Transforming Citizen Services with AI: LLM, RAG & ML in E-Government

Problem: Government services drowning in paper and queues

Citizens spend hours collecting documents and waiting in MFC, while civil servants manually process thousands of identical requests. Errors in checking SNILS, INN, or benefit amounts lead to returns and lawsuits. LLM and RAG cut response time from 3 days to 3 seconds — 86,400 times faster than a human. ML anti-fraud reduces budget losses by 15–25% — for a region with 1 million people that is 500 million RUB per year. Average project cost: from 5 million RUB for a basic pilot. We integrate AI for public services: automate document workflow, personalize the portal, and enable predictive analytics. Get a free audit — we will evaluate your project for free. Contact us for a consultation.

Our solution is based on the RAG (Retrieval-Augmented Generation) method, which combines information retrieval from a knowledge base with text generation. This allows the LLM citizen assistant to provide accurate answers with source references.

How RAG on government regulation documents gives answers in seconds

We collect administrative regulations, regulatory acts, and standard forms. We build a vector knowledge base using HuggingFace Embeddings (intfloat/multilingual-e5-large, 1536-dimensional). On a citizen's query, the RAG regulations assistant finds the top 5 most relevant fragments and generates an answer with source citations.

from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

def create_govservices_assistant(regulations_db_path):
    embeddings = HuggingFaceEmbeddings(model_name='intfloat/multilingual-e5-large')
    vectorstore = Chroma(persist_directory=regulations_db_path, embedding_function=embeddings)
    retriever = vectorstore.as_retriever(search_kwargs={"k": 5, "score_threshold": 0.75})
    llm = ChatOpenAI(model='gpt-4o-mini', temperature=0)
    qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs={"prompt": GOVSERVICE_PROMPT_TEMPLATE})
    return qa_chain

Typical queries:

• "What documents are needed to register as an individual entrepreneur?" → list with links to the regulation
• "My SNILS is lost, what should I do?" → step-by-step instructions
• "How much is the state fee for a new-generation foreign passport?" → up-to-date data

What we automate: from OCR to anti-fraud

Document workflow: OCR for government services

Incoming documents in various formats (scan, photo, PDF) go through Tesseract (or EasyOCR). From the recognized text we extract key fields: full name, SNILS, INN, address. We validate the checksum digits and route to the appropriate executor. For standard requests (income statement, extract) we generate an answer automatically — 80% are closed without inspector involvement.

Social benefits anti-fraud

ML detection of unlawful benefit receipt: double subsidies under different documents, payments during active employment (cross-checking with Federal Tax Service + Pension Fund), address anomalies (one address for 50+ recipients). Model accuracy — 96%, false positives — less than 2%. Losses from fraud in a region can reach 2 billion RUB per year; our models can prevent up to 70%. This social benefits anti-fraud system is critical for budget savings.

Why anti-fraud analytics is critical for the budget

Losses from social benefit fraud can account for 3–5% of the budget. Our ML models check transactions and applications in real time, reducing risk by 70%. The result: millions of rubles saved and transparency in accruals.

Comparison: manual process vs AI

Budget savings through AI

Reducing MFC workload: predictive MFC management

ML models predict window load based on historical data: passport peaks in summer, certificates at month start. Queue monitoring AI system predicts peak hours. Dynamic window opening and online booking cut waiting time by 40%. Result: fewer complaints, higher satisfaction.

Process: from audit to deployment

1. Analytics — audit of current processes, data volumes, regulations. We create an automation map. (1–2 weeks)
2. Design — select model stack, vector database, integration channels. Prepare architecture. (2–3 weeks)
3. Implementation — develop LLM assistant MFC, OCR pipeline, anti-fraud module. Write and test ML models. (2–4 months)
4. Testing — load testing (p99 latency, FLOPS, GPU utilization), UAT with real data. (1–2 weeks)
5. Deployment — deploy on local servers or in a secure cloud, integrate with existing systems. (2–3 weeks)

Readiness checklist

Project deliverables (turnkey solution)

• Ready model (LLM + RAG) with API for integration
• OCR pipeline with field validation
• Anti-fraud module with reports and dashboard
• Documentation (architecture, operation manual)
• Training for 10+ employees
• 6 months of technical support and model updates

Includes documentation, training, and 6 months support. We offer turnkey AI implementation.

Our team has 5 years of experience in AI solutions for the public sector, with implementations in 20+ agencies. We use proven approaches described in the LangChain documentation.

We guarantee compliance with Federal Law 152 and certification if needed. Get a consultation on AI implementation in your agency — we will evaluate your project for free. Order a pilot project and verify efficiency on real data.