At this point most everyone in the technology world has heard of Palantir. Their solutions are in use across multiple industries and government agencies. But Palantir continues to evolve in capability and with the current release of their platform they are bringing together data, analytics and visualizations in ways that can add significant value to end users and the enterprise missions they support.

To get an update on the latest capabilities we sought out an interview with Ryan Beiermeister, the lead product manager of Palantir’s Gotham platform. This post captures insights from that interview.

In Ryan’s seven years at Palantir she has succeeded at multiple functions in the firm, including key roles like customer facing deployment strategist, which required her to bring knowledge of all Palantir capabilities to customer challenges. She also helped ensure product developments were rapidly and accurately communicated to customers via product marketing. For the last 4 years she has been the product manager of Palantir Gotham, the company’s flagship platform. In this role she leads engineers, developers, designers, and planners in continuous development of new capabilities focused on customer success. In the interview I was struck by Ryan’s leadership style and vision and will be making that a topic of a future post. For now we will focus on Palantir Gotham and the new Titan release.

Gourley: Ryan can you tell us what Palantir Gotham is and how it originated?

Gotham is Palantir’s core Operations and integrated Intelligence platform — honed over a decade of partnership with military, civil, and intelligence communities. We built Gotham because we deeply believe in empowering institutions to make data-driven decisions to counter threats and abuse. The challenge, however, is that as our world accelerates — the data our organizations depend on is ever-increasing in volume and complexity, while response times and human resources narrow.

Palantir’s goal is to provide clarity amongst this storm — to build technology so that organizations are supercharged, not hindered, by Big Data. Gotham turns this data into knowledge: integrating an organization’s disparate datasets, mapping them onto a common ontology, and layering analytical tools for users to generate and share insights. This is all backed by powerful security frameworks and fine-grained access controls — creating an expressivity that no other data platform has — so that users can securely collaborate, discover, and evolve knowledge across time. Gotham thus gives organizations the advantage against a changing landscape — so their experts can act on the most informed, comprehensive intelligence to achieve their mission.

One of the things that makes Gotham so unique is the level of partnership we have with our customers. We are not technologists in a lab in Silicon Valley — we are engineers on the ground, in the battlefield, at the operations centers. I was just talking to colleagues yesterday who have been on base abroad for 4 straight weeks, working side-by-side with the customer and channeling feedback so our software supports key operational missions. Real collaboration with our partners is what pushes the Gotham product forward.

Gourley: How are your customers using the Gotham platform?

For our partners, Gotham represents the integrated picture of what they know about the world. Customers range from military or intelligence organizations who might be conducting counterterrorism work, to regulatory agencies investigating financial or securities fraud, to nonprofits helping law enforcement find missing and exploited children.

More and more, we’re also supporting operational workflows. In a high-risk scenario, you need everyone coordinated on the same page, acting on the same information — and you can’t afford any disjoint between intelligence and operations teams. That’s why we’re deploying thousands of ruggedized laptops with Gotham to the US Army to assist soldiers in the field. Users can live collaborate geospatially and quickly answer questions about threats, equipment, and terrain. Operations that previously required weeks of preparation now take hours and each mission creates intelligence that is stored in Gotham to inform preparation for the next. That knowledge capture is so important — we once had a Commander in Afghanistan tell us that the 17-year war in Afghanistan more resembled 17 “one-year” wars — because they had to start over between deployments and rotations. Instead, Gotham facilitates an integrated “common operating picture” that lends efficiency and accuracy to soldiers operating under the highest stakes.

One of the other use cases close to my heart is disaster response. I’m a native Houstonian (who happens to be named after MLB-great Nolan Ryan…), so it was very moving to see our 2017 deployment with Team Rubicon in the aftermath of Hurricane Harvey. Within 24 hours of the storm, we had teams of engineers on the ground with Team Rubicon volunteers — fusing geographical, weather, and vulnerability data to facilitate rescue operations for over 70 people. That’s another type of impact I’m really proud of.

Gourley: We also hear about Palantir Foundry. What is Foundry, and how are Gotham and Foundry different?

Great question! Foundry is another key offering in the Palantir platform ecosystem. Whereas Gotham traditionally sings in operations and investigations — taking large data sources, unifying them, and enabling users to mission-plan and find insights from the “ground up” — Foundry was built to answer the computationally challenging questions enterprises have at scale. This means integrating petabytes of information into a common data foundation — empowering discovery and cross-functional collaboration that is truly changing whole industries. For example, via our partnership with Airbus, we are transforming aviation by deploying Skywise, a common data environment powered by Foundry, for almost 100 airlines and their suppliers. This enables airlines to share and operationalize massive-scale data around production, inventory, sensors, and maintenance systems — so that end-to-end, the industry is more efficient. The scale of data collaboration is really incredible. Another example is the World Food Programme, which serves 90 million people in 80 countries and uses Foundry to scale and optimize its global supply chain.

We’re seeing more and more government customers use Gotham in the traditional sense combined with Foundry. This might look like Gotham as the backbone intelligence platform for an agency — where analysts, agents, and operators build knowledge — with Foundry as the key system used for massive-scale analytics and management of data pipelines. And of course, both Gotham and Foundry share the same Palantir DNA: open APIs, fine-grained security and access controls, audit capabilities, interoperability with legacy and 3rd party systems, and cross-organization information sharing to name a few.

Gourley: The latest release of Gotham is called Titan. What was it like to build this release – and what are customers excited about?

To build Titan, we really had to go back to Gotham’s roots — and understand what has made the platform so valuable and enduring over the last decade. There’s a lot of dimension to the product — but at its core, what Gotham delivers is very simple and profound. It’s enabling customers to build a data asset — their most holistic, integrated picture of the world. It’s a phenomenally obvious concept — but one that requires an exorbitant amount of technical innovation and orchestration to get right. So we wanted Titan to extend that value — making that data asset more robust, open, and connective to the external (or “federated”) data stores to which the customers have access. The data asset is also powerful due to Gotham’s ontology — or the common model we use to translate raw data into meaningful representations. So instead of rows and columns, you have people, places, things. With Titan, we aimed to take the value of an ontology even further — by enabling custom, domain-specific views of this data that integrate various pieces of intelligence and add rich visualizations. We call these “Custom Object Views.” So instead of wading through thousands of unordered things we know about an entity, analysts can look at COVs and answer their first 10 questions within seconds — such as the last flagged financial transaction, or any suspicious travel abroad.

Finally, Titan introduces four new Modules into the Gotham platform: Ava, Dossier, Gaia, and Table. These Modules derive their power from seamless integration with the Gotham ecosystem: the underlying data asset, ontology, search, collaboration, and security frameworks. They build on what has been tried, true, and differentiating about Gotham all along — while injecting new methods of finding insight.

Gourley: Is a module like an add on?

Correct — Modules are additional investments on top of the Gotham platform. We think about them as accelerants to Gotham’s value, uniting the data asset with elements like real-time knowledge management (Dossier), geospatial collaboration (Gaia), high scale search and triage (Table), and Artificial Intelligence (Ava). Each Module was driven by real customer need — such as coordinated response to a crisis in the aftermath of an attack — and was prototyped on the ground with deep partnership. The Titan site is an excellent resource to learn more.

Gourley: Seems like openness is a key focus for Gotham.

That’s absolutely right. We know that it’s essential for governments to have extensible, interoperable technologies — and that they are the ultimate owners of their data. Period. All data in Gotham can be exported in a variety of open formats, on the front and backends, so that data is never trapped.

As I mentioned, one of the other core components of Titan is “federated” search of external systems to which the customer has access — so users can explore that data without the need for backend ingesting. Gotham makes it possible, from a single search interface, to search every data source at an enterprise and present users with views of data that include insight into where the results actually came from. Records of interest can be imported on the fly while respecting data provenance and security, with Titan supporting additional frameworks to write back to those systems.

We are also finding more and more opportunities to collaborate with developers on the customer side. Custom Object Views are one such opportunity (we ship Titan with an Object View editor for easy-to-configure views of your data). Titan also ships industry-standard RESTful HTTP/Json protocols for all APIs — so that experts can develop against the nuances of their institution.

Gourley: You mentioned the Ava Module. How is Gotham investing in AI/ML technologies?

It’s a major part of our roadmap. So many organizations are bogged down by data complexity, with their analysts and operators spending 80% of their time just searching and manually crunching data. That overhead is untenable moving forward — so we spend a lot of time thinking about how Gotham can use AI to flip that paradigm. How can we use technology so that experts spend less time on repetitive tasks, and more time interpreting and making decisions?

We also want to unite AI/ML innovations with what makes Gotham so differentiated in value. Fundamentally, these algorithms are only as good as the data that backs them — if you don’t have a holistic data asset from which to train and drive connections, you’re not unlocking true potential. You’re just building another piecemeal dataset. For AI to be transformative, it needs to be operationalized within a data and collaboration platform — and that’s where we’re focused with the future of Gotham.

You can see this throughout the platform — where we’re using AI to drive insights for operators, for mission planners, for intelligence analysts. For example, our Ava module automates the discovery of connections across the data asset for humans to review. This might look like alerts for complex patterns of behavior across millions of data points — all sent directly to users at a scale that would be impossible to track by human capacity alone.

Of course, all of this must be done transparently — and there is no replacement for human judgement. But we can and should enable analysts to use AI to offload specific complex and routine tasks – and are deeply investing in it. Gotham leverages the best analysts and operators and scales them — so that they can focus on building intelligence and enacting missions to keep us safe.

Gourley: What excites you most about the future of Gotham product?

I’m most excited by Gotham’s rate and speed of innovation. There’s an electricity within our product organization — where features are shipping faster than ever and we’re working on problems that most thought impossible to solve. This is all driven by deep engagement with our customers — a testament to the scale and impact Gotham has had over the past 10 years. I once met an Army veteran at a conference — who approached me to say that Gotham had saved his friends’ lives down range by alerting them to placed IEDs. It’s easy to get lost in pixels and data — but truthfully, we’re all working towards real issues that matter to real people in the world. And we know we need to do more. The challenges our institutions face are exorbitant – we have to keep pushing to ensure they have the technological advantages they need. That mission motivates us every day — and though it’s been a decade, Gotham feels like it’s just getting started.

Gourley: I’ve heard many customers are amazed by how different Gotham looks today and the new capabilities in Titan. Do you have more information online?

Yes! For more information on the Titan release, you can check out our website which includes visuals for the core upgrade features and new Modules: https://www.palantir.com/palantir-gotham/titan/

Additionally, you can contact Gotham-Titan-Requests@palantir.com for any questions or requests related to the release. And as always, we encourage our partners to engage with Palantir engineers in the field so that Gotham innovations can be as transformative as possible.

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