LLM agents · RAG · AI recommendations · Workflow automation
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.
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.
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.
Search and Q&A grounded in your catalog, SOPs, contracts, and order history. The model answers only from retrieved sources and cites them.
Semantic site search, attribute-aware recommendations, catalog enrichment, and review summarization for Magento, Shopify, BigCommerce, and WooCommerce.
Invoices, POs, RFQs, BOLs, and forms parsed into structured records and pushed to your ERP or WMS — with confidence scores and exception queues.
Evaluation datasets, regression tests on every prompt change, PII redaction, rate limits, cost controls, and full prompt/response logging.
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.
Inventory candidate workflows, data sources, and volumes. Rank by hours saved × feasibility. Pick one.
Two to three weeks against your actual documents and systems, measured on an evaluation set — not a demo.
Wire into ERP/eCommerce/CRM with structured outputs, approvals, logging, and cost controls.
Baseline hours and error rate vs. after. Then the next workflow.
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; 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 →
Leveraging AI in web development →
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.
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.
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.
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.
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.
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.