DOE · DOE-495 · record R3357

LANL AI Portal

Department of Energy · LANL - Los Alamos National Lab (LAFO)

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: General purpose business system
Start date: 2025-01-06 (day precision)Source: 2025-01-06T00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Administrative Functions
Operational functions: Software development assistance, Search and knowledge retrieval Derived by keyword rules; see methodology

Problem the AI is intended to solve

"Democratized access to open-source/open-weights Large Language Models (LLMs) for general purpose office productivity, research of AI models, software development, operational streamlining, code development "

Expected benefits

Democratized access to open-source/open-weights Large Language Models (LLMs) for general purpose office productivity, research of AI models, software development, operational streamlining, code development

System outputs

Interactive text chat replies from user prompts, summaries of documents user submitted for Retrieval Augmented Generation (RAG), Replies to API queries from enterprise and scientific applications

Technology

AI classification: Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
System name(s): Authorization to Operate Amazon Web Service Federal Risk and Authorization Management Program Infrastructure as a Service Cloud Service Provider, Amazon Web Service Information System Security Plan, SD-006-CP-002/L2
Custom-developed code: Yes
Public source code: github.com ↗

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Amazon Web Services (hosting provider)
Vendors (standardized): Amazon Web Services

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: Not trained in-house, using open-source/open-weights models. Reliant on model provider transparency
Federal Data Catalog: Not reported

Governance

8 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingReported in placeSource: a) Yes

AI impact assessmentReported in progressSource: b) In-progress

Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processNot applicableSource: b) Not applicable

User and public consultationReported in placeSource: a) Direct usability testing

Potential impacts and how they were identified

Positive impact on laboratory cost/time efficency by reducing compliance burden on internal teams