AI intelligent agent enterprise solution
Encapsulate large models into deliverable, controllable, and measurable enterprise intelligent agents: connect enterprise knowledge bases and business systems, and work for people in real processes, rather than staying in demonstration videos.
- More than just general Q&A: Inject enterprise terminology, process rules, and business data to make the answers fit your industry context and internal standards.
- Documents, tables, drawings, work orders, and historical emails are stored in multiple sources, with integrated management of slicing, vectorization, and permission filtering.
- Decompose complex tasks to multiple role agents and schedule them by the supervising agent to form a reusable and rollable task pipeline.
- Through APIs, plugins, and enterprise WeChat/DingTalk portals, intelligent systems can access data, send processes, create work orders, and write back to business systems.
What exactly does this solution solve for you
Do not stack functions, each ability corresponds to a real business problem.
Intelligent agents that understand business
More than just general Q&A: Inject enterprise terminology, process rules, and business data to make the answers fit your industry context and internal standards.
RAG Enterprise Knowledge Base
Documents, tables, drawings, work orders, and historical emails are stored in multiple sources, with integrated management of slicing, vectorization, and permission filtering.
Multi agent collaborative orchestration
Decompose complex tasks to multiple role agents and schedule them by the supervising agent to form a reusable and rollable task pipeline.
Integrate with existing systems
Through APIs, plugins, and enterprise WeChat/DingTalk portals, intelligent systems can access data, send processes, create work orders, and write back to business systems.
Security and Innovation
Private deployment, data anonymization, unauthorized access interception, and full audit of operations meet the requirements of cybersecurity and industry compliance.
Observability and optimization
Session quality inspection, satisfaction scoring, knowledge hit rate, and gap analysis enable intelligent agents to use more accurately.
From dilemma to solution, explain it all at once
Common difficulties
- I bought a set of conversational robots, but after trying them out for two weeks, no one used them anymore
- Knowledge is scattered in documents, group chats, and the minds of old employees, asking AI questions and not knowing
- The model looks good, but it cannot be integrated into real processes such as approval, work orders, ERP, etc
- Sensitive data, afraid to use public cloud, and heard that the threshold for local deployment is very high
- No one can say clearly how much value AI has created
Abbot's approach
- Intelligent agents embedded in real business entry points, becoming tools that employees must use every day
- Unified storage and continuous updating of enterprise knowledge, answers with source and traceability
- Connect existing systems through APIs/plugins/workflow engines to automatically execute tasks
- Fully privatized deployment, with models and data located within the enterprise intranet, supporting the innovation environment
- The conversation volume, resolution rate, and time saving are all fully embedded, and the effect can be quantified
The Four Chapter Story of a Project
From the initial challenge to being truly utilized by the business. We will explain it to you in its entirety.
The old master is about to retire, how can he keep his skills?
A precision manufacturing enterprise in East China, with a complex range of production line equipment models, heavily relies on several experienced technicians for troubleshooting. New employees not only dare not take action when they encounter a police report, but also repeatedly call and disturb their master; Once there is a problem with the night shift, it is often necessary to shut down and wait until the next day. The head of the enterprise said a sentence that the project team had been memorizing for a long time: "The master is going to retire, how can we keep his skills
We are not lacking a set of software, we are lacking a way to preserve our experience——Vice President of Customer Production
Don't chase big models for now, clarify the knowledge of the enterprise first
The Abit team did not rush to build the model, but spent three weeks conducting a knowledge inventory: organizing 12 years of accumulated maintenance records, equipment manuals, alarm code tables, and team handover records into structured knowledge, and labeling permissions and scope of application. At the same time, clarify the boundaries of intelligent agents - only answer device related questions and answers, and transfer all process parameter adjustments to manual labor to avoid "appearing intelligent but actually dangerous".
Let the intelligent agent live in the entrance that is used every day in the workshop
The system did not create a 'new website', but instead connected to the enterprise WeChat: employees can scan the device QR code to inquire about the alarm meaning, processing steps, and required spare parts, and the answers are accompanied by manual page numbers and similar historical cases. After confirming the solution, a maintenance work order will be automatically generated and written back to the equipment ledger, forming a closed loop of "Q&A - processing - sedimentation". The disposal process of the old master is reversed and deposited back into the knowledge base, becoming thicker as it is used.
The new employee dares to take action, the old master's time is back
After three months of online operation, 70% of common faults were handled by frontline employees according to the recommendations of intelligent agents, and the average downtime decreased by 45%. The master changed from "answering phone calls every day" to "reviewing the knowledge base every week" and focused his energy on process improvement. The second phase of the project has been expanded to include two intelligent agents: procurement inquiry and after-sales Q&A, and a multi-agent collaborative order review process has been planned.
What we are discussing now is no longer whether AI can be used, but which next agent should do it first——Customer Information Center Manager
Different industries, the same sense of certainty
Smart Manufacturing12 years of maintenance experience, installed into an enterprise intelligent agent
Knowledge inventory+privatization deployment+enterprise WeChat entrance, allowing frontline employees to independently troubleshoot.
Professional ServicesContract review agent: from 3 days to 4 hours
The comparison of terms, risk warnings, and version differences are automatically generated, and the legal department only makes final confirmation.
Government institutionsPolicy Q&A Intelligent Agent in the Information and Innovation Environment
Localization of software and hardware full stack adaptation, unified storage of policy documents, and accelerated Q&A for window personnel.
You may still want to know about this plan
Do you need a more specific answer? Contact our solution consultant directly and usually reply within 1 working day.
Other product directions
Want to see what AI intelligent agent enterprise solutions look like in your industry?
Leave your business scenario and goals, and we will provide targeted solution suggestions, similar case references, and implementation path planning.
