CUSTOM AI AGENT DELIVERY
MAXLAKER Agent
Custom AI agents for your industry
4
in-house foundation layers
Tailored
Tailored to business workflows
4 Layers
Built on a four-layer stack
User Find this month's abnormal ordersAgent Called ERP → 3 orders over SLA, undeliveredCreated handling tickets, awaiting warehouse confirm→ 2 tool calls · 12s total
Positioning
MAXLAKER Agent is an industry-specific AI agent customization service: built on AIHub model connectivity, Knowledge's trusted enterprise knowledge, and Studio's content capabilities, it delivers dedicated applications that fit your business processes. Within explicit permission and data boundaries, an Agent can understand tasks, retrieve knowledge, call business tools, and move workflows forward — taking AI from a demo effect to an accepted, usable, continuously improvable business capability.
Built for enterprises with clear business goals, complex workflows, or internal-system integration needs, Agent combines AIHub connectivity, Knowledge's trusted knowledge, and Studio's content capabilities into a dedicated application. It goes beyond answering questions: within explicit permission, data, and approval boundaries, it can understand tasks, call tools, and move workflows forward as an AI assistant or digital worker.
Reuse of the four-layer stack
Reuse AIHub connectivity, Knowledge's trusted knowledge, Studio's content capabilities, and GEO's growth reach instead of rebuilding the foundation from scratch.
POC first
Validate accuracy, efficiency, and adoption in one bounded scenario before expanding to more teams and workflows.
Deep-integration delivery
Connect ERP, CRM, OA, knowledge bases, and existing permission systems through APIs or webhooks, then deploy to your required environment.
Core Capabilities
From idea to production: Agent turns requirements discovery, POC validation, agent development, system integration, and ongoing operations into an AI capability embedded in real workflows. Each project defines its users, callable tools, data boundaries, human handoff, and acceptance metrics.
Requirements & solution design
Business mapping
Map users, inputs, decisions, exceptions, and human handoff.
Permission boundaries
Define what the Agent can read or call, and which actions require approval.
Knowledge & tools
Match enterprise knowledge, systems, and callable tools into multi-step flows.
Acceptance metrics
Set targets for accuracy, completion, efficiency, handoff, and business outcomes.
Turn business goals into an executable agent plan, clarifying value, boundaries, and acceptance before development begins.
Industry agent development
Role design
Define users, responsibilities, voice, and decision principles so the Agent acts and communicates appropriately.
Knowledge supply
Connect reviewed enterprise knowledge, rules, and cases while retaining evidence in answers.
Tool calls
Call ERP, CRM, OA, and ticketing tools within permission boundaries to read data or trigger controlled actions.
Workflow orchestration
Chain steps into traceable tasks and hand off exceptions, low-confidence results, or risky actions for approval.
Build dedicated assistants with roles, knowledge, tools, and workflow memory so an Agent moves beyond Q&A into task execution and business coordination.
Business system integration
Connect ERP, CRM, OA, ticketing, data platforms, and knowledge bases so the Agent can read and act within its authorized scope.
Operations & iteration
Track success rate, completion rate, human handoff, and business outcomes; iterate as rules, data, and processes change.
Operating Model & Delivery Path
Step 1
Requirements
Interview owners and frontline users to map processes, pain points, data sources, permissions, and human handoff boundaries.
Step 2
Solution & POC
Design the knowledge, model, tool, and workflow plan on the four-layer stack and validate accuracy, efficiency, and control with real examples.
Step 3
Build & deliver
Implement prompts and orchestration, tool calls, permissions, integrations, acceptance checks, and production deployment.
Step 4
Operate & iterate
Monitor, evaluate, and optimize from runtime data and user feedback as business rules and systems evolve.
Business Scenarios & Applications
Customer-service agents
Answer from enterprise knowledge and service rules, hand off low-confidence or complex cases to humans with context preserved.
Internal digital workers
Help employees query data, summarize information, create tickets, and move approvals forward across systems.
Industry-specific assistants
Combine dedicated knowledge, tools, and decision rules for vertical workflows such as product selection, game NPCs, and short-drama planning.
Deployment, Billing & Delivery
Deployment
Project-based delivery starts with a POC to clarify value and boundaries, then deploys to your designated environment. We can align with your intranet, permissions, and existing systems, with ongoing monitoring, evaluation, and iteration support.
Planning a specific deployment? Talk to our solution team for guidance based on your environment.
Book a Demo →Pricing & Editions
| Project-based pricing | Scoped by scenario complexity — POC first, validate at minimum cost |
| Delivery | Deployed in your designated environment (private supported) |
| Ongoing service | Monitoring, evaluation, and continuous iteration |
Position in the Four-Layer Architecture
L1 · Infrastructure & Connectivity
L2 · Core Intelligence & Assets
L3 · Productivity & Growth
L4 · Infinite Extension
Current position
Enterprise Buying FAQs
What is MAXLAKER Agent?
It is our AI agent customization service built on the in-house four-layer stack: dedicated AI applications tailored to your business processes.
How is it different from buying SaaS?
SaaS solves generic problems; Agent customization deeply fits your business processes, integrating with internal systems and data to deliver an application that truly belongs to your enterprise.
How does a project proceed?
The standard flow: requirements discussion → solution design & POC validation → development & delivery → operations support. POC first — validate value at minimum cost before scaling.
How are timeline and cost estimated?
They depend on scenario complexity, with no one-size-fits-all figure; we keep POC small-step validation to lower your decision risk.
How does it relate to the four-layer products?
Agent is the final delivery form: it reuses AIHub's model connectivity, Knowledge's enterprise knowledge, and Studio's content capabilities, then reaches customers through GEO — the in-house advantage that sets MAXLAKER customization apart from pure outsourcing.
Make AI Your Growth Engine
Start with a demo and see how MAXLAKER's full-stack solutions land in your enterprise.