NeoTek Solutions designs and builds AI solution architectures for organizations in Nashville, across Middle Tennessee and throughout the US. An AI solution architecture is the blueprint for how models, data, applications and controls fit together in a working system. It decides where data lives, how the AI reaches it, who can see what and how the system is tested and run. Our engineers use the patterns below on client projects every day.

Each page below covers when a pattern fits, its components, how requests flow, required security controls and a reference stack by cloud.


How NeoTek Solutions Designs AI Systems

Start From the Business Problem

We begin with the decision, task or customer interaction you want to improve. Then we look at the data, systems and people involved. The architecture follows from that problem, not from a favorite tool.

Choose the Simplest Architecture That Works

A search index with a good prompt often beats a complex multi-agent system. We add components only when a clear requirement needs them. Simpler systems are cheaper to run, easier to secure and easier for your team to support.

Design for Security and Operations From Day One

Access control, logging, evaluation and monitoring are part of the first design, not a later phase. We plan how the system will be tested, deployed, observed and improved. This keeps a promising pilot from stalling before production.

Stay Vendor-Neutral

We build on Microsoft Azure, AWS, Google Cloud, open-source software and on-premises infrastructure. We recommend commercial or open-source models based on your cost, privacy and hosting needs. You own the design, the code and the choices.


Generative AI and Agent Architectures

Retrieval-Augmented Generation (RAG)

We build retrieval-augmented generation (RAG) so a large language model looks up passages in your own content before it answers. It is the pattern we reach for first when people need knowledge search and document answers with citations. Explore the RAG architecture.

Agentic AI

We build agents that use a language model to plan steps and call tools until a task is finished. This architecture covers the single agents and multi-agent workflows we build, plus the approvals and audit trails we put around them. Explore the agentic AI architecture.

LLM Gateway and Guardrails

We put a central gateway between your applications and every AI model you use. It handles routing, sensitive-data redaction, content filtering, cost limits and logging in one place. Explore the LLM gateway and guardrails architecture.

Conversational AI and Contact Center

We use this pattern to build chat and voice assistants that answer questions, complete tasks and hand off to your live agents. It connects speech services, language models, knowledge search and your CRM. Explore the conversational AI architecture.


Data and Machine Learning Architectures

Intelligent Document Processing

We build pipelines that turn your forms, invoices, claims and letters into structured data. They combine text extraction, AI classification and review by your staff for low-confidence fields. Explore the intelligent document processing architecture.

MLOps Platform

We use MLOps to build, deploy and monitor machine learning models in a repeatable way. The platforms we build cover feature pipelines, experiment tracking, model registries, deployment and drift monitoring. Explore the MLOps platform architecture.

Data Lakehouse

We build lakehouses that combine low-cost data lake storage with the structure and governance of a data warehouse. That gives your analytics, reporting and AI workloads one trusted data foundation. Explore the data lakehouse architecture.

Computer Vision and Edge AI

We build computer vision systems that interpret images and video, such as spotting defects on a production line. We often run those models on edge devices near the camera instead of in the cloud. Explore the computer vision and edge AI architecture.


Engineering Architectures

AI-Assisted Software Delivery

This is how our engineers use AI across the software development lifecycle. Coding assistants, AI-generated tests and AI review support the work, while experienced engineers review every change. Explore the AI-assisted software delivery pipeline.


How We Choose an Architecture

  1. Define the outcome

    We agree on the task, the users and how success will be measured.

  2. Map the data

    We identify sources, owners, quality issues, sensitivity and access rules.

  3. Set the constraints

    We capture compliance needs, hosting preferences, budget and your team’s existing skills.

  4. Compare options

    We weigh simpler approaches first, such as search, rules or a standard product feature.

  5. Prove it with real data

    A focused proof of concept tests the riskiest assumption before a full build.

  6. Plan for operations

    We design monitoring, evaluation, support and cost controls before launch.

Many projects combine patterns, such as a contact center assistant that uses RAG, a gateway and an agent. If you are still deciding where AI fits, start with AI strategy consulting.


How Can NeoTek Solutions Help With Your Architecture?

Reading about patterns is the easy part. Fitting one to your data, budget and risk is where most teams want help.

  • What we buildKnowledge search and copilots, AI agents with approvals, document pipelines, conversational assistants, lakehouses, MLOps platforms and computer vision systems.
  • Built on your platformsWe design on Azure, AWS, Google Cloud, open-source software and on-premises infrastructure, working with the systems and data you already run.
  • How we workShort cycles with AI-assisted delivery and human review, success criteria agreed early and working software you can try on your data, so scope and cost stay visible.
  • Skills on the teamAI and machine learning engineers, data engineers, cloud and security specialists and QA in one team, so models, pipelines, integration and testing are all covered.
  • What makes us differentWe are vendor-neutral rather than reselling one platform, so the architecture follows your requirements instead of a product we need to place.
  • Security and ownershipAccess control, redaction, evaluation and audit logging are in the first design, you own the code and your data never trains public models.

Tell us the problem you want to solve and the constraints around it. Book a free AI consultation and an engineer will talk through the options with you.


Frequently Asked Questions

What do you deliver when you design an AI architecture?

We deliver the design of your AI system’s parts and how they connect: data sources, models, applications, integrations, security controls, evaluation and monitoring. We design it to be reliable, secure and practical for your team to operate. You own the design, the code and the choices.

Which architecture would you start us with?

We choose the simplest pattern that solves a real problem for you, based on your data, risk and goals. For many clients that is retrieval-augmented generation for knowledge search, or document processing for paperwork. Our guide to RAG architectures explained shows how RAG itself grows from basic search to AI agents.

Do you work with Azure, AWS and Google Cloud?

Yes. We are vendor-neutral and design AI systems on Microsoft Azure, AWS, Google Cloud, open-source software and on-premises infrastructure. We recommend platforms and models based on your requirements and existing investments, not on what we resell.

How do you keep the AI systems you build secure?

We design security in from the start. That includes role-based access control, encryption, sensitive-data redaction, audit logging and human review for high-risk actions. Our AI governance and security practice adds the policies and ongoing monitoring around it.

Can you build these for regulated industries such as healthcare?

Yes. We design for requirements such as HIPAA and work within your compliance program, though we claim no certification and give no legal advice. We support business associate agreements with model providers where required. See our approach for healthcare and financial services.


Talk Through Your Architecture With an Engineer

Bring your use case, data sources and constraints. We will sketch a practical architecture and a sensible first step. Talk to an AI architect

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