Zhongshan Peter Artificial Intelligence Technology Co., Ltd.
Find the workflow most worth rebuilding with AITurn it into your company's own operating system
No tool pitch first. No model talk first. We first judge which workflow is frequent, painful, measurable, and controllable,
then run an agent pilot and turn the experience into SOPs, knowledge bases, and exception case libraries.
Enterprise AI consulting, AI Agent implementation, and enterprise knowledge systems for SMEs. PeterAI does not promise operating results on a client's behalf.
FDE Scene Screening
Prioritize frequent, painful workflows and verify ROI fast
Workflow Redesign
Standardize key nodes and build executable SOPs
Enterprise Knowledge Base
Turn operating experience into a shared enterprise knowledge foundation
Agent Deployment
Launch pilots quickly and let people work with agents
GEO Brand Building
Build credible content and evidence that AI search can understand and cite
Risk Control
Keep permissions, review, and compliance under control
Acceptance Review
Quantify outcomes and keep improving
Peter AI Tech Consulting CEO: Peter
AI Era Enterprise Management Consultant · Agent Architect
We do not deliver PPT plans.
We make AI run inside workflows,
and let the results speak.
Why am I the perfect
AI Architect for CEOs?
15 Years of Cognitive Evolution: From foundational logic to macro insights, to AI implementation.
Foundational Logic
Researching brain science and cognitive psychology, deciphering 'highly efficient learning & memory systems'.
Frontline Factory Experience
Bank Relationship Manager. Wore out countless shoes diving deep into factory frontlines to understand business ecosystems.
Macro Industry Insights
Securities Industry Researcher. Read through endless reports, seeing through the profit pools of all industries and the VC market.
Productization & Operations
Education Consulting Operator. Transforming non-standard experience into K12 full-stack Agent products.
Enterprise Restructuring
Founded Peter AI, becoming an AI-Native OPC and FDE (Forward Deployed Engineer).
PeterAI does not sell tools,we help companies rewrite how work gets done
The first question in enterprise AI transformation is not "which model to use", but "which workflow is worth rebuilding with AI, who owns acceptance, how failure is handled, and how experience is captured".
No AI tool shopping list
Identify the business workflow most worth rebuilding with AI
No one-off training theater
Teach leaders and teams how to dispatch, review, and iterate
No promise of instant full AI transformation
Start with a frequent, painful, measurable small loop
No layoff story
Move people from low-value actions to judgment, relationships, and exception handling
Not every workflowshould be AI-transformed immediately
PeterAI first judges whether the business is worth it, the organization can absorb it, and the risk is controllable. The first step is not choosing a model, but choosing the first acceptably testable small loop.
Five Screening Questions
Before AIDoes this workflow happen frequently?
Are the actions repetitive and decomposable?
Is there a clear standard for quality?
Is the cost of error controllable with human takeover?
Can it produce data, rules, SOPs, or exception cases?
Ready for a pilot
Enter formal diagnosis and pick one small loop to establish baseline, AI solution, and acceptance standards.
Clarify the workflow first
The process is not clear enough yet. Start with interviews, issue trees, and a human-AI responsibility sketch.
Do not start yet
If standards are unclear, risk is uncontrolled, or the organization lacks consensus, do not buy tools first.
Delivery is not a demo.It is turning workflows into systems.
PeterAI's B2B delivery follows diagnosis, redesign, deployment, training, operations, and replication. Every step must leave reusable assets, not just another tool account.
Diagnose
InputOwner goals, employee pain points, existing forms and workflows
OutputIssue tree + pilot priority
Redesign
InputInputs, actions, standards, responsibilities, and outputs
OutputHuman-AI division map + acceptance criteria
Deploy
InputKnowledge base, tools, forms, workflows, and permissions
OutputRunnable Agent / Workflow
Train
InputReal scenarios for leaders, managers, and frontline staff
OutputDispatchers, reviewers, and takeover mechanism
Operate
InputErrors, human takeovers, client feedback, and cost
OutputException case library + SOP revision
Replicate
InputValidated small loop and review samples
OutputThe company's own AI operating system
Every delivery should leave behinda reusable PeterAI method base
The long-term moat is not knowing a few AI tools, but turning real problems, evaluation standards, workflow SOPs, exception cases, and review samples into knowledge assets that can be reused in the next delivery.
Learning Sector Architecture
Knowledge Base + Agent Team
Peter's Local Knowledge Base
Chief Learning Professor
Auto-designs lesson plans & content
Learning Assessor
Generates multi-dimensional reports instantly
IP Operations Officer
Takes over content matrix
B2B Consulting Digital Legion
Knowledge Base + Agent Team
Fuses FDE deployment thinking
Chief Management Consultant
Reshapes acquisition & repurchase flows
Tax & Finance Agent
Handles accounts and compliance
Legal Expert Agent
Mitigates commercial risks
Cases are not myths.They show workflows, assets, and boundaries.
High-ticket clients are not afraid of AI jargon; they are afraid of buying wrong. Each case must answer where the workflow was stuck, what PeterAI did, what assets remained, and which decisions still belong to people.
Lighting Hardware Factory
From Excel jail to an owner decision system
Quoting, supply chain alignment, and order follow-up relied heavily on manual spreadsheets, trapping the owner and second-in-command in repeated confirmations.
Decomposed quote inputs, supply chain actions, owners, and review standards; built a CEO strategy avatar and a trading-platform agent department.
Quote SOPs, supply chain coordination rules, and review fields for key account development and competitor analysis.
The first phase does not pursue fully automated pricing; key prices and major-account strategy remain under human review.
Traditional Tax and Finance Firm
Freeing the owner from high-level customer service
The owner repeatedly answered policy, compliance, and credit repair questions, while service experience was not captured.
Built a vertical knowledge base and Q&A assistant to structure common consulting questions, material lists, and risk prompts.
Policy explanation library, client question taxonomy, escalation rules, and service boundary notes.
Tax judgments and high-risk compliance conclusions must be reviewed by professionals; AI does not replace responsibility.
Video IP Media Studio
From labor-heavy team to one-person company system
Shooting and delivery depended on manual experience, margins were thin, and high-quality topic selection and communication judgment were hard to replicate.
Structured shooting SOPs, brand planning logic, and communication judgment into a callable knowledge base.
Shooting templates, brand positioning cards, topic evaluation rules, and review samples.
Creative judgment, client relationships, and final taste remain human responsibilities; AI assists generation and checking.
PeterAI Delivery Principles
Enterprise AI transformation is not inserting tools into an organization. It is redesigning human-AI division of labor, responsibility boundaries, and review mechanisms.
AI replaces actions, not responsibility
Goals, review, correction, accountability, and high-risk judgment must remain owned by people. Responsibility cannot be outsourced to a model.
Define acceptance before configuring tools
Without human baselines, quality standards, error rates, takeover mechanisms, and cost definitions, a smooth demo is not delivery.
Failure must enter knowledge compounding
Every misjudgment, overreach, rework, and client feedback item should become an exception case, rule update, or next-round test sample.
The real moat
Models will change and tools will get cheaper. PeterAI must capture field Know-How as an operating system that can be reused, validated, and continuously revised.
The goal is not to make clients depend on PeterAI's labor, but to help them own their AI way of working; PeterAI turns every delivery into the base for the next one.
Before scanning,think clearly about one workflow
You do not need to understand AI first. Just tell me which workflow consumes the most people, repeats most often, makes the most mistakes, or affects orders most. I will help you decompose it, configure agents, run a pilot, and read the data.
Enterprise AI workflow conversation
This is not a tool recommendation call. It is a workflow diagnosis: whether to start, which small loop to test first, how to accept results, and how to handle failures and capture exception cases.
SCAN TO CONNECT