Shaping the Future of Work With Agentic AI
Aentic AI demonstrates a retrieval-augmented workflow that turns sales planning from days into minutes. VAS built a sales research AI agent in three days using the NVIDIA IQ blueprint and an accelerated AI data platform. The pipeline uses Nemo Retriever to continuously extract, embed, and index data for fast semantic search, then an agent drafts an outline, queries CRM/multimodal knowledge bases and internal tools, and Llama Nematron converts the outline into a step-by-step sales plan. Outcome: rapid, production-oriented agent creation and per-employee specialization enabled by Vast's accelerated AI data platform.
Key technical points
- Aentic AI positions AI to change how businesses use data to make decisions.
- VAS developed a sales research AI agent in three days, indicating rapid prototyping capability.
- The implementation used the NVIDIA IQ blueprint as an architectural/implementation reference.
- Deployment ran on an accelerated AI data platform (referred to as Vast's accelerated AI data platform).
- The platform uses Nemo Retriever as the retrieval component.
- The data platform continuously extracts, embeds, and indexes data to support fast semantic search.
- The agent's workflow begins by drafting an outline before detailed plan generation.
- The agent integrates with CRM systems, multimodal knowledge bases, and internal tools as data sources.
- After outline generation, Llama Nematron is used to transform the outline into a step-by-step sales plan.
- The end-to-end workflow reduces sales planning time from days to minutes, starting with an AI prompt and finishing with a plan.
- The platform is presented as enabling creation of specialized agents for every employee, implying scalable per-user agent instances.
- The architecture sequence described: continuous data ingestion and embedding -> semantic retrieval (Nemo Retriever) -> outline drafting -> final plan generation (Llama Nematron).
Use the NVIDIA IQ blueprint and an accelerated AI data platform as an architectural template when building retrieval-augmented agents (explicit in the source). Implement a continuous data pipeline that extracts, embeds, and indexes enterprise data to enable fast semantic search (explicit). Select a retrieval component compatible with the pipeline—source uses Nemo Retriever for semantic retrieval (explicit). Design agent logic to first produce an outline, then perform targeted retrieval from CRM, multimodal knowledge bases, and internal tools before final generation (explicit sequence). Choose a generation model to convert structured outlines into executable step-by-step plans—source uses Llama Nematron for this role (explicit). Plan for rapid prototyping and iteration: the example demonstrates a three-day build for a working sales research agent (explicit). Anticipate operational scaling to support specialized agents per employee, which implies per-user data access, indexing, and model invocation patterns (implied by source's claim of per-employee agents). Measure outcome in task latency and business time savings: the source reports shifting sales planning from days to minutes using the end-to-end flow (explicit).
The described architecture (NVIDIA IQ blueprint + accelerated AI data platform + Nemo Retriever + Llama Nematron) is a concrete stack Japanese engineering teams can evaluate against local infrastructure constraints (explicit components listed in source). Integration points called out—CRM systems, multimodal knowledge bases, and internal tools—map directly to typical enterprise systems in Japanese companies and should be prioritized for connectors (explicit). The example's three-day prototype timeline demonstrates feasibility for fast pilots in Japanese enterprise procurement and pilot cycles (explicit timeline). The platform's ability to reduce sales planning from days to minutes aligns with business-impact goals common in Japanese sales organizations and supports arguments for internal adoption (explicit outcome). The claim that the platform enables specialized agents for every employee is relevant for large Japanese firms considering scale and per-user customization; evaluate scaling of indexing, retrieval, and model costs accordingly (explicit platform capability).