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AI Agent Consulting Case Study

This case study shows how Nextbrick designs, builds, and deploys enterprise AI agent systems that combine retrieval, orchestration, memory, and action layers. The implementation patterns below are published as visible page content so they can be read by users, search engines, and AI retrieval systems.

Past Project Case Study: Empowering Sales Associates with Agentic GenAI

A retail sales organization needed faster product discovery, customer-history awareness, and better recommendation quality for store associates. Product catalog data lived in Azure Blob Storage, purchase history lived in Azure Cosmos DB, and warehouse data came from inventory systems. The client needed one AI agent experience that could unify this fragmented data and return practical, explainable recommendations in real time.

The Challenge

• Sales associates struggled to navigate a catalog of more than 50,000 SKUs spread across disconnected systems.
• Customer history was locked in one system while inventory data lived in another, creating data silos.
• Manual search took 3 to 5 minutes per customer interaction, increasing latency on the sales floor.
• Cross-sell and upsell opportunities were missed because the system could not surface relevant recommendations at the right moment.

The Solution

Nextbrick deployed an agentic retrieval architecture built on Azure and Elasticsearch to unify structured and behavioral data, improve relevance, and support multi-step reasoning. The platform combined Elasticsearch Enterprise indexes, Azure Databricks data enrichment, Azure Machine Learning embeddings, GPT-4o on Azure OpenAI, and Azure Functions orchestration.

• Unified indexing of catalog, purchase, and inventory data into enterprise-grade search and relationship indexes.
• Agentic RAG controller with hybrid scoring using BM25 plus vector search.
• Context-aware reranking based on user history, query rewriting, and multi-step planning.
• Sales assistant user interface built for personalized recommendations and real-time product guidance.

Business Impact

• 30% increase in conversion rate within 90 days.
• 40% reduction in sales cycle time.
• 15% uplift in customer retention.
• Store staff became faster product experts because the AI agent returned grounded, actionable answers instead of forcing manual lookup.

Current Architecture Assessment for Enterprise Support Modernization

In another enterprise support environment, the existing stack relied on unmanaged infrastructure on AWS with EC2, Elastic Beanstalk, Kafka, Spring Boot query services, and legacy search logic based on field boosting, wildcards, and filters. That architecture exposed both technical limitations and direct business pain points.

Technical Limitations

• No semantic understanding. Keyword matching alone failed for natural-language questions and intent-rich queries.
• Rigid query logic. Relevance changes required code updates instead of runtime tuning.
• No reasoning engine. The architecture connected data directly to the UI without an agent layer to choose tools, indexes, or actions.
• High operational overhead from managing Kafka, Spark, and Elasticsearch infrastructure manually.

Business Pain Points

• Users received lists of links instead of direct answers.
• The experience lacked session context and memory, so the system did not understand prior questions or user state.
• Low relevance and no action layer increased case creation and support effort.

Search vs. Agentic Action

Legacy support search often returns passive retrieval results that force the user to read multiple links. A modern agentic support architecture can route intent to dedicated indexes, perform semantic retrieval, use keyword retrieval when needed, and then take action. In practice, that means an AI agent can look up an ERP order, retrieve a calibration certificate status, summarize a knowledge article, send an email update, or create a Salesforce case from the same interface.

• Dedicated indexes for specific business domains such as assets, certificates, orders, and knowledge.
• Multiple retrieval modes including agentic action, semantic RAG, and deterministic keyword search.
• Active execution across APIs and enterprise systems rather than passive content lookup only.
• Lower friction for end users because the platform delivers completed outcomes, not just documents.

Elastic Agentic Architecture on AWS

Nextbrick also designs AI agent consulting solutions for AWS environments using private VPC deployment patterns, ECS ingest microservices, Kafka and Spark processing, Lambda orchestration, embedding generation, self-hosted Elasticsearch clusters, agent memory, routing layers, and open-weight language models such as Mistral. These systems can connect to Coveo, Microsoft SQL Server, Confluence, Salesforce, S3, Adobe, Snowflake, and other enterprise platforms.

• Reasoning loops with tool orchestration and multi-step workflows.
• Secure multi-index retrieval for grounded answers and reduced hallucination risk.
• Conversational memory for multi-turn support and sales interactions.
• Action capabilities including case creation, email notifications, order lookup, and status retrieval.

Solution Detail and Execution Flow

A production AI agent architecture typically shifts the user experience from legacy search to agentic AI, from keyword match to semantic intent, and from static links to automated actions. The execution flow starts with user intent interpretation, continues through tool selection and retrieval, analyzes tool responses, and returns a final grounded answer or triggers the next action. Depending on the use case, the agent layer may include planner agents, executor agents, memory agents, supervisor agents, workflow orchestration, notifications, external action APIs, observability, and retrieval systems such as Elasticsearch.

Multi-Agent AI Architecture Patterns

For advanced enterprise use cases, Nextbrick designs multi-agent systems in which a router agent interprets the incoming request, a supervisor coordinates tasks, specialized agents perform search or external actions, and report-writing or response agents assemble the final output. Knowledge bases can span audio, image, text, and structured data, while evaluation layers determine whether the answer is strong enough for direct delivery or should fall back to another model or workflow.

Why This Matters for AI Agent Consulting

This is the practical work behind AI agent consulting: architecture assessment, index design, retrieval strategy, orchestration design, memory planning, model routing, API integration, observability, and measurable business outcomes. Nextbrick helps enterprises move from isolated chat demos to production AI agents that can search, reason, and act across real systems.

AI Agent Consulting Deliverables

• Current-state architecture assessment and gap analysis.
• Target-state agentic architecture for AWS, Azure, or hybrid enterprise stacks.
• Retrieval, embedding, reranking, and index strategy for structured and unstructured data.
• Agent workflow design for support, commerce, operations, and internal knowledge use cases.
• Implementation roadmap with KPIs, governance controls, and production rollout milestones.

If your team is evaluating AI agent consulting services, agentic RAG architecture, enterprise search modernization, or multi-agent execution workflows, Nextbrick can design and implement a production-ready solution aligned with your business systems and operating model.

On-Prem and Hybrid AI Agent Consulting

Nextbrick helps enterprises evaluate when AI agent consulting should use on-prem infrastructure, hybrid architecture, or cloud-first deployment. The content below is intentionally published as visible HTML text for users, search engines, and AI retrieval systems, with architecture, cost, and operational guidance for real production agent systems.

Reasons and Benefits to Go On Prem in Santa Clara for AI Agent Consulting

1. Lower long-term cost at scale. Fixed hardware cost can become materially cheaper than per-token, per-call, or per-hour cloud pricing for high-volume agent workflows.
2. Ultra-low latency. Models, vector search, memory, and tool execution stay on the same network for faster reasoning loops and more responsive agents.
3. Unlimited token usage within fixed infrastructure. Agents can reason, retry, self-reflect, and plan without constant token-cost pressure.
4. Full data control and ownership. Prompts, embeddings, logs, and intermediate reasoning remain inside the enterprise environment.
5. No vendor lock-in. Teams retain the ability to switch LLMs, vector databases, orchestration frameworks, and supporting tools.
6. Higher reliability for critical workloads. Operations are less exposed to API rate limits, third-party service disruptions, and cloud outages.
7. Custom agent architectures. On-prem deployments support long-running, tool-heavy, multi-agent, and highly customized orchestration patterns.
8. Predictable performance and predictable cost. Capacity planning is clearer for production agent workflows with stable utilization.
9. Security by design. No shared prompt-logging infrastructure and lower multi-tenant exposure for sensitive enterprise workloads.
10. Better memory and context handling. Large vector stores and long-context pipelines can grow without escalating cloud retrieval charges.
11. Enterprise trust and sales advantage. Data-sovereign AI deployments can strengthen procurement, legal, and customer confidence.
12. Optimized GPU utilization. Enterprises avoid paying for idle cloud premium and can tune inference infrastructure around actual demand.
13. Offline and air-gapped capability. The architecture can operate in disconnected or restricted environments where cloud access is limited.
14. Long-term strategic control. Infrastructure becomes an owned platform asset instead of a recurring usage liability.

AI Agent Consulting Cost Comparison: Cloud vs On-Prem

For enterprises operating agent workflows at scale, on-prem AI agent architecture can produce large cost deltas over one-year and five-year periods. The planning model below reflects a scenario comparing Elastic Cloud plus premium hosted LLMs against an on-prem Elasticsearch and open-weight LLM deployment in Santa Clara, California.

In this comparison, the on-prem architecture produces cost savings ranging from about 7M to 54M in one year and about 37M to 297M across five years, depending on the cloud model and hosted LLM combination being replaced.

Solutions Comparison for Enterprise AI Agents

Save on Your LLM Bill with Nextbrick Agentic AI

For organizations with heavy AI agent usage, cost optimization is not just about choosing a cheaper model. It depends on architecture, retrieval strategy, inference placement, and infrastructure ownership. Nextbrick’s AI agent consulting includes cloud-cost analysis, migration planning, model strategy, and infrastructure design to reduce annual and multi-year LLM spending.

• OpenAI-related scenarios can save up to about 52M in one year and up to about 300M over five years when large-scale hosted usage is replaced by an on-prem or optimized architecture.
• Anthropic-related scenarios can save up to about 10M in one year and up to about 50M over five years.
• Gemini-related scenarios can save up to about 3M per year and up to about 15M over five years.

Proposed On-Premise AI Agent Architecture in Santa Clara, California

A representative on-premise AI agent architecture in Santa Clara includes enterprise data sources such as AEM, Confluence, Salesforce, PIM, Skilljar, Oracle, Snowflake, SharePoint, SQL Server, and Adobe. These systems feed a custom ingest and processing layer built with Java, Scala, Python, Kafka, and Spark. Search runs on Elasticsearch 9.x search nodes, while the agent and inference cluster uses GPU LLM nodes running open-weight models such as gpt-oss-20b, embeddings such as BGE-M3, and orchestration layers such as LangChain and LangGraph.

• Search nodes cluster with enterprise Elasticsearch for retrieval and user context.
• GPU inference nodes for agent execution and model serving.
• Shared NAS storage for large-scale retained data and snapshots.
• High-throughput 10GbE or 25GbE networking for low-latency coordination between ingest, search, and inference.

On-Prem Hardware Diagram for AI Agents

A typical hardware footprint for this architecture includes 2 GPU LLM nodes with multi-GPU acceleration, 5 dedicated search nodes, high-speed networking, and 80TB NAS storage. This supports large vector indexes, search performance, memory retention, and enterprise-grade orchestration for AI agents running in production.

Hybrid AI Agent Architecture: AWS and On-Prem Santa Clara

When full on-prem migration is not the right immediate step, Nextbrick designs hybrid AI agent architecture that keeps sensitive search, embeddings, and inference on-prem while using AWS for selected ingest, processing, or user interaction services. This approach gives enterprises a middle path between full cloud dependency and full on-prem ownership.

• AWS can host managed ingest services, streaming, API gateway, and user interaction layers.
• On-prem Santa Clara infrastructure can host search nodes, embedding generation, user context, and GPU LLM inference.
• Hybrid connectivity allows data flow between the AWS cloud boundary and the on-prem data center while preserving control over the most sensitive or expensive workloads.

What Nextbrick Delivers in AI Agent Consulting

• Cloud-versus-on-prem economics assessment for agent workloads.
• Architecture selection across on-prem, hybrid, and cloud deployment models.
• Infrastructure sizing for search nodes, GPU nodes, storage, and networking.
• Model, embedding, and orchestration strategy for enterprise AI agents.
• Migration roadmap to reduce LLM spend while improving control, latency, and reliability.

If your team is evaluating AI agent consulting for cost reduction, on-prem AI architecture, hybrid agent deployment, enterprise search modernization, or large-scale agent execution, Nextbrick can design the operating model, infrastructure, and implementation roadmap.