FIELD NOTES
A little clarity goes a long way.
Practical decisions behind dependable AI, manufacturing workflows, and connected business software. Written by the Agentix editorial team.
AI strategy
3 MIN READCustom AI solutions in Nashville: how to scope the right first project
A practical Nashville buyer’s guide to custom AI: define the workflow, compare build options, verify data access, and set measurable acceptance criteria.
What should an AI audit and roadmap include for a Tennessee business?
Turn an AI audit into an implementation roadmap with workflow evidence, data readiness, priorities, owners, and clear decisions about what to build next.
Custom AI
3 MIN READOpenAI agent or workflow automation: which does your business need?
Compare custom agents, rules-based automation, and hybrid systems using task variability, tool access, review requirements, and operating complexity.
Enterprise AI knowledge search: how to build a useful RAG system
Plan enterprise knowledge search with source ownership, permission-aware retrieval, citations, freshness checks, and tests that separate search from answer quality.
AI document processing for business: from extraction to reliable review
Design AI document intake for invoices, orders, forms, and service requests with field validation, duplicate handling, exception queues, and human review.
AI adoption
3 MIN READAI team training that changes everyday work
Build role-specific AI training around actual tasks, source checking, review responsibilities, escalation, and measurable adoption after a new system launches.
Manufacturing
3 MIN READManufacturing AI in Tennessee: a practical readiness checklist
Assess manufacturing AI readiness across workflows, data, system access, plant responsibilities, and acceptance tests before choosing the first use case.
Manufacturing RFQ automation: prepare better quote packets with AI
Scope AI-assisted RFQ intake with revision checks, missing-information review, source references, and a clear separation between preparation and quote approval.
AI for manufacturing quality documents: control the version and the review
Plan AI-assisted quality documentation with controlled sources, traceable draft preparation, revision handling, and accountable review before release.
AI maintenance knowledge assistants: connect manuals, history, and review
Scope a maintenance knowledge assistant that retrieves approved manuals and service history, preserves equipment context, and avoids unsupported operating instructions.
ERP and MES integration: the data decisions manufacturers need first
Plan manufacturing system integration around authoritative records, event timing, revisions, reconciliation, and a clear boundary between reporting and control.
How to evaluate a manufacturing AI pilot before expanding it
Use a manufacturing AI pilot scorecard that measures complete work, correction effort, exception handling, adoption, and readiness for another site.
Software & operations
3 MIN READManaged AI support: what happens after your system goes live?
Define monitoring, incident response, evaluation, change control, and ownership before handing an AI workflow into everyday operations.
Business system integration architecture: start with ownership and events
Plan dependable integrations by defining systems of record, events, data contracts, reconciliation, and accountable operating teams.
CRM and ERP integration: decide who owns each field
Avoid conflicting customer, order, and invoice data by defining field ownership and exception rules before connecting CRM and ERP systems.
Reliable API integrations: retries, duplicate events, and reconciliation
Build integrations that survive ordinary failures with idempotency, bounded retries, durable work records, clear alerts, and reconciliation.
Enterprise custom software in Nashville: how to write a useful delivery brief
Scope enterprise software around users, system boundaries, acceptance evidence, migration needs, and long-term ownership.
Custom software or SaaS: compare the operating model before you buy
Evaluate configuration, integration, and custom development using process fit, ownership, portability, support, and realistic total effort.
From Dots Agents
Our separate publication covers OpenAI dots agents and business adoption. Dots Agents is operated by Agentix and is not an official OpenAI publication.
4 MIN READWhat is an OpenAI dot, and where does it fit at work?
A practical explanation of OpenAI dots, ongoing responsibilities, cloud computers, and the boundaries businesses should understand before adoption.
Your first dot: a setup and acceptance checklist
Plan a bounded first OpenAI dots workflow with source access, permissions, review criteria, and a repeatable acceptance test.
Cloud or local? Where your dot actually works
Understand cloud computers, local-device requirements, browser sessions, and task locations before assigning ongoing work to a dot.
Permissions, app access, and the right to take action
A business guide to separating connected accounts, app actions, instructions, custom rules, and OpenAI dots action review.
From a Slack message to an ongoing responsibility
Understand the difference between messaging a dot, assigning monitoring, saving a schedule, and stopping its different kinds of work.
Dots, automation, or a custom agent: which fits the job?
A decision framework for choosing personal dots, deterministic workflow automation, and custom agent systems based on business requirements.
A small-business guide to the first useful AI workflow
Choose a practical first AI workflow for a small business, define review boundaries, and assess the real effort of operating it.
AI adoption across departments: fix the handoff first
A practical guide for midsize businesses introducing agents across sales, operations, finance, and delivery without losing ownership.
An enterprise readiness plan for OpenAI dots agents
Plan an enterprise dots pilot around workspace access, app permissions, data boundaries, oversight, and evidence-based acceptance.
How to evaluate an agent before making the work recurring
Create meaningful acceptance cases for AI workflows and assess accuracy, source use, authorization, exceptions, and recovery.
Manufacturing AI: connect the information, respect the controls
An implementation framework for manufacturing AI across business systems and operational technology, including integration and safety boundaries.
Choosing an AI implementation partner in Nashville and Tennessee
A buyer checklist for comparing AI implementation partners on scope, integration, ownership, evaluation, and ongoing support.