AT&T
S.C.O.U.T. Network Operations Application System
Palantir
Telecommunications
Typical Forward Deployed model
Standard case | Useful reference with remaining gaps
Evidence level B
Scaled
B
Evidence level
AT&T: S.C.O.U.T. Network Operations Application System
Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.
Standard case | Useful reference with remaining gaps
9 / 12
Business context1 / 2
Transformation workflow1 / 2
Technical workflow2 / 2
Human roles & governance2 / 2
Measured outcomes2 / 2
Source traceability1 / 2
Remaining gaps: Business context, Transformation workflow, Source traceability
Business problem
Data and operating processes for large telecommunications networks are extremely fragmented and require engineering teams to build business applications on a continuous basis on the same platform.
Solution
Palantir worked with AT&T to build applications such as S.C.O.U.T. and used Foundry as the base for large-scale in-house applications.
Technical architecture & production workflow
Step 01
Multi-source operating data and historical events
→
Step 02
Foundry harmonized data models and privileges
→
Step 03
Online map of business objects, relationships, status and executable Action
→
Step 04
AIP/rules/prediction model generation recommendations or automated steps
→
Step 05
First-line personnel confirm and execute in Workshop/Application
→
Step 06
The feedback, the results and the new facts form a closed loop.
Key technology & infrastructure components
FoundryOntologyInternal application developmentGovernance of competencesOperational data
Human roles & accountability
AT&T has its own engineering team developing and operating applications on an ongoing basis, and Palantir is responsible for the platform ' s co-construction with the previous period.
FDE delivery actions
- Work with the first line of network operations to complete the process and identify nodes that really require decision-making rather than displaying data
- Collapse decentralized data sources, competencies and business terms into a single, operational Ontology
- Combine business rules, optimization models, LLM and traditional software according to a reliable boundary, instead of letting the LLM operate
- Insert recommendations directly into existing operating workstations and design approvals, rejections, upgrades and write back to Action
- Continue to modify Ontology, rules and automation with operational KPI acceptance and feedback
Reusable delivery patterns
- Let's be clear about the client, the relationship, the state and the action, and then talk about Agent.
- The recommendation must enter the executable stream, otherwise it's just another dashboard.
- High-value deployment is often a combination of data integration + rules/ optimization + AI + Human-in-the-lop
- Continuous rewriting of the results of implementation to build cumulative operational memory
Business outcomes & delivery results
Officially, S.C.O.U.T. grew from a joint project to 100+ AT&T full-time engineers for maintenance, with some 660 applications already available on Foundry.
Evidence boundaries & verification notes
- Palantir ' s network of open clients quoted the explicit disclosure of 100+ full-time engineers and about 660 Foundry applications; individual applications were not fully disclosed.
- The current link is the official aggregation entrance, and a deep link or page number that directly locates the case has yet to be added.