organizations to redefine what it means to be data-driven, streamlining the process of integrating diverse data sets across features, and delivering cutting-edge business intelligence through intuitive reporting and visualization tools. Does this imply connecting granular sales insights with your marketing CRM, HR data to revenue trends, and countless other features?
Let’s outline analytics for answering an enterprise query through software. There are two distinct approaches currently evident in the immediate landscape.
- The primary objective is to acquire a vertical software application. While many CFOs do use NetSuite, for others, it may also be SAP Concur, Intacct, or Microsoft Dynamics. The Customer Relationship Management (CRM) platform is likely to be Salesforce, given its market dominance and widespread adoption in various industries. While vertical software programs have their advantages, being end-to-end, highly specialized, and easily deployable are just a few benefits that make them particularly appealing in today’s tech landscape. The inherent constraint of vertical software programs lies in their very nature – they are designed for a specific industry or function. Therefore, when integration with another system is required, such as connecting Netsuite to Salesforce, a significant hurdle arises, effectively forcing you back to the drawing board. one. While vertical software may be comprehensive, it’s indeed limited in its applicability.
- Purchasing horizontal software programs provides an alternative solution for achieving desired outcomes. Three distinct software programs are likely needed: one designed specifically for knowledge ingestion, another for efficient storage, and a third focused on thorough evaluation. Horizontal software programs are advantageous because they can manage and integrate a wide range of data and applications seamlessly. You can definitely ingest, store, and analyze both your Salesforce and Netsuite data through this pipeline. Collective data management requires unity in action, with all components functioning within a single framework. While horizontal software offers flexibility, its scope is limited, as “nothing works out of the box.”
By developing a platform that seamlessly integrates key scientific disciplines, we enable users to tackle complex issues without requiring extensive engineering expertise or technical infrastructure. It’s versatile and full. Tracer emerges as the market’s most potent platform, boasting unparalleled utility-agnostic and end-to-end capabilities.
Scaling is crucial in today’s world, and it has always been a top priority for Tracer from its inception. By harnessing the most advanced technologies, we efficiently navigate the extensive scope of knowledge, capitalizing on existing expertise to minimize duplication of effort and optimize our approach. We’re delighted with the robust infrastructure we’ve built, and we’re equally transparent about our achievements. Our organization’s structure and program information is readily available for viewing on our website.
We’re suggesting that the internal engineering teams within companies aren’t capable of replicating our work because it’s not their responsibility to do so. The framework showcases its eco-friendliness, robustness, and modularity, enabling our organization to adapt seamlessly to the ever-changing landscape.
Strategic initiatives require a focus on core competencies, prompting numerous colleagues to relinquish engineering assets and redirect resources towards more far-reaching objectives. They utilize Tracer’s framework to achieve a successful outcome. Implementing a database does not necessarily address enterprise-level concerns. Having a well-designed ETL (Extract, Transform, Load) pipeline does not automatically guarantee that all enterprise-level data integration questions are answered. What ultimately matters is the potential uses and applications of that infrastructure once it’s fully developed. We created Tracer as a rapid path to finding answers, streamlining the process for you.
Structured knowledge is crucial for AI, enabling guided human interaction and ultimately fostering efficient outputs through informed collaboration. In today’s interconnected ecosystem, we possess unparalleled tools for extracting insights from unstructured data sources – such as documents, images, videos, and more – that were previously inaccessible.
By providing a platform, we enable individuals familiar with underlying datasets to contribute supplementary context when relevant information becomes available. Unorganized information transforms into coherent insights → organized data structures → our proprietary contextual framework → AI-generated results. We facilitate a collaborative dialogue by sitting in between parties, enabling more effective suggestion sharing, and allowing for guided interventions when necessary.
Without an adequate framework to organize and manage unstructured knowledge, the problem persists? As data is infused into the AI model, it generates solutions with limited scope for refinement within the confined digital environment.
To identify the most impactful content in a media marketing campaign, we should first define what constitutes “impactful”. Is it engagement rates, conversions, or brand awareness? Once our goals are clear, we can analyze the available data and metrics to determine which pieces of content are driving the greatest results. This may involve A/B testing different formats, targeting specific audience segments, and optimizing for performance over time. By focusing on what’s working best, we can optimize our campaign to maximize ROI and drive meaningful connections with our target audience. To streamline tracking and analysis, Tracer could leverage AI-driven tools to provide metadata insights on all content executed across advertisements. The system could leverage AI to derive final-mile analytics by processing a highly structured dataset and generating insights for optimizing delivery routes.
Our platform enables users to forge links between media knowledge and datasets, thereby preserving outcomes that are more finely detailed, specifically defining “impactful” and clarifying categorisations made by AI. By streamlining and productizing the process, we’ve effectively eliminated the blank slate. Without AI integration, tasks within Tracer’s workflow could significantly increase for the human user. Without Tracer, AI cannot achieve an equivalent level of response quality.
You can consider Tracer across three core product categories: Sources, Content, and Outputs?
- Sources is a software solution that automates the process of ingesting, monitoring, and quality assurance for diverse knowledge sources.
- Context is a drag-and-drop semantic layer that enables the organization of ingested knowledge into meaningful groups.
- Outputs are a place where you can respond to business inquiries with knowledge contextually applied.
At Tracer, we recognize AI not as a replacement but rather as another technology that can complement the three classes by expanding the scope of automation.
For instance:
- Leveraging artificial intelligence (AI), we aim to develop innovative API connectors that bridge the gap to extensive tail knowledge sources inaccessible via our existing associate catalog.
- Leveraging artificial intelligence to preprocess metadata prior to applying tagging guidelines. Consolidating diversity in publication titles across languages.
- Leveraging AI as a drop-in substitute for traditional dashboards, where the enterprise use case is more exploratory rather than a fixed set of key performance indicators that need to be reported on repeatedly?
- Artificial intelligence enables us to accomplish most of these tasks through intuitive and user-friendly interfaces.
Tracer is a trailblazing platform that consolidates the outputs of numerous aggregators, streamlining information and insights for seamless access. Companions will rely on our team for specific roles within groups and features, or leverage our capabilities in cross-functional business intelligence. With Tracer, a key advantage lies in its versatility: whether you’re utilizing our platform to inform strategic decisions about your media spend and creative assets, or crafting customizable dashboards that seamlessly integrate diverse metrics spanning supply chain to sales – and everything in between – the underlying architecture remains consistent.
Organizations that once relied heavily on our capabilities within a specific department are now expanding those same functions across the entire enterprise. Our major clients are comprised of former senior media executives and company partners who have recently expanded their scope to partner with CIOs, CTOs, data scientists, and business analysts across the organization. As we continue to build out our tools, we’re focused on creating a flexible infrastructure that can adapt to growing demands and diverse user needs, ensuring our core technology remains scalable, versatile, and easily accessible to non-technical users.
