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When I step into a new enterprise project, the point of contention is often the same: huge corporations are desperately drowning in their own legacy swamp and terabytes of unstructured data.
All this information chaos is scattered across dozens of clunky ERP systems, raw data lakes, and ancient infrastructure. Because of this tech zoo, the business inevitably suffers from fragmented data, slow decision-making, and incredibly tangled, complex workflows.
On the other hand, I saw that the problem for corporations may not be a lack of software, as they have already poured millions of dollars into IT. They may have killer cloud storage, heavy-duty ERPs from top-tier vendors, and a ton of expensive analytical software, but the pain point remains: this entire heavy-hitting IT landscape still operates in rigid data silos.
The cost of these disconnected systems is massive because siloed and fragmented data can drain a company of up to 30% of its annual revenue. Another study highlights that businesses bleed an average of $5 million every year, with 7% reporting losses of $25 million or more precisely because they hit a dead end of deaf data silos and poor integrations.
Artificial intelligence is capable of closing this gap, but only if it has the right foundation. If you need an in-depth Palantir technologies overview from a practitioner, I’ll say it like it is: this is a harsh backend engine that drags a business out of chaos.
Let’s break down exactly how this engine works from the inside and why it forces legacy infrastructure to produce accurate predictions in real time.
During kickoff calls, client CTOs regularly ask me why they should invest in yet another system on top of the ones they already have. I get it, and I warn teams against treating this software as just another BI dashboard or a basic ML sandbox.
In reality, we are deploying a fundamental, operating-system-style infrastructure that fuses your data, models, and workflows into a single layer. That exact layer powers governed data applications, automated decision-making, and real-time operational actions across every business unit.
Over the last few years, the vendor’s engineers made a killer architectural pivot to create the perfect environment for orchestrating generative AI. They give you the ability to use the brains of modern language models while retaining paranoid control (but in a good way) over exactly what those models can touch inside your closed databases.
The platform grabs a wild amount of raw info from the corporate perimeter, like logs, telemetry, and transactions, and forces AI to work strictly within a governed environment. Because the intelligence runs both on the underlying raw data layer and straight on top of the ontology, you gain massive flexibility. You execute low-level ML crunching on raw datasets, and then apply high-level, business-oriented reasoning over real-world objects to establish a single reliable source of truth.
We begin by addressing how to ingest data and set up pipeline infrastructure. Before we even touch AI, Foundry has to connect to your highly fragmented ERP estates, SQL and NoSQL databases, data lakes, real-time IoT streams, document repositories, and external APIs.
We take all those disparate sources of information and combine them into a single, governed environment. The way I describe it, we essentially build the big pipes that support the overall system.
Once the integration layer is stable, we build the ontology on top of it. This is the semantic and operational layer that takes the datasets produced below and maps them onto the real-world entities and processes they represent.
We deal with two distinct dimensions here.
The first category of data is referred to as semantic elements: objects, properties, and links. These are the nouns and represent the main components of your business: e.g., your factory’s production line, delivery vehicle, customer orders, raw material batches, staff, etc. Each object type is backed by datasets from the integration layer and carries properties that come from structured data, streaming feeds, model outputs, or any combination of the three.
Second, there are kinetic elements that include actions, functions, and dynamic security, which are the verbs that can actually happen through the ontology. An action updates the state of an object, fires an external API call, or feeds a decision back into a downstream system. Dynamic security controls who can see and do what within the strict parameters of a given object.
AI gets unleashed specifically on these linked objects, so it understands the actual context of your business instead of just parsing dead SQL columns.
This part of the engine crunches through colossal volumes of historical logs and generates accurate predictions in real time, thus allowing businesses to finally start playing offense instead of just putting out fires after the fact.
Built-in ML models continuously scan telemetry and highlight the future state of your system. For example, you will be notified in advance of the time when a certain production conveyor or pump will fail or when the parts in a warehouse are about to be out of stock.
Naturally, any ML model will always have probabilistic outputs. Well, at least that’s what we have these days. The algorithm simply drops powerful actionable insights that your crew has to pick up and turn into real moves, and let’s be honest, predictions are absolutely useless numbers on a screen if they don’t lead to concrete actions.
This is the exact spot where raw analytics transform into aggressive business action. The system starts pushing moves that flip the situation right in prod with little to no human intervention.
For example, the platform receives data from IoT sensors indicating there is a cargo truck that is stranded in transit and will likely miss a hard SLA. The platform will issue a red alert, plus it will also send an API trigger directly to your ERP system.
The ultimate goal is to remove as much of the human factor from the routine decision-making process as possible. The system can automatically push a command into the client’s SAP, instantly reroute the logistics, and send the cargo to a backup warehouse.
The Palantir ecosystem is smartly divided into specialized modules that we surgically implement to target specific business pains. You don’t have to buy all the Palantir software at once. Start small, prove the value, then scale up. We build the architecture like Lego blocks, where each piece snaps into the next without breaking what’s already in place.
Let’s start with the historical foundation and smoothly move to the absolute enterprise hits.
This beast of a product was initially built for intelligence agencies and the government sector. I only bring it up to make one point: the paranoid, military-grade security backing this entire platform started here. Gotham was designed to handle extreme classified loads and classified information.
While its data isolation standards are in a completely different league compared to traditional tools, the vendor definitely has much better-suited options for your everyday corporate needs.
Based on my own time spent with Palantir technologies, I know that Foundry is definitely the B2B segment’s main backend brain. It also functions as an extremely powerful sandbox for our data engineers to work with your analysts so that they can demolish data silos, create a custom ontology, and connect raw logic from enterprise systems to real-time data flows.
I would like to remind all parties involved that AIP Services have been integrated throughout all tiers of Foundry. In other words, every engineer on all teams now has seamless access to the AIP services and the entire data pipeline.
In order to deploy updates safely across such a large environment without interrupting production, we make use of the next module.
Apollo serves as the backbone CI/CD solution for DevOps engineers to help alleviate server infrastructure complications at their very core. All three platforms, including Gotham, Foundry, and AIP, utilize Apollo natively, meaning that you will not have to rely on a unicorn specialist for any product upgrade.
Apollo automates the secure deployment of ML models to the AWS and Azure cloud environments, as well as bare-metal physical servers, and edge devices located on the floor of manufacturing facilities. All three methods of deployment occur simultaneously with no manual intervention at any point in the process.
Dumping commercial secrets into public ChatGPT and other AI bot systems without much thought creates a lot of fear from a security standpoint, and I totally see why that’s true. We wire up the AIP module to elegantly cure that massive fear of leaking data to the web.
The AIP takes powerful public LLMs (i.e., GPT or Claude) and safely orchestrates them directly over your corporate ontology while keeping all AI activity within a closed perimeter. Thus, AI acts as a very intelligent and secure gateway that allows you to access complex metrics using plain human language without having your company’s trade secrets exposed to the public
Rubix is the backbone of everything that exists in Palantir and Gotham, Foundry, and AIP all rely on it natively. It is not sold separately from either of those products, but it acts as the internal execution engine powering the entire Palantir product suite. There is no possible way to hold the Palantir ecosystem together without it.
Rubix provides the base infrastructure that gives the platform its tremendous consistency of execution across completely different deployment environments (technically, I would refer to it as an infrastructure substrate). Based on the extensive backend functions, it also helps to mitigate the complexities of the backend, allowing for seamless and secure deployment of the main platforms on any of the following: AWS, Azure, or on-premises.
What I regard to be most significant about Rubix is that it is baked into the platform rather than bolted on top, so there’s no gap between what the policy says and what the system actually enforces.
If you ask what Palantir does in practice, I would describe Palantir’s architecture as very flexible and capable of processing an enormous amount of different types of data, from worldwide banking transactions to very complex supply chains to various medical protocols.
Now let’s examine some real-life examples.
The Palantir platform can identify hidden, multilayered anti-money laundering (AML) networks and also make it significantly easier and faster for analysts to conduct KYC. Machine models sift through global financial flows in real time, flag anomalies, and put those anomalies into a triage queue for the compliance team to investigate and possibly take action before regulators come calling. And trust me, in banking, they always eventually come knocking.
Predictive maintenance is an absolute goldmine that delivers insane ROI. The system can forecast the wear and tear of equipment long before there is a true failure by using raw telemetry data pulled from IoT sensors placed on offshore oil rigs or aircraft engines. Using predictive maintenance allows companies to schedule the replacement/repair of critical components to avoid unplanned, catastrophic downtime that can result in significant dollar losses.
In this niche, we focus on optimizing the internal supply chain by enabling the system to allocate critical resources with mathematical precision. For instance, in larger hospital systems, the system tracks all your vital medication inventories and staffing levels, as well as your available bed capacity, so your clinic operates as smoothly as possible, like a finely tuned machine.
Massive logistics hubs gain the ability to see their supply chains from a bird’s-eye view. If there is a storm in the sea or if there is a staff strike at a port, the dispatcher will get an alert right away to take action and resolve the problem.
The system calculates the alternative routes automatically and updates the ERP right away. Dispatchers can use this data to reroute giant container ships in real time, and thereby save hundreds of millions of dollars in contracts that would have otherwise gone down the drain.
Chief information security officers on the client side often raise serious red flags at the mere thought of introducing generative AI. They express strong concerns about intellectual property leaks, and honestly, I can’t blame them.
But if you look closely at the base infrastructure level, you’ll see a Zero Trust paradigm. This architecture was originally designed to eliminate corporate fears of multimillion-dollar regulatory fines.
Let’s break down exactly how your brand secrets are protected.
At the deepest infrastructure level, the entire data array is strongly encrypted at rest and in transit, and the system is built with strict data isolation as a baseline. In most deployment configurations, even integrators like us work with anonymized data dumps rather than raw production databases. In any case, the exact access patterns always depend on your governance setup, permission model, and how the environment is deployed. Besides, any action requires explicit system approval from your security team.
The platform’s granular security model goes way beyond a single killer feature. The system enforces dynamic data masking, strict object-level permissions, purpose-based access control (PBAC), and more.
Aside from checking users’ job titles, the governance engine verifies the exact business context behind WHY an employee needs a specific data slice in the first place. Generative AI fully adheres to these strict policies and will never hand over executive financial reports to a regular sales rep because there’s no verified business context for that request.
The platform provides powerful technical tools out of the box for deep auditing of every user step and AI response. You will easily close out questions regarding GDPR in Europe, CCPA in the US, HIPAA in healthcare, and a wide range of similar strict compliance obligations. The software doesn’t negate the rules of social engineering, but it will hand you a flawless, bulletproof baseline of logs for any regulator.
A MIT report covered by Fortune confirms that 95% of generative AI pilots at enterprise companies are actively failing. Without the right engineers driving the Palantir integration, this software will forever remain just a very expensive and useless toy in your stack.
Beyond our deep technical expertise with Palantir, we bring massive global delivery capabilities to the table. This is exactly why large enterprises trust Innowise with their digital transformations:
That covers our global delivery scale and the business foundation we bring to every project. Now let’s zoom in on the actual Palantir technical stack.
Our team is packed with highly specialized data engineers, technical leads, and architects holding all three official Palantir Foundry certifications, and they know the ins and outs of deploying heavy cloud infrastructures.
We deeply understand the internal logic of vendor solutions and don’t try to learn the basics on the client’s dime. We deliver expert Palantir services by writing native Python and PySpark code right inside Foundry’s Code Repositories, relying entirely on the rigorous, real-world technical expertise of our development squads.
We take on the most complex backend work that other integrators usually avoid. Our team constructs end-to-end data pipelines using Pipeline Builder en Code Repositories.
For heavy-scale transformations on multi-terabyte datasets with complex joins and serious aggregation logic, we apply PySpark. For simpler row-level operations or when speed matters more than raw throughput, lightweight compute engines are available and often more practical than spinning up a full Spark job.
These engines spin up faster, burn far less compute, and in a lot of cases get the job done in a fraction of the time. I constantly hear of teams reaching for Spark out of habit and blowing their compute budget on work that never needed it in the first place.
We also use incremental pipelines in Pipeline Builder, which process only the data that actually changed rather than re-running full refreshes every time. On large datasets, this alone can cut pipeline runtime by an order of magnitude. On top of that, we integrate Foundry AIP capabilities directly into operational workflows and Werkplaatsen, so AI can accurately assist your analysts within complex, regulated environments.
During the production phase, we set up reliable CI/CD pipelines to safely deploy all future platform updates. Our Palantir experts optimize existing workloads by refactoring legacy code into Foundry-native pipelines and redesigning full-refresh data pipelines into incremental processing flows, significantly reducing compute usage.
We also implement Medallion architecture layers to improve data quality and analytical performance. Our targeted optimizations even include designing logic that minimizes unnecessary external API screening calls.
If you need to inject this power into your enterprise infrastructure, please don’t hesitate to stuur ons een bericht, and we’ll build the right architecture from scratch or modernize your old-hat solution.
First, the vendor demonstrates the core business value of its products before selling an enterprise license to your executives. After your organization has gained official access to the Palantir ecosystem, our team sets up the core data integration pipelines, models your semantic ontology right on top of that unified data, and then deploys custom business logic into your new infrastructure.
The software has a fully-functional audit trail built in for both user and algorithm steps, which creates the perfect technical baseline to pass a compliance audit.
The Palantir platform automatically collects telemetry from your logistics network and pushes API actions to your ERP to quickly re-optimize routing during a force majeure event.
We connect your disjointed legacy databases into a unified integration layer, and then map that raw data onto a single semantic ontology, so that AI can access those databases without forcing you to replace the existing physical servers.
We connect your disjointed legacy databases into a unified integration layer, and then map that raw data onto a single semantic ontology, so that AI can access those databases without forcing you to replace the existing physical servers.
The platform is a highly secure gateway that allows your team to easily extract complex metrics from corporate databases using plain English without having to write a single line of SQL code.
The platform relies on the Apollo module to securely push updates and heavy machine learning models across cloud environments, on-prem servers, and edge hardware. It orchestrates deployments in a controlled, environment-aware way so updates land safely across a distributed infrastructure without breaking what's already running.
We have a dedicated team of engineers who continuously manage your deployment pipelines and optimize the backend architecture to ensure that your analytics can perform seamlessly even while experiencing massive daily loads.
Chief Technology Officer
Dmitry leidt de technische strategie achter aangepaste oplossingen die echt werken voor klanten - nu en wanneer ze groeien. Hij combineert visie met praktische uitvoering en zorgt ervoor dat elke build slim, schaalbaar en afgestemd op het bedrijf is.
