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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Accenture’s AI Refinery is an enterprise framework and services offering—not a new standalone AI model. Announced on July 23, 2024, it combines Accenture’s model-customization and implementation work with NVIDIA AI Foundry to help businesses build and deploy custom models based on Meta’s Llama 3.1 and company data. The launch describes a route to tailored enterprise AI, but does not establish customer-specific cost, performance, or production outcomes.
What Accenture announced
Accenture introduced AI Refinery as part of its foundation model services. It said clients could use the framework to adapt foundation models to their data and business processes, and said it would also use the framework internally, beginning with marketing and communications. NVIDIA identified Accenture as the first adopter of AI Foundry for custom Llama 3.1 models for internal and client use.
The announcement came the same day Meta released the Llama 3.1 collection. The service announcement is therefore about combining an existing model family with enterprise customization and deployment support; it is not a claim that Accenture created Llama 3.1. Accenture’s July 23, 2024 announcement and NVIDIA’s announcement describe the partnership from each company’s perspective.
How AI Refinery is described to work
Accenture outlined four parts of its framework. These are the company’s product descriptions, not independently audited capabilities or proof that every deployment includes every element.
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Customize models for a business domain
Accenture described refining prebuilt foundation models with a client’s data and processes. The objective is to adapt a model to a particular organization or use case rather than train a new general-purpose model from scratch.
Choose among models with Switchboard
The Switchboard platform is described as selecting a model—or combination of models—according to business context and factors such as cost or accuracy. The launch announcement does not provide a model catalog, selection methodology, or comparative results.
Make corporate information available through an enterprise index
Accenture’s “enterprise cognitive brain” is described as scanning and vectorizing corporate data into an enterprise-wide index. NVIDIA separately described NeMo Retriever microservices for retrieval-augmented generation (RAG), a method that retrieves relevant information to accompany a model’s response. The releases do not specify how a particular client’s data is handled, governed, or permissioned.
Build agentic systems
Accenture described an agentic architecture in which systems can reason, plan, and propose tasks for execution with minimal human oversight. That description should not be read as evidence that autonomous actions were deployed without human review. Buyers need to establish what actions an agent may take, which require approval, and how activity is logged and monitored.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhat NVIDIA AI Foundry contributes
NVIDIA characterized AI Foundry as an end-to-end model service combining its software, infrastructure, and expertise with open community models and its partner ecosystem. Its launch release named several technical components used in the custom-model workflow:
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
- NeMo: customization of models.
- Llama 3.1 405B and Nemotron-4 340B: models NVIDIA said could be used to generate synthetic training data.
- NIM inference microservices: serving models for inference.
- NeMo Retriever microservices: supporting retrieval-augmented generation.
NVIDIA said custom models could be deployed through customers’ preferred cloud and MLOps/AIOps platforms, including on NVIDIA-Certified Systems. The releases describe overlapping parts of a service stack, but AI Refinery and AI Foundry are distinct offerings; the announcements do not specify a single standard architecture or customer contract.
What Llama 3.1 means in this announcement
NVIDIA’s July 2024 release described Llama 3.1 in three sizes: 8B, 70B, and 405B parameters. It said the collection was trained on more than 16,000 H100 GPUs. Those figures describe the model family and its training, not the hardware or resources a particular AI Refinery customer must use.
NVIDIA also said Llama 3.1 NIM microservices could deliver “up to 2.5x higher throughput” than inference without NIM. This is a vendor claim in the 2024 launch announcement, not an independently verified benchmark for Accenture implementations; the release does not establish that a customer will see that result under its own workload.
How the offering expanded in 2025
AI Refinery for Industry
On January 6, 2025, Accenture announced AI Refinery for Industry with 12 initial agent solutions and said it planned to expand the collection. Examples included revenue growth management for consumer goods, a clinical trial companion for life sciences, industrial asset troubleshooting, and B2B marketing. Accenture said the platform was available on public and private cloud platforms. It also reported that more than 600 of its marketing professionals were using agents with access to over 20 data sources; those are company-reported deployment details, not independently verified results. Accenture’s January 2025 release gives the examples and stated availability at that time.
Agent builder and industry examples
In a March 18, 2025 announcement, Accenture described a no-code agent builder for business users to create or customize agent teams, with governance and guardrails it said were built in. It also named several initiatives with different statuses:
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- Blackwell Architecture
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- ESPN’s FACTS avatar was described as a research-and-development pilot with SEC Nation.
- HPE was developing a solution with HPE Private Cloud AI.
- Noli was described as an AI-powered beauty shopping platform built with Accenture.
- The United Nations was working with Accenture to develop a multilingual research agent.
“Pilot,” “developing,” and “working to develop” are not equivalent to a generally available product or a verified, completed deployment. Accenture also listed agent use cases in telecom call-center assistance, insurance underwriting, order-to-cash, and commercial credit sales intelligence, and said the platform was available across public and private cloud platforms. The March 2025 announcement is the source for these plans and examples.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the announcements do—and do not—show about results
Accenture’s March 2025 release attributed several performance figures to a telecom agent-assist solution: 25× faster call processing, a 2.6× improvement in call efficiency, and a 24% improvement in overall call accuracy. Those figures are company-reported claims; the announcement does not provide independent validation in the materials cited here. It also estimated that as much as 50% of property-and-casualty insurance submissions were left untouched in traditional processes. That is Accenture’s estimate, not a measured result of AI Refinery.
The same release cited separate Accenture research saying slightly more than one third of organizations had scaled at least one industry-tailored solution for a core process, and that those organizations were three times more likely to have exceeded expected ROI. This is a secondary citation to underlying research, not a finding about AI Refinery customers specifically.
Accenture’s 2025 releases also described plans to expand its agent offerings: January’s announcement referred to 12 initial solutions, while March’s said the company was developing more than 50 industry-specific agent solutions and aimed to exceed 100 by the end of 2025. These are dated company plans, not evidence that the year-end target was reached. The official launch and follow-up announcements do not provide an independent head-to-head evaluation of AI Refinery.
What an enterprise buyer should verify
The releases leave important purchasing and implementation details open. A buyer assessing AI Refinery should ask for specifics tied to its own workloads and deployment requirements rather than treating launch descriptions as a performance guarantee.
- Data handling and customization: Which data is used for customization, training, or retrieval? Where is it stored and processed, and what access controls and retention terms apply?
- Model choice: Which models are supported for the proposed use case, how does model selection work, and can the organization switch models without rebuilding its integrations?
- Deployment and sovereignty: Which cloud platforms and regions are available for the actual engagement, and what customer controls apply to data location and operations?
- Quality and safety: How are outputs evaluated, retrieved sources checked, agent actions constrained, and failures escalated to people?
- Integration and operations: What work is required to connect corporate systems, identity and permissions, monitoring, and the customer’s MLOps/AIOps processes?
- Economics: What are the implementation and ongoing costs, and what latency, throughput, and accuracy does the proposed system achieve on representative customer workloads?
The launch announcements mention cost or accuracy as model-selection factors and describe cloud deployment options, but do not provide customer-specific pricing, comparative performance, or detailed governance terms. Availability, supported models, regions, and commercial terms should be confirmed for the proposed engagement.
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