Case Studies

AI automation practical case sharing

Every case is a timeThe journey from pain point to solution. See how Smato helps individuals throughPersonalized AI micro-toolsAchieve a leap in efficiency.

Data processing AI OCR

AI OCR Ultimate Optimization - Tenfold Accuracy Leap

Through the multi-perspective cross-validation architecture, the accuracy of document recognition is increased from 60-70% to 90-95%, achieving a qualitative change in professional-level data processing.

90-95%

Data can be trusted directly

<5%

Abnormal rate

10x

Improved accuracy

Project Challenge

A large number of receipts, invoices and financial documents are processed every day. Traditional OCR performs well under ideal circumstances, but when faced with complex scenes in the real world - faded thermal paper, handwriting, reflective card sleeves, folded invoices - the accuracy will drop significantly.

The recognition results of the same document may be inconsistent under different lighting, angles, and materials. A deviation in a single number may enter the financial system, causing knock-on effects on subsequent reconciliations. The customer needs aA solution that maintains high accuracyin all environments.

Smato solution

We designed a setMulti-perspective cross-validation architecture, the core idea is:

Multiple independent perspectives

Multiple models can be identified independently without interfering with each other

Cross-validation

Compare the results from each perspective and mark the differences

In-depth analysis

Automatically trigger depth recognition when inconsistent

n8n Workflow · Actual deployment architecture
n8n AI OCR workflow architecture: Manual Trigger → List & Batch → Loop Over Items → Run OCR / Merge Results

Process description: Manual Trigger startup → List & Batch batch grouping → Loop Over Items item-by-item processing → Run OCR execution identification → Merge Results merge results loop to ensure that each data is completely verified.

Effectiveness comparison

Before optimization · Traditional OCR

Can be used directly 60-70%
  • Approximately 3-4 of every 10 photos require manual verification.
  • Single model single identification

After optimization · Smato AI OCR

Can be trusted directly 90-95%
  • Less than 1 in every 10 is worth paying attention to
  • Multi-perspective cross-validation + automatic deep analysis

core value

This is not the result of a "stronger" model, but aArchitecture-level breakthrough. When the anomaly rate drops from 30% to less than 5%, it brings not only an improvement in technical indicators, but alsoQualitative changes in work processes:

Release manpower

No need for a dedicated person to check each card one by one

Data is credible

90-95% directly credited

Exception management

From "full inspection" to "spot inspection"

Business growth AI Agent

AI intelligent potential customer development - a systematic solution to discover hidden business opportunities

Use AI Agent to combine Tavily search and Google Sheets automation to systematically discover high-potential B2B partners that are not covered by SEO optimization and transform the "invisible market" into a list of business opportunities that can be acted upon immediately.

100%

Automated development process

0

Duplicate records

24/7

Continue to dig

Project Challenge

A large number of high-potential B2B partners—retail channels such as supermarkets, stores, pharmacies, and coffee shops—actually exist in the market, but because they have not been optimized for SEO, they are outside the visibility of traditional search engines.

These "invisible business opportunities" operate quietly but are difficult to detect due to insufficient digital visibility. The customer needs a setGo beyond traditional searchmethods to systematically map these untagged market opportunities while avoiding duplication with existing partners.

Smato solution

We constructed a setAI intelligent potential customer development system breaks through the limitations of traditional search in a structured way:

Initial alignment

Synchronize existing databases and establish known boundaries

Wide area sensing

Tavily AI search breaks through the limitations of traditional engines

Precise extraction

Instant extraction and double anti-re-verification

Dynamic expansion

Continue iterating until sufficient scale is accumulated

n8n Workflow · AI Agent Architecture
n8n AI Agent potential customer development workflow: Chat Trigger → AI Agent → Alibaba Cloud Chat Model, Simple Memory, Tavily, Google Sheets, Read Existing Companies

Process description: Chat message trigger → AI Agent calls Alibaba Cloud Chat Model to make intelligent decisions → Discover potential customers through Tavily search → Simple Memory maintains conversation context → Google Sheets writes new records → Read Existing Companies compares and prevents duplication, ensuring that every new piece of information is a new and high-value business opportunity.

core value

This system is like a strategic consultant with a global vision and a meticulous executor——Highly automated, yet retaining the judgmental nature of human intelligence.

Territorial expansion

Extend from known markets to hidden opportunities

Efficiency release

Save a lot of manpower and search time

Immediate combat power list

Clean, structured, and immediately followable

Achievements in practice: It has successfully assisted customers in effectively discovering a large number of high-quality channel partners that are “unsearchable but real” in the tea beverage and FMCG fields.

Data processing Excel

Excel Mapping automation - n8n wisdom of one node

Even if there is only one Code node, it is worth putting in n8n. Centralized management, one-click startup, and unified monitoring allow simple tasks to enjoy the benefits of automated infrastructure.

1

Nodes completed

100%

Centralized management

One click

Start execution

Project Challenge

Customers need to regularly process Excel data mapping - converting and regrouping the fields of the source table and outputting them into the target format. The logic itself is not complicated and can be handled by a JavaScript function.

But the problem is:Where should I put this "simple task"? In local script? Difficulty tracking execution records. Put it in a cloud function? Each execution must be triggered manually. The customer needs aA solution that not only simplifies development but also unifies management.

Smato solution

We chose to build this workflow using n8n - even though it only includesTwo nodes:

Manual Trigger

Start manually at any time without scheduling

Code in JavaScript

Processing Excel mapping logic

n8n Workflow · Minimalist Architecture
n8n Excel Mapping workflow: Manual Trigger → Code in JavaScript

Process description: Manual Trigger Manually start → Code in JavaScript to execute Excel mapping logic → Output the processed data. Minimalist architecture, but enjoying n8n’s complete execution records and management interface.

Why do you need n8n when using only one node?

This is aboutInfrastructure thinkingDecision making. Even if the task itself only requires one Code node, putting it in n8n can still bring long-term value:

Centralized management

Unified entrance for all workflows

One-click start

No need to remember commands or script paths

Execution record

Automatically save logs for each run

Core thinking:The value of automation does not lie in task complexity, but inRepeatable, Trackable, Manageable. n8n allows even simple Excel mapping to enjoy the infrastructure dividends of enterprise-level workflows.

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