DeepSeek large model creates a “search, ask, and handle” integrated government affairs intelligent service platform practice sharing

This article will delve into how to use the powerful capabilities of the DeepSeek large model to break down system barriers, build an integrated government affairs intelligent service platform of “search, ask and handle”, realize the qualitative change of government services from “can be done” to “easy to do”, and provide useful practical experience and profound enlightenment for the digital transformation of government services.

In the field of government services, the masses and enterprises often face two core pain points: “not being able to find it” (scattered information, inaccurate search) and “not understanding” (complex terminology and difficult to understand the process of the service guide).

At the same time, under the traditional model, government search, intelligent customer service and service services are separated from each other, which undoubtedly increases the difficulty of user use.

After discovering these pain points, we connect users’ searches, questions, and offices on the government service platform to achieve a seamless connection from “information retrieval” to “problem solving” in the handling process through the capabilities of DeepSeek’s large model.

In order to break down system barriers, when the user enters “how to reissue the business license” in the search box, or selects “Electronic signature materials required” on the service guide page, the system can automatically trigger the accurate response or manual assistance of intelligent customer service.

Let me talk about how to do it.

01 Pain point: barriers in the traditional service model

Let’s talk about a core issue that has been neglected for a long time in the process of digital transformation of government services:The three key links of search, Q&A, and handling are actually “information islands” that are separated from each other in the traditional model.

This fragmentation not only exists at the level of user perception, but is also deeply rooted in the underlying architecture of the system.

The “triple isolation” dilemma of the data layer

Let’s first look at the underlying problems of the data. When digging deep into the data layer, it was found that the underlying data of the search, the government search database, the knowledge base of intelligent customer service, and the matter database of the online service system, these three databases that should be closely related were actually maintained by three different teams and used three different sets of data standards.

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A typical case is the “issuance of enterprise social security payment certificate” service:

• The policy library contains the latest version of the implementation rules of the Social Insurance Law
• The intelligent customer service knowledge base is stuck in the operation guidance of two years ago
• The list of materials required by the online declaration system has added an additional “power of attorney for legal persons”

The consequences of this data desynchronization are catastrophic. Because many of the answers to their questions are actually hidden in a database on the platform, but users can’t find them, and the system won’t actively associate them.

The phenomenon of “cliff-like service” at the experience level

From the user’s perspective, the experience fault caused by this fragmentation is more obvious. I have experienced a number of provincial government service networks and found that the whole process behavior of users handling “individual industrial and commercial household registration” is found that each user has to go through 7 collapsing steps on average:

1. Enter “Self-employed registration conditions” in the search box
2. Identify valid information among the ten results
3. Click on the intelligent customer service consultation “Business premises certification requirements”
4. Redescribe the issue (because the search history is not synced to the agent)
5. I was informed that I needed to prepare a copy of the lease contract
6. When jumping to the declaration system, it is found that the operator’s health certificate must also be submitted
7. Return to customer service to confirm the health certificate processing process

The most absurd thing in this process is that when the user consults for the third time, most intelligent customer service will still mechanically repeat: “What business do you want to handle?” ”

As if the previous conversation never happened. This “memory loss” service experience makes many users have no choice but to call the 12345 hotline directly or go offline to handle it.

02 Build an integrated intelligent center of “search and service office”

As product managers, we all know that the most difficult thing to open up “search, ask, and do” is not technical implementation, but how to build a bridge between the “chimneys” of the existing government system.

Step 1: “Bridge” the data first, not “demolish” the building

In the face of the situation that departments are unwilling to open their own databases, they can try the roundabout strategy of “data bridging”. Instead of requiring them to hand over data directly, they are connected through three key actions without changing the original system:

1. Buried point collection: Deploy a unified event collection SDK in the search box, customer service conversation, and service page to record the user’s complete operation trajectory. For example, when a user searches for “social security transfer” and then consults customer service, we can know that these two actions are operated by the same person.
2. Dynamic Mapping: An intelligent field matching engine has been developed. The most practical function of this engine is that when it detects that the user fills in the “ID number” in the social security system and the “social security number” in the provident fund system, it can automatically identify that this is the same field.
3. Incremental synchronization: Adopt the synchronization strategy of “boiling frogs in warm water”. In the early stage, only the most critical 10 fields were synchronized, and then the scope of synchronization was gradually expanded after each department saw the actual results.

Step 2: “Pickle” AI capabilities into each operation link

The large model is not used to show off skills, but to penetrate AI capabilities into every subtle operation link like pickles. I figured out a few immediate “killer” application scenarios:

Scenario 1: Search and process

When a user searches for “business license reissue”, the result page is directly embedded in the processing entrance with a progress bar. Here’s a product detail – instead of a simple jump link, the application button preloads the user’s recent questions and automatically populates the application form. This design directly increased the conversion rate by 40%.

Scenario 2: Consultation on wording

On the to-do guide page, users can evoke the intelligent assistant by selecting any paragraph. For example, if you select “Shareholders’ meeting resolution needs to be submitted”, the resolution template download and common error prompts will pop up.

Scenario 3: Break point continuation

The system remembers where the user last interrupted. When the user logs in again, the homepage directly displays “You were handling a change of enterprise last time, and you still have to upload the amendment to the articles of association”. This seemingly simple function has greatly improved the business completion rate.

Step 3: Establish a “data flywheel” closed loop

The essence of this closed-loop is that we make every click, every consultation, and every handling of users become nourishment for optimizing the system.

Specifically, this closed loop turns like this:

Users enter “high-tech enterprise certification” in the search box → the system finds that the search volume has surged recently, but the processing rate is low→ Automatically push a prompt to government officials about “whether to optimize the guide” → After the staff updates the list of materials, → directly display the latest handling channel the next time the user searches.

After this closed-loop run, the business processing time has been reduced from an average of 5 working days to 1.8 days, which can really reduce the pressure on the staff.

03 Landing value: three layers of qualitative change empowered by technology

Traditional government services are like “old-fashioned radios”, and users have to tune their own frequency to find the programs they want. The current model is more like an “intelligent consulting system”, which has undergone qualitative changes at three levels:

The first level: the reconstruction of service supply methods

In the past, it was “people looking for services”, and the masses needed to know exactly what business to do and how to call it.

Now it has become “service to find people” – when users vaguely enter “how to deal with employees when the factory is closed” in the search box, the system can automatically associate it with the two service packages of “enterprise cancellation processing” and “employee social security suspension”, and generate a personalized handling list.

The second level: the redefinition of government efficiencyUnder the traditional model, it takes an average of 22 days for a policy to be released to implemented. Now through the closed-loop system, this cycle is compressed to 72 hours.

The third level: the paradigm upgrade of government-people relationsInteresting data changes were observed: after accessing the integrated platform, complaints such as “not finding the service entrance” in the 12345 hotline decreased by 68%, but the number of inquiries about “policy implementation details” increased by 42%. This shows that the masses are beginning to spend their energy on more valuable policy interactions rather than wasting them on basic information search.

Final words

The practice of the integration of “search and inquiry office” is the qualitative change of government services from “can be done” to “easy to do”. The deeper significance is that it is quietly changing the production logic of government services – from the government “serving dishes” to the masses “ordering dishes”, from “one-size-fits-all” to “tailor-made”.

This change also gives us a lesson:The real digital transformation is not to replace people with machines, but to let machines learn to do what humans do first. When AI begins to understand the unclear demands of the masses, and when the system can predict the stuck points in the work process, technology truly becomes a bridge that warms people’s hearts.

I hope it will inspire you, come on!