In the wave of digital transformation, the approval efficiency of government services has become the key to improving public satisfaction and government efficiency. This article will delve into how the DeepSeek model empowers the government application acceptance platform, solves pain points in the approval process through intelligent means, and improves the speed and quality of approval.
Approval speed up has long been notOptionalbut“Just needed”。
On the one hand, the people are looking forward to running less errands and doing things quickly; On the other hand, government departments are under pressure to promote process reengineering and system integration.
Especially after the construction of a “digital government” has become a national consensus, all localities are accelerating the construction of a unified entrance for government services. In this process, the “unified application acceptance platform” proposed by Beijing has high hopes, with the goal of creating a government service base that “only enters one door and handles all matters”.
However, no matter how unified the platform is, if the process still depends on human judgment, the materials have to be read one by one, and the policy update has to be manually maintained, it is just a façade, and the efficiency improvement is still limited.
So, the big model is coming.
This time, let’s not talk about the concept, just look at the implementation – how does it really play a role in the approval process? Has the mass experience improved? Can departmental collaboration be accelerated? Where have results already been made? What enlightenment does it give us?
In this article, we try to give you the answer.
01 Why is approval always slow? There are public grievances outside and pain points inside
For the masses: “The process is complicated, the information is unclear, and the errands are running back and forth.” ”
The biggest feeling of the masses is one word: toss.
After 10 years of interaction design, why did I transfer to product manager?
After the real job transfer, I found that many jobs were still beyond my imagination. The work of a product manager is indeed more complicated. Theoretically, the work of a product manager includes all aspects of the product, from market research, user research, data analysis…
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It is obviously a simple matter, such as registering a company, applying for decoration approval, and temporary business license, but you have to go to multiple departments and submit five or six materials.
It is difficult to fill out the form, there are many terms, and the conditions are not clear, so doing something is like a game. If you accidentally submit the wrong materials, they will be rejected and you will have to re-make an appointment, re-upload, or even run the site again. At the same time, the rules of different matters are different and updated frequently, and users cannot judge which path they should take. At this time, if there is no manual guidance, it will often “go in circles” in the platform and miss the timeliness.
In the final analysis, the pain points of the masses are:Opaque, uncertain, and unintelligent.
For me personally, although online application is already very convenient, I am still used to going to the scene and being guided by others, which is far away but very down-to-earth.
For the government: “The process is fragmented, manual, and stressful. ”
From a departmental perspective, the problem is more structural:
First, the system is not connected, and the data cannot run.
Many approval matters rely on the verification of materials from other departments, such as taxation, environmental protection, market supervision, etc. in enterprise approval, but the information is in different systems. Auditors can only rely on experience, screenshots, phone calls, and emails, and the labor cost is extremely high.
Second, material review is cumbersome and easy to make mistakes.
For matters such as construction permits and project filings, the materials often cost dozens of pages. Manually verify seals, qualifications, approvals and other contents one by one, and it is not an exaggeration to do anything in 2 hours. The problems of “missed trials” and “wrong judgments” caused by inexperience and fatigue operations have also put great pressure on the front line.
Third, policy updates rely on manual maintenance, which is prone to errors and omissions.
Regulations and policies change every year, and the conditions and material requirements of an item in the application system must be manually updated. If one is wrong, it may affect the judgment of the entire batch, which is extremely risky.
Fourth, cross-departmental coordination is slow, difficult and unpushable.
There is no automated mechanism for the distribution, urging and feedback process of joint review matters, and it completely relies on “people urging people”, not to mention low efficiency, and the boundaries of responsibility are not clear.
02 Analysis of practice paths in various places
In the face of similar approval problems, many cities have already launched their own intelligent reform exploration.
In Shanghai, Zhejiang, and Guangdong, where the degree of digitalization is high, the “government service + large model” is moving from pilot to actual combat, and has made replicable results.
Practical points that can be used as a reference:
First, the government affairs model cannot be just “intelligent customer service”
Many places initially used large models as “question and answer tools”, but what is really valuable is being able to understand policies and logic. For example, Zhejiang Yuhang uses AI for approval material analysis and compliance verification, greatly reducing manual pre-examination time.
Second, the agent should “grow” in the original platform, rather than starting anew
The practice that can really run through is “enhanced access”, not “tearing down and starting over”. For example, Shanghai’s approach is to add an intelligent assistant module to the original unified platform, allowing window personnel to directly adjust AI, check policies, and go through processes, which not only saves time but also does not disrupt the system.
Third, the data is not transferable, but the “standard” is not uniform
This may be a difficulty in implementation everywhere. Shenzhen chose to start with a field (such as enterprise start-up), unify the structure of rules, materials, and forms, and build a knowledge graph, so that the large model “has data to learn and rules to adjust”.
I think data governance is not urgent, and it can be gradually improved from the entrance of high-frequency scenarios, which may be more suitable for promoting implementation.
03 Unified practical path of intelligent application platform
In the face of the problems of poor scalability, cumbersome approval process, and difficult departmental cooperation, the evaluation and analysis of the landing and problem-solving perspective of the evaluation and analysis of the platform’s intelligent capabilities are promoted step by step around several key modules.
First, build a material pre-examination agent
- Multimodal model capability based on DeepSeek + self-built material label library;
- It can identify whether the uploaded materials are missing, expired, or duplicated, and can even proofread the qualification name and corresponding code;
- The matching mechanism of “matter-material-field” is constructed, such as the lack of seals in “fire drawings” will be automatically marked as “risk parts”;
- Auditors only need to confirm once on the system interface, which greatly reduces the burden of manual judgment.
After the launch of this mechanism, the approval personnel changed from “turning pages” to “looking at risk points”, and the pre-examination time for a single piece was compressed from 2 hours to less than 10 minutes.
Second, design a “policy rule trigger engine”
Policies change frequently, and many approval rules are manually maintained by business accounts, which is not only lagging behind, but also prone to errors. We plan to build a “rule triggering engine”:
- Every time a new policy is released, we first use a large model to parse the text into a “clause-element comparison table”;
- Then use the rule template to mark the change points, such as which clauses “affect the submission conditions of materials”;
- Finally, the approval rule center triggers a reminder whether a certain matter needs to be added or deleted.
This module helps us realize the closed loop of “automatic reminders → system rules are updated quickly→ and avoid the low-level problem of “the system does not keep up with the policy change”.
Third, build a “collaborative scheduling agent”
As long as more than two departments are involved in the approval process, there will basically be problems of shirking, waiting, and opaque processes.
We designed a lightweight “co-scheduling agent” to solve this problem:
- Based on the departmental responsibility portrait, the agent can identify which departments need to participate in the approval of the matter;
- The system automatically calculates the average processing time and node waiting time based on historical data, and generates a “joint review path”;
- It is also combined with the work order system, with “intelligent dispatch + supervision mechanism”, 48-hour unprocessed will push reminders, and serious overtime will be automatically CC to the leading department;
All scheduling processes are written to the blockchain to form a multi-party verifiable “process certificate”.
Fourth, build a “conversational intelligent guide assistant – chat and do it”
Last time in this article, we talked about the specifics: the practical sharing of the DeepSeek large model to create an integrated government affairs intelligent service platform of “search, ask, and handle”, the role is that many people do not know “what to do” in the early stage of doing things, so we have developed a conversational guidance assistant at the front end of the unified application, which can be used to consult how to write materials, and can also be used to handle relatively simple matters.
Fifth, a set of “matter knowledge graph + model interface layer” has been built to prepare for future upgrades
When I first wrote the plan, I realized that if I only stacked a few model APIs and did not unify knowledge, then future intelligence would soon fail.
Therefore, the plan establishes a “matter knowledge graph + large model interface scheduling layer”:
- All matters, materials, fields, and rules are structured to form a unified standard;
- The model is no longer “looking at PDF by feeling”, but can directly call the graph data to judge, recommend, and link;
- At the same time, we have encapsulated all large model services into a common call interface, which can be reused in other business systems in the future.
This part is the “smart foundation” left for the future, which can bloom all over the ground.
The overall process is as follows:
Final words
The unified bidding platform is the “hub” and “nerve center” of the government’s digital transformation. It connects the department’s business, carries the implementation of policies, and directly affects the experience of the masses.
And intelligent upgrading, especially combined with the introduction of large model technology,It is not a simple “technology stack”, but a deep change from “data island” to “intelligent collaboration”.
In the future, with the continuous improvement of agent capabilities, the unified application platform will become a real “accelerator” for Beijing’s municipal affairs services and achieve high-quality and all-round upgrades of government services.
I hope it will inspire you, come on!
