what are the product and services from Palantir that have been in use by government or military

Palantir’s government and military work centers on a small set of core software platforms that are deployed across defense, intelligence, law enforcement, immigration, health, and other civil agencies.12

Core products used by governments

  • Gotham – An “operating system” for intelligence, defense and law‑enforcement data, used by militaries and counter‑terrorism analysts in the U.S. Intelligence Community and Department of Defense. It fuses data from many sources (sensors, databases, reports) and supports tasks like targeting, investigations, and battlefield intelligence.21
  • Foundry – A data integration and analytics platform used more broadly in civilian government, including health and administrative agencies, to organize large datasets and support AI/ML analytics. It is used, for example, in U.S. health data initiatives and cross‑agency data‑sharing efforts.341
  • Apollo – A deployment and operations layer that runs underneath Gotham and Foundry, allowing them to be deployed, updated, and managed across highly restricted and disconnected military and government environments (e.g., classified clouds, submarines, forward bases).53

Defense and intelligence uses

  • U.S. Army uses Gotham and related stacks in:
    • Distributed Common Ground System–Army (DCGS‑A) for battlefield intelligence.4
    • Tactical Intelligence Targeting Access Node (TITAN), a next‑generation ground station for fusing data from space and airborne sensors to provide targeting information; Palantir holds a contract worth about 178 million USD to deliver TITAN prototypes.678
    • Maven Smart System (Project Maven), an AI system that aggregates multi‑source data for target identification; Palantir has a contract worth about 480 million USD running into 2029.96
    • A long‑term “Enterprise Service Agreement” with the Army valued up to about 10 billion USD to provide software services across the service.10
  • Other U.S. defense/intel users include the Defense Intelligence Agency and CIA for large‑scale intelligence analysis, counter‑IED work, and other analytic workflows using Gotham.24
  • U.S. Special Operations Command and other services (Navy, Air Force, Space Force, Marine Corps) use Palantir platforms for mission command, AI/ML analytics, and command‑and‑control modernization.4

Law enforcement, immigration, and border control

  • U.S. Immigration and Customs Enforcement (ICE) uses Palantir software for:
    • Investigative Case Management (ICM), a system (built under a ~41 million USD contract) that links personal and criminal records of immigrants across multiple federal and private databases.10
    • Additional contracts (around 30 million USD) for real‑time migrant tracking and deportation‑related analytics, sometimes described as providing the “engine” for large‑scale deportation operations.1110
  • U.S. Customs and Border Protection (CBP) uses Gotham to track immigrants and travelers at borders, integrating multiple data sources for risk assessment and targeting.114

Civilian and international government uses

  • U.S. Department of Health and Human Services and partners use Foundry for health‑data integration and analytics, including COVID‑era national data‑sharing initiatives.34
  • The U.S. Department of State has used Palantir for projects such as monitoring the health of diplomats and other analytics work.4
  • Internationally, Gotham has been deployed with:
    • Norwegian Customs for screening passengers and vehicles based on integrated customs and financial data.4
    • Danish, German, and other European police forces (e.g., POL‑INTEL in Denmark, Hesse state police, and Europol) for predictive policing and intelligence analysis.4
    • The International Atomic Energy Agency (IAEA) to help verify Iran’s compliance with the 2015 nuclear agreement by analyzing nuclear‑related data.24

Newer AI/agentic offerings

  • On top of Gotham and Foundry, Palantir now markets AI‑driven decision platforms (often branded as “Maven Smart System” or broader AI “operating systems”) that embed large‑scale analytics and AI agents directly into military and government workflows, especially in targeting, logistics, and case management.126

Do you want this broken down by specific agency (e.g., Army, ICE, HHS) with a list of the concrete systems and contract values for each? 131415

⁂

Focus only on military and intel agencies

Here’s a focused rundown of Palantir products and concrete programs used specifically by military and intelligence organizations.161718

Core platforms used in defense/intel

  • Gotham – Primary intelligence and defense analysis platform used by the United States Intelligence Community (USIC) and the U.S. Department of Defense for counter‑terrorism and military intelligence workflows. It fuses multi‑source data (signals, HUMINT, sensors, reports) and supports targeting, investigations, and operational planning.171816
  • Foundry – Used in some defense contexts as a data backbone for broader analytics and AI/ML, especially where logistics, readiness, and enterprise‑wide data integration are involved. It often underpins “AI ops centers” or command‑and‑control analytics environments.1917
  • Apollo / AIP (AI Platform) – Deployment and operations layer plus newer AI tooling that lets Gotham/Foundry be run across classified clouds, tactical edge, and coalition environments, and supports AI‑assisted decision tools like Maven Smart System.201917

U.S. military and intelligence agencies

Department of Defense and combatant commands

  • Maven Smart System (Project Maven) – AI‑powered system to aggregate multi‑source ISR data and highlight areas/targets of interest for analysts and commanders.2120
    • Initial Army contract: ~480 M USD, through ~2029.2021
    • Contract ceiling later increased by ~795 M USD to meet “growing demand” from multiple military users.19
    • Users include at least five U.S. combatant commands: Central Command, European Command, Indo‑Pacific Command, Northern Command/NORAD, and Transportation Command.19
  • Tactical Intelligence Targeting Access Node (TITAN) – Next‑generation Army ground station that fuses data from space and high‑altitude sensors to provide targeting data to field units.2220
    • Contract: ~178.4 M USD for 10 prototypes (advanced + basic) over ~24 months.2220
    • Identified as a “foundational element” of Army intelligence modernization.22
  • Gotham in Army intelligence – Used within Army intelligence systems such as Distributed Common Ground System–Army (DCGS‑A) and related analytic environments for battlefield intelligence, pattern analysis, and mission planning.1816
  • Enterprise‑wide DoD usage – Gotham as an intelligence tool is used broadly across the U.S. Department of Defense and the United States Intelligence Community for counter‑terrorism and defense analysis.161718

United States Intelligence Community (USIC)

  • USIC (CIA and other agencies) – Gotham is explicitly described as an intelligence tool whose customers include the United States Intelligence Community. It supports large‑scale intelligence fusion (terrorism networks, financial flows, security threats) and is used by analysts for operational and strategic assessments.171816

Non‑U.S. defense and intel organizations

United Kingdom: Ministry of Defence (MoD)

  • UK MoD – enterprise analytics
    • The UK Ministry of Defence has a three‑year, ~£240.6 M contract (started April 2026) with Palantir for continued licensing and support of data‑analytics capabilities used for “critical strategic, tactical and live operational decision making” across classifications.232425
    • The contract notice emphasizes interoperability with NATO and allied nations’ Palantir systems, implying deployment across multiple commands and operational domains (joint, coalition operations).2425
    • Palantir software is used to support defense decision‑making while the MoD states that UK defense data remains sovereign and under MoD control.24

NATO / allied defense users

  • Public contract language from the UK MoD and other reporting explicitly notes interoperability with NATO and allied nations’ Palantir systems, indicating use by multiple NATO‑aligned defense organizations beyond just the U.S. and UK.2524
  • Palantir itself markets its Intelligence and Defense offerings as supporting allied and coalition partners with secure data‑sharing, cross‑domain fusion, and joint targeting workflows, all built on Gotham/Foundry/AIP.2627

Summary table (military and intel only)

Organization / community Product(s) in use Program / context Example contract scale
U.S. Department of Defense (Pentagon) Gotham, Foundry, Apollo/AIP Enterprise intelligence, AI‑assisted ops and targeting Maven, TITAN, others16201719
U.S. Army Gotham, Foundry, AIP Maven Smart System, TITAN, DCGS‑A, intel modernization 480 M + 795 M Maven, 178.4 M TITAN202219
U.S. combatant commands (CENTCOM, EUCOM, INDOPACOM, NORTHCOM/NORAD, TRANSCOM) Maven Smart System (on Gotham/Foundry/AIP stack) AI‑powered multi‑source ISR and targeting support Covered under Maven contract ceiling19
U.S. Intelligence Community (e.g., CIA) Gotham Counter‑terrorism and broader intel analysis Not publicly itemized161718
UK Ministry of Defence Gotham/Foundry‑based data analytics Strategic, tactical, live operational decision support £240.6 M, 3‑year contract232425
NATO and allied defense organizations Gotham/Foundry/AIP Interoperable joint and coalition operations Referenced via MoD and NATO‑interop language2426

Would it be most useful if I next map these products to specific mission types (ISR, targeting, logistics, cyber, etc.) or dive into the technical architecture stack they typically deploy in classified / tactical environments? 282930

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can elaborate all the information involving StarShield from SpaceX.

Starshield is SpaceX’s dedicated, government‑only variant of Starlink, focused on secure communications, Earth observation, and hosting classified payloads for national security customers.3132

What Starshield is

  • Starshield is a secured satellite network “designed for government use,” distinct from the consumer/commercial Starlink service but built on the same LEO constellation and tech stack.3233
  • It adds mission‑specific satellites and payloads plus higher‑assurance encryption so that government users can run classified missions and process sensitive data end‑to‑end.343532

Core capabilities

SpaceX and defense sources describe three primary Starshield capability areas.363234

  • Earth observation
    • Satellites can carry sensing payloads (imaging, other ISR sensors) for tasks like monitoring military movements, natural disasters, or environmental conditions.313634
    • Data is processed and delivered securely to customers, with details like exact sensor resolution undisclosed.36
  • Secure communications
    • Provides assured global communications for government users, using Starshield user equipment and leveraging the broader Starlink constellation for coverage.353732
    • Uses laser inter‑satellite links to route traffic without relying on ground stations, increasing resiliency and global reach.3436
    • Incorporates “additional high‑assurance cryptographic capability” beyond standard Starlink, to meet classified handling requirements and resist interception/jamming.38323534
  • Hosted payloads / custom missions
    • SpaceX builds satellite buses that can host “the most demanding customer payload missions,” meaning government agencies can fly their own instruments or mission packages on Starshield platforms.323534
    • The architecture is proliferated LEO (many small satellites) for resilience and rapid reconstitution.3538
  • Technically, Starshield reuses Starlink’s broadband tech, laser links, manufacturing, launch, and terminal ecosystem, but wraps them in a separate, government‑only service layer and security architecture.393234
  • Starlink already has end‑to‑end user data encryption; Starshield adds further cryptography and handling so it can carry classified payloads and data.393235
  • Starshield satellites can communicate with the main Starlink constellation via laser links, extending network reach and flexibility for military users.3834

Government customers and contracts

  • SpaceX identifies Starshield’s “primary customers” as the U.S. Space Development Agency (SDA), National Reconnaissance Office (NRO), and U.S. Space Force.3136
  • U.S. Space Force awarded SpaceX a Starshield contract worth up to about 70 M USD for one year, providing “Starshield end‑to‑end service via the Starlink constellation, user terminals, ancillary equipment, network management and other related services.”373438
  • The Starshield contract framework, first established around 2021 and expanded later, is also being leveraged for a larger MILNET‑type constellation program (about 480 satellites) funded by Space Force and managed by the NRO to meet DoD connectivity requirements.4041

Ukraine access

  • Ukraine’s armed forces have been granted access to Starshield as a secure, militarized version of Starlink through U.S. Department of Defense contracts.424344
  • An earlier agreement connected 500 terminals to Starshield for about 40 M USD; a later contract expanded access so roughly 3,000 terminals in Ukraine could use the encrypted Starshield network, with service extended through 2025.434542

Security and architecture aspects

  • Starshield emphasizes:
    • High‑assurance cryptography and ability to host/process classified payloads and data.32343538
    • Proliferated LEO architecture for resiliency against attacks and failures, with constant connectivity and rapid launch‑based augmentation.3538
    • Full‑stack control from SpaceX (launch vehicles, satellites, ground/user terminals, network management) enabling rapid development and global scaling for government missions.3235

Would you like a deeper dive into Starshield’s potential military mission profiles (e.g., PNT backup, tactical comms, missile warning, ISR) and how they compare to other DoD constellations like SDA’s Proliferated Warfighter Space Architecture?

⁂

Elaborate on Palantir AIP

Palantir’s Artificial Intelligence Platform (AIP) is an “AI control plane” that sits on top of Foundry and Apollo to connect LLMs and other AI models directly to operational data and workflows, with heavy emphasis on security, governance, and human‑in‑the‑loop control.46474849

What AIP is and how it fits in

  • AIP is a platform for deploying and governing large language models and other AI (ML models, VLMs, etc.) inside an organization’s own networks, including classified and highly sensitive environments.48505146
  • Together with Foundry (data/ontology layer) and Apollo (software deployment and updates), AIP forms an “operating system” that can deliver LLM‑powered web apps, mobile apps, and edge apps that embed localized AI.474946

Core capabilities

Palantir’s docs break AIP capabilities into several major buckets.494648

  • Model and tool access
    • Governed access to a catalog of LLMs and multimodal models (first‑party and external) with routing and abstraction so apps aren’t hard‑wired to one model.5248
    • Tools to deploy models into private networks, including air‑gapped / classified environments and tactical edge devices.505146
  • Agents and workflows
    • Building blocks to define LLM‑driven functions and multi‑step agents that can read from ontologies, call tools/APIs, and take actions in operational systems.4648
    • Automation primitives to orchestrate these agents across business or mission processes, with scheduling, triggers, and guardrails.5348
  • Evaluation and observability
    • Suites to define evaluation metrics and tests (quality, safety, bias, hallucinations) and to monitor agent/app performance over time.4846
    • Observability tooling to inspect prompts, completions, tool calls, and outcomes, and to iterate on configurations based on real usage.4648

Security, privacy, and governance

AIP is designed for regulated and classified contexts, so most of the differentiation is in security and governance controls.54555646

  • Data protection
    • Access controls, encryption, and detailed auditing are inherited from Palantir’s stack and applied to prompts, completions, and actions taken by agents.555654
    • When it uses third‑party‑hosted models, Palantir states that no customer data in prompts or completions is retained by those third parties; calls are mediated with strict contracts and technical safeguards.5549
  • Fine‑grained policy control
    • Mechanisms like markings, cipher, checkpoints, and granular access controls constrain what data an agent can see and what operations it can perform.565455
    • “Tool scopes” and similar primitives restrict which tools/APIs an agent may invoke, tailored to the way LLM‑driven agents operate.56
  • Governance and lineage
    • Built‑in governance tools track historical lineage of AI operations and give transparency into why an agent did something, who approved it, and what data it used.545346
    • This is intended to support compliance, audit, and accountability requirements in defense, government, and regulated industries.574754

Defense and military usage patterns

Palantir’s defense demos and write‑ups show AIP being used as the AI layer on top of defense ontologies and operational systems.51585057

  • Threat monitoring and ISR fusion
    • Operators query LLM‑backed agents for real‑time situational awareness about enemy units in a region, which then pull from integrated intel data (sensors, reports) to construct likely unit formations and identify vulnerabilities.50
    • Agents can task drones or other ISR assets, analyze imagery or video, and confirm presence of specific threats (e.g., an enemy tank at a location).50
  • Course of action (COA) generation and planning
    • AIP agents generate multiple COAs to neutralize detected threats and automatically package them for review up the chain of command.5750
    • The system validates logistics (e.g., whether units have required munitions) and proposes routes or plans that respect constraints and rules of engagement.5750
  • EW, comms, and targeting support
    • AIP identifies and pairs validated enemy communication nodes and suggests how to assign jammers, helping planners disrupt adversary comms while protecting friendly forces.5057
    • In general, AIP acts as the AI layer that recommends actions, while humans retain authority to approve and execute operational steps.515750

How it works architecturally (at a high level)

From a systems point of view, you can think of AIP as an AI runtime tightly integrated with Foundry’s ontology and Apollo’s deployment fabric.475246

  • Data / ontology layer (Foundry)
    • Operational systems and data sources (logistics, sensor feeds, intel databases, case management, etc.) are modeled in an ontology accessible to AIP.5247
    • AIP agents call into that ontology via governed APIs, rather than free‑form DB access, which allows consistent semantics and access control.485246
  • AI control plane (AIP itself)
    • Central services to manage models, agents, tools, evaluation suites, and policies.4648
    • Supports deployment of applications (chat‑style UIs, task copilots, embedded assistants) that operate across web, mobile, and edge.4746
  • Deployment / edge (Apollo)
    • Apollo handles continuous deployment and updating of AIP‑based apps and agents across cloud regions, on‑prem, and tactical edge environments.4746
    • This includes disconnected or intermittently connected nodes, with local inference where applicable and synchronization when connectivity returns.514650

If you want to go deeper, I can next sketch how you’d architect an AIP‑like stack yourself (model gateway, policy engine, tool runtime, eval harness) using open‑source components, for comparison to Palantir’s approach. 5960

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elaborate on WhereDaddy

“Where’s Daddy?” (often written “WhereDaddy”) is the reported nickname of an Israeli military AI targeting support system used in the Gaza war, designed to track people already placed on a kill list and alert operators when they return home to their families.6162

  • Investigations by Israeli outlet +972 Magazine and Local Call describe two linked AI systems: Lavender, which generates a “kill list” of suspected Hamas or Islamic Jihad members, and “Where’s Daddy?”, which tracks those flagged individuals to time strikes.6261
  • Lavender reportedly identifies tens of thousands of suspected militants and their home addresses; WhereDaddy then monitors movements and signals when the person goes back to their residence, especially at night.6162

Reported function and targeting logic

  • WhereDaddy is described as a monitoring and alert system that uses surveillance data to tell intelligence officers “the target is now at home,” so airstrikes can be launched against family houses rather than military facilities.6261
  • Several anonymous Israeli intelligence officers told reporters the system was explicitly built to prefer hitting targets at home, because it is “easier to bomb a family’s house” than to strike them during military activity.6162
  • This approach, combined with a permissive bombing policy, allegedly contributed to entire families being killed inside their homes, with civilians treated as “collateral damage.”6261

Data sources and technical ingredients (as reported)

Public reports do not give source code or detailed architecture, but they describe the types of inputs and methods used.636162

  • Inputs:
    • Communications and location metadata (mobile phones, SIMs, possibly other signals intelligence).6361
    • Geospatial and ISR feeds such as satellite imagery and drone video, fused with address records and social/family ties.6362
  • Methods:
    • Machine‑learning models and pattern analysis to infer where a target likely is at specific times and when they are inside a home associated with their family.6362
    • Event triggers to send alerts to human operators when the system’s criteria (target at home, time window, etc.) are met.6263

Human oversight and error concerns

  • Sources quoted in the +972/Local Call investigation say human oversight over Lavender and WhereDaddy was extremely thin, with operators sometimes “rubber‑stamping” machine selections after only seconds of review, mainly checking the target’s gender.6162
  • Lavender itself reportedly had around a 10% error rate in labeling people as militants; combined with timing strikes on homes, this magnified the risk of killing people with weak or no real links to armed groups, plus their families.61
  • AI and safety experts interviewed by media outlets argued that such use of AI is “dangerously close to indiscriminate targeting” and raises serious questions under international humanitarian law.646261

IDF’s official position

  • The Israel Defense Forces (IDF) publicly dispute key claims about these systems:
    • They say reports about automated kill lists and deliberate targeting of family homes are “misleading” and contain “numerous inaccuracies.”61
    • The IDF insists it does not use an AI system to automatically identify terrorists or to decide if someone is a terrorist; it says human analysts must independently verify that targets comply with international law and IDF directives.6261
  • At the same time, the IDF acknowledges using AI‑based tools to assist in target generation and says it takes measures to minimize civilian harm.6462

Broader implications

  • WhereDaddy is widely cited in academic and policy discussions as an example of AI‑mediated “human targeting”, where algorithms track individuals and optimize when to kill them rather than just where to strike infrastructure.6564
  • Commentators argue it illustrates how AI can collapse the line between surveillance and execution, making intimate life patterns (like visiting one’s children) into triggers for lethal force.656362
  • Human‑rights lawyers warn that designing systems to hit people in their homes, where large numbers of civilians are predictably present, may violate proportionality and distinction requirements in the laws of war.646562

Do you want a more technical, system‑design style breakdown (e.g., how a WhereDaddy‑like pipeline might be architected from data ingestion to alerting), or are you mainly interested in the legal/ethical analysis and precedent it sets? 666768697071727374

⁂

Give a technical pipeline style breakdown of WhereDaddy

A reasonable way to think about “Where’s Daddy?” is as an end‑to‑end pipeline that ingests bulk surveillance data, maintains a dynamic “person → home” and “person → device” graph, and drives an alerting system that notifies operators when a listed person is detected at a strikeable location (typically their home). The exact implementation is not public, so this is a reconstructed architecture consistent with the behaviors and data sources described by open reporting.757677

1. Upstream inputs and person graph

a. Data ingestion layers

  • Telecom and SIGINT feeds: Call detail records, device IDs (IMEI/IMSI), approximate cell‑tower or RF‑based location, possibly Wi‑Fi/Bluetooth or other RF sensors where available.7675
  • ISR and geospatial data: Drone and aircraft video, satellite imagery, map data, building outlines, address databases.7776
  • Intelligence databases: Lavender’s outputs (suspected militants and family ties), prior HUMINT reports, social/organizational network data.7577

b. Entity resolution and mapping

  • Identity resolution: Link each person in the Lavender kill list to devices, phone numbers, and other identifiers; maintain many‑to‑many edges (person ↔ phone ↔ SIM).
  • Home and family inference: For each person, identify one or more “home” locations (houses, apartments) using: nighttime location clustering of phones, registry or address data, and kinship/household graphs derived from intel databases.7677
  • Result: A graph/ontology where every tracked person has attributes like home_geos, known_devices, family_members, risk_score, and current_status.

2. Location and presence estimation

a. Real‑time or near‑real‑time tracking

  • Streaming location updates: As telecom/SIGINT events arrive (tower pings, handset geolocation, etc.), the system updates the estimated location of each device and, by association, each person.7576
  • Filtering: Only devices linked to Lavender targets are followed in this detail; others may be processed in aggregate or filtered out.

b. Geospatial correlation with homes

  • Zone modeling: For each “home,” define a spatial footprint (polygon or radius) representing the structure and immediate surroundings.
  • Presence model: For each target, compute a time‑varying probability $P(\text{target at home} \mid \text{device signals, ISR cues})$.
    • Inputs: device location within home polygon, dwell time, time of day, prior patterns (e.g., usually sleeps between 22:00–06:00).7776
    • Corrections: If multiple devices tied to the same person are scattered, use heuristics or learned models to decide which is most likely carried by the person at that moment.

c. ISR confirmation loop

  • Tasking ISR: When the presence probability crosses a threshold, the system may automatically propose or schedule ISR collection (e.g., drone camera on that building).7677
  • Visual/sensor confirmation: Computer vision and analyst review can confirm indications like lights on, vehicles arriving, or a person with matching gait/appearance characteristics entering the building.

3. Targeting logic and alert generation

a. Eligibility and policy filters

  • Reference the Lavender kill list entries and rules of engagement (ROE) to determine whether a person is currently authorized as a valid target.7775
  • Apply policy logic such as:
    • Only trigger alerts during specific time windows (often night) when the person is likely at home.
    • Reject if recent family‑density estimates around the home exceed certain thresholds, or—per the reporting—accept even with high predicted civilian presence.7577

b. Event generation (“Where’s Daddy?”)

  • When conditions are satisfied (Lavender target, $P(\text{at home})$ above threshold, timing window open, target not recently struck), generate a WhereDaddy event:
    • Fields might include: target_id, home_location, confidence scores, suggested strike window, ISR evidence references, and collateral estimates.
  • Events are pushed to an operational UI used by intel officers and targeteers.

c. Human‑in‑the‑loop interface

  • Operators see a list or map of “ready” targets: “Target X is at home, confidence 0.9, last seen 10 min ago, family present (Y/N or unknown).”7775
  • According to whistleblower accounts, review time was often minimal (seconds), with humans mostly checking coarse attributes (e.g., gender) rather than re‑verifying ground truth or civilian risk.7577
  • On approval, the event is promoted to a fire mission request, which triggers standard fire‑control and weaponeering processes.

4. Strike execution integration

a. Tasking weapons platforms

  • Once authorized, the pipeline hands off to the C2 / strike system:
    • Target coordinates (home centroid or specific entrance).
    • Desired timing window.
    • Desired weapon type and yield based on target type and local constraints.
  • The C2 system then assigns aircraft, drones, or ground‑launched munitions to the mission.

b. Time‑sensitivity and tracking

  • Because the target might leave home, the pipeline may maintain a “strike window” TTL; if new signals indicate departure, it can auto‑update status to “no longer at home,” prompting abort recommendations.
  • In practice, whistleblowers suggest such safeguards were weak or underused in favor of throughput and rapid targeting.7775

5. Post‑strike assessment and feedback

a. Battle damage assessment (BDA)

  • Post‑strike ISR imagery and sensor data feed back to the system to update strike outcome: building destroyed, fires, ambulances, detected bodies, etc.
  • This may also update the person graph (e.g., marking target as “likely killed” or “confirmed KIA”) and household/family casualty records.

b. Model feedback loop

  • In a more sophisticated setup, the system could use BDA and later HUMINT/SIGINT to refine:
    • Presence models (e.g., when targets are typically at home).
    • Error estimates (e.g., how often a supposed presence at home was actually wrong).
  • Reporting suggests error rates of around 10% for Lavender’s initial classification, with WhereDaddy compounding this by using home strikes; whether these numbers are formally tracked as metrics is unclear.7577

6. System components (abstracted)

Putting this together, an engineer’s block diagram for a WhereDaddy‑like system would likely include:

  • Data ingestion bus: message queues / streams for telecom, SIGINT, ISR, intel DB updates.
  • Entity \& graph store: person–device–location–family graph, backed by a graph DB or ontology service.
  • Presence and behavior models: ML models and rule engines for “who is where when.”
  • Policy/ROE engine: encodes targetability rules, time windows, and any collateral constraints.
  • Target‑state service: maintains current state of each target (at home, abroad, in vehicle, unknown).
  • Alerting and UI: dashboards and APIs that surface “target at home” events to intel officers and feed C2.
  • Audit/logging: logs all inferences, decisions, overrides, and strike outcomes (at least internally), though whistleblowers suggest the human review layer was shallow.7775
⁂

  1. https://www.palantir.com/platforms/ ↩ ↩2 ↩3

  2. https://en.wikipedia.org/wiki/Palantir ↩ ↩2 ↩3 ↩4

  3. https://d3.harvard.edu/platform-digit/submission/palantir-a-software-that-safes-and-takes-lives/ ↩ ↩2 ↩3

  4. https://x.com/i/grok/share/Z6ITPk8UBunEHVtb5LRn1r2El ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10

  5. https://www.palantir.com/platforms/apollo/product/ ↩

  6. https://defensescoop.com/2024/05/29/palantir-480-million-army-contract-maven-smart-system-artificial-intelligence/ ↩ ↩2 ↩3

  7. https://breakingdefense.com/2024/03/palantir-wins-contract-for-army-titan-next-gen-targeting-system/ ↩

  8. https://www.defensenews.com/land/2025/03/07/palantir-delivers-first-2-next-gen-targeting-systems-to-army/ ↩

  9. https://www.reuters.com/technology/palantir-wins-480-million-us-army-deal-maven-prototype-2024-05-29/ ↩

  10. https://en.wikipedia.org/wiki/Palantir_Technologies ↩ ↩2 ↩3

  11. https://caat.org.uk/data/companies/palantir/ ↩ ↩2

  12. https://www.youtube.com/watch?v=99o-yV1eyp0 ↩

  13. https://financhle.com/articles/what-does-palantir-actually-do ↩

  14. https://www.bmj.com/content/392/bmj.s168 ↩

  15. https://www.youtube.com/watch?v=F-srFEeHET0 ↩

  16. https://en.wikipedia.org/wiki/Palantir ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  17. https://en.wikipedia.org/wiki/Palantir_Technologies ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8

  18. https://en.wikipedia.org/wiki/Palantir_Gotham?oldformat=true ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  19. https://defensescoop.com/2025/05/23/dod-palantir-maven-smart-system-contract-increase/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  20. https://defensescoop.com/2024/05/29/palantir-480-million-army-contract-maven-smart-system-artificial-intelligence/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  21. https://www.reuters.com/technology/palantir-wins-480-million-us-army-deal-maven-prototype-2024-05-29/ ↩ ↩2

  22. https://breakingdefense.com/2024/03/palantir-wins-contract-for-army-titan-next-gen-targeting-system/ ↩ ↩2 ↩3 ↩4

  23. https://www.theregister.com/2026/01/28/mod_palantir_deal/ ↩ ↩2

  24. https://www.publictechnology.net/2026/01/29/defence-and-security/mod-signs-240m-palantir-deal-as-ministers-insist-uk-defence-data-remains-sovereign/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  25. https://finance.yahoo.com/news/uk-mod-just-signed-240-145101136.html ↩ ↩2 ↩3 ↩4

  26. https://www.palantir.com/offerings/intelligence/ ↩ ↩2

  27. https://www.palantir.com/offerings/defense/community/ ↩

  28. https://www.statista.com/chart/34846/palantir-technologies-incs-annual-revenue-by-segment-and-by-country/ ↩

  29. https://www.appsruntheworld.com/customers-database/products/view/palantir-gotham ↩

  30. https://marketrealist.com/p/who-are-palantir-customers/ ↩

  31. https://en.wikipedia.org/wiki/SpaceX_Starshield ↩ ↩2 ↩3

  32. https://www.spacex.com/starshield/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11

  33. https://www.cnbc.com/2022/12/05/spacex-unveils-starshield-a-military-variation-of-starlink-satellites.html ↩

  34. https://www.space.com/spacex-starshield-space-force-contract ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10

  35. https://news.satnews.com/2022/12/05/spacex-introduces-the-starshield-secured-satellite-network-for-government-entities/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9

  36. https://newspaceeconomy.ca/2025/03/01/spacex-starshield-a-new-frontier-in-government-satellite-services/ ↩ ↩2 ↩3 ↩4 ↩5

  37. https://www.pcmag.com/news/spacex-wins-pentagon-contract-to-supply-military-communications ↩ ↩2

  38. https://www.airandspaceforces.com/space-force-contract-spacex-starshield/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  39. https://fedscoop.com/spacex-starlink-starshield-government-military-satellite-internet/ ↩ ↩2

  40. https://breakingdefense.com/2025/06/space-force-is-contracting-with-spacex-for-new-secretive-milnet-satcom-network/ ↩

  41. https://news.satnews.com/2025/12/29/u-s-space-force-and-spacex-partner-to-develop-480-satellite-milnet-constellation/ ↩

  42. https://www.defensemirror.com/news/38339 ↩ ↩2

  43. https://www.defensemirror.com/news/38339/Ukraine_Allowed_Access_to_Military_Satellite_Internet__Starshield ↩ ↩2

  44. https://www.bloomberg.com/news/articles/2024-12-06/spacex-gets-us-contract-to-expand-ukraine-s-access-to-starshield ↩

  45. https://defence-industry.eu/spacex-and-pentagon-expand-ukraines-access-to-secure-starshield-network-bloomberg-reports/ ↩

  46. https://palantir.com/docs/foundry/aip/overview/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16

  47. https://palantir.com/docs/foundry/platform-overview/overview/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  48. https://www.palantir.com/docs/foundry/platform-overview/aip-capabilities ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10

  49. https://www.palantir.com/docs/foundry/aip/overview ↩ ↩2 ↩3 ↩4

  50. https://www.toolify.ai/ai-news/unleashing-the-power-of-ai-in-military-operations-palantirs-aip-revealed-1410062 ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10

  51. https://www.youtube.com/watch?v=XEM5qz__HOU ↩ ↩2 ↩3 ↩4 ↩5

  52. https://unit8.com/resources/palantir-foundry-aip/ ↩ ↩2 ↩3 ↩4

  53. https://www.amcham.it/upload/documenti/1/10/102/1029/10293.pdf ↩ ↩2

  54. https://www.palantir.com/docs/foundry/aip/aip-security ↩ ↩2 ↩3 ↩4 ↩5

  55. https://palantir.com/docs/foundry/aip/aip-security/ ↩ ↩2 ↩3 ↩4

  56. https://www.youtube.com/watch?v=pGBkkZFqLn4 ↩ ↩2 ↩3 ↩4

  57. https://www.toolify.ai/ai-news/revolutionizing-defense-and-military-with-palantir-aip-19357 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  58. https://www.cnbc.com/2025/08/01/palantir-lands-10-billion-army-software-and-data-contract.html ↩

  59. https://www.palantir.com/platforms/aip/ ↩

  60. https://www.youtube.com/watch?v=Xt_RLNx1eBM ↩

  61. https://www.yahoo.com/news/israels-wheres-daddy-ai-system-093311866.html ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13

  62. https://www.democracynow.org/2024/4/5/israel_ai ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16

  63. https://substack.com/home/post/p-164261706 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  64. https://theconversation.com/gaza-war-israel-using-ai-to-identify-human-targets-raising-fears-that-innocents-are-being-caught-in-the-net-227422 ↩ ↩2 ↩3 ↩4

  65. https://www.linkedin.com/pulse/israels-wheres-daddy-system-hidden-risks-ai-combat-controism-gfr5e ↩ ↩2 ↩3

  66. https://www.instagram.com/reel/DOdkfRlgLGY/ ↩

  67. https://ia801704.us.archive.org/3/items/in.ernet.dli.2015.110201/2015.110201.The-Readers-Digest-Vol79july-dec1961_text.pdf ↩

  68. https://www.youtube.com/watch?v=4RmNJH4UN3s ↩

  69. https://www.debaser.it/p/379340 ↩

  70. https://www.youtube.com/watch?v=qE8UYhb5zRg ↩

  71. https://www.reddit.com/r/conspiracy/comments/1nfribk/leaked_reports_reveal_israeli_ai_system_wheres/ ↩

  72. https://www.reddit.com/r/IsraelPalestine/comments/1n7dzzt/can_anyone_verify_or_debunk_the_reports_of_a/ ↩

  73. https://www.facebook.com/61580086476346/videos/mommy-it-hurts-wheres-daddy-watch-more-/1439882717268194/ ↩

  74. https://dnsdumpster.com ↩

  75. https://www.yahoo.com/news/israels-wheres-daddy-ai-system-093311866.html ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11

  76. https://substack.com/home/post/p-164261706 ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  77. https://www.democracynow.org/2024/4/5/israel_ai ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13