# AI Development & Automation | LLM Agents, RAG & Workflow Automation | Michigan

> AI development from Ann Arbor, Michigan: LLM-powered order processing and support agents, retrieval-augmented search over your catalog and documents, AI product recommendations, and workflow automation wired into your ERP, eCommerce, and CRM — with guardrails, logging, and measurable ROI.

**Keywords:** AI development company, AI development services, AI integration services, LLM integration, AI automation for business, RAG implementation, AI agents for ecommerce, AI customer service automation, OpenAI API integration, AI consulting Michigan, business process automation AI

**Source:** https://reeddynamic.com/services/ai-automation-integration/

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LLM agents · RAG · AI recommendations · Workflow automation

# AI development that removes work from your team — not a chatbot on your homepage.

Reed Dynamic has been connecting eCommerce, ERP, and CRM systems since 2010. AI is the newest component in that pipeline: models that read purchase orders, answer stock questions from live data, draft support replies from your policies, and enrich catalogs — with guardrails, logging, and a human in the loop where money moves.

[Book an AI readiness call](https://reeddynamic.com/contact/) · [See pricing](https://reeddynamic.com/pricing/)

### Where AI pays off first

- **Order entry from emailed POs/RFQs** → ERP orders, human-approved
- **"Is it in stock / when does it ship?"** answered from live inventory
- **First-response support** drafted from your policies and order history
- **Catalog enrichment**: attributes, descriptions, categorization at scale
- **Product recommendations** from attributes and purchase history
- **Ticket / lead classification and routing**

**Reed Dynamic provides AI development and automation services** for businesses in Michigan and nationwide: LLM-powered agents for order processing and customer support, retrieval-augmented generation (RAG) over product catalogs and documents, AI product recommendations, document extraction, and workflow automation — integrated with ERP, eCommerce, and CRM systems, with guardrails, evaluation, and logging. Typical projects run $7,500–$90,000.

## What we build

### LLM agents in your workflows

Agents that read, decide, and act inside bounded workflows — creating ERP orders from emailed POs, drafting quotes, triaging tickets — with approval steps wherever a commitment is made.

- Structured outputs
- Human-in-the-loop
- Audit log

### RAG over your data

Search and Q&A grounded in your catalog, SOPs, contracts, and order history. The model answers only from retrieved sources and cites them.

- pgvector / Pinecone
- Hybrid search
- Citations

### AI for eCommerce

Semantic site search, attribute-aware recommendations, catalog enrichment, and review summarization for Magento, Shopify, BigCommerce, and WooCommerce.

- Semantic search
- Recommendations
- Enrichment

### Document & email extraction

Invoices, POs, RFQs, BOLs, and forms parsed into structured records and pushed to your ERP or WMS — with confidence scores and exception queues.

- OCR + LLM
- Schema validation
- Exception queue

### Guardrails & evaluation

Evaluation datasets, regression tests on every prompt change, PII redaction, rate limits, cost controls, and full prompt/response logging.

- Evals
- Redaction
- Cost caps

### Private deployment

Azure OpenAI in your tenant or open-weight models (Llama, Mistral) on your infrastructure when data can't leave the building. BAA available for PHI.

- Azure OpenAI
- Open-weight
- BAA

## How we run an AI project

1. ### Readiness assessment
  Inventory candidate workflows, data sources, and volumes. Rank by hours saved × feasibility. Pick one.
2. ### Prototype on real data
  Two to three weeks against your actual documents and systems, measured on an evaluation set — not a demo.
3. ### Integrate with guardrails
  Wire into ERP/eCommerce/CRM with structured outputs, approvals, logging, and cost controls.
4. ### Measure and expand
  Baseline hours and error rate vs. after. Then the next workflow.

### Why an integration shop for AI?

The hard part of production AI isn't the model — it's the plumbing around it: reliable retrieval from your systems, schema-validated outputs into your ERP, retries, exception handling, and monitoring. That's the work we've done for a decade with [ERP and WMS integrations](https://reeddynamic.com/services/erp-integration/); the model is one more component in a pipeline we already know how to make boring and reliable.

[AI and machine learning in modern web applications →](https://reeddynamic.com/blog/ai-machine-learning-modern-web-applications/)[Leveraging AI in web development →](https://reeddynamic.com/blog/leveraging-ai-in-web-development-the-future-is-now/)

## AI integration FAQs

**

### What does "AI integration" actually mean for a mid-sized business?

**

Not a chatbot on the homepage. It means putting a language model inside a workflow you already run: reading inbound purchase orders and creating ERP orders, answering "is this in stock / when does it ship" from live data, drafting support replies from your policies, classifying and routing tickets, or recommending products from catalog attributes. The model does the reading and drafting; your systems remain the source of truth; a human approves anything with money or commitments attached.

**

### How much does an AI integration cost?

**

An AI readiness assessment is $2,500–$5,000. A focused production integration (one workflow, one model, guardrails, logging) typically runs $7,500–$30,000. Multi-step agents with RAG over large catalogs or document sets run $30,000–$90,000. Model usage fees are pass-through and usually small relative to the labor saved. See [pricing](https://reeddynamic.com/pricing/).

**

### Which AI models and platforms do you use?

**

OpenAI, Anthropic Claude, Google Gemini, and Azure OpenAI for hosted models; open-weight models (Llama, Mistral) on your own infrastructure where data can't leave your tenant. Vector search with pgvector, Pinecone, or OpenSearch. We pick per use case — accuracy, cost, latency, and data residency — not by brand loyalty.

**

### How do you keep the AI from making things up?

**

Retrieval-augmented generation (the model answers only from documents and data we retrieve), structured outputs validated against schemas, confidence thresholds that route to a human, evaluation sets we run before every change, and full logging of prompts and outputs. For anything transactional, the model proposes and a person or a deterministic rule approves.

**

### Is our data used to train models?

**

No. We use API tiers with no-training data policies, and where required deploy on Azure OpenAI or private open-weight models inside your cloud. Data handling is documented in the SOW and, for PHI, covered by a BAA.

**

### What ROI should we expect?

**

The strong cases are high-volume, repetitive reading-and-typing work: order entry from emailed POs, first-response support, catalog enrichment, RFQ triage. We baseline the current cost in hours, ship the integration, and measure hours removed and error rates — the same KPI discipline we use for ERP integrations.

## Which workflow eats the most hours?

Tell us the one your team complains about. We'll assess whether AI can take it and what it would cost — honestly.

            [Start a project](https://reeddynamic.com/contact/) · [Custom software & APIs →](https://reeddynamic.com/services/custom-software/)

Or call [(734) 535-8954](tel:+17345358954) · Mon–Fri 9–5 ET