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AI Discovery Pipeline Overview

Buyer discovery system is implemented as a multi-stage AI pipeline. Instead of performing a simple search query, the system executes a sequence of structured reasoning steps. Pipeline structure: 
    Product Input
         ↓
   Product Analysis
         ↓
   Query Expansion
         ↓
  Company Retrieval
         ↓
  Entity Extraction
         ↓
 Company Enrichment
         ↓
Segment Classification
         ↓
   Deduplication
         ↓
   Scoring Engine
         ↓
      Ranking
         ↓
Decision Maker Discovery
Each stage transforms raw web data into increasingly structured information.

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Product Analysis

The discovery pipeline begins with product understanding. The system must interpret the product description provided by the user. Example input: textile stain remover spray The AI system extracts structured product attributes. Example output:
attributevalue
industrytextile chemicals
categorystain remover
product typeaerosol chemical
applicationtextile manufacturing
synonymstextile spot remover
The goal is to build a product knowledge profile.

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Query Expansion

Once the product context is understood, the system generates multiple search queries. This step is essential because exporters rarely know the exact keywords used by buyers. Example generated queries:
  • textile chemical distributor Germany
  • textile auxiliaries distributor Germany
  • garment factory chemical supplier Germany
  • Textilchemie Händler Deutschland
  • Textilchemikalien Vertrieb Deutschland
The system generates queries in:
  • English
  • local language
  • industry terminology
This dramatically increases discovery coverage.

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Retrieval Layer

The retrieval layer collects candidate companies from multiple sources. Sources include:
  • web search results
  • industry directories
  • company websites
  • trade portals
  • LinkedIn company pages
  • trade association lists
Example discovery result:
companywebsitecountry
TextilChem GmbHtextilchem.deGermany
ChemTex Solutionschemtex.euGermany
GarmentAux Tradinggarmentaux.comGermany
At this stage the data is noisy and unverified.

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Entity Extraction

The system extracts structured company information from raw results. Fields extracted include:
fieldexample
company_nameTextilChem GmbH
websitetextilchem.de
countryGermany
descriptionTextile chemical distributor
Extraction uses:
  • HTML parsing
  • LLM interpretation
  • structured prompt extraction

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Company Enrichment

The next step is to understand what the company actually does. Questions the AI attempts to answer:
  • Is this company a distributor?
  • Do they serve the textile industry?
  • Do they sell chemicals?
  • Are they manufacturers or traders?
Example enrichment result:
companyrolesector
TextilChem GmbHdistributortextile chemicals
ChemTex Solutionssuppliergarment auxiliaries

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Segment Classification

Companies are classified into segments. Segment definitions:
segmentmeaning
S1ideal distributor
S2potential buyer
S3related company
Example:
companysegment
TextilChem GmbHS1
ChemTex SolutionsS2
IndustrialTrade AGS3
Segment classification helps prioritize results.

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Deduplication

Because multiple queries may return the same companies, duplicates must be removed. Deduplication checks include:
  • domain match
  • company name similarity
  • address similarity
Example duplicates:
  • TextilChem GmbH
  • TextilChem GmbH & Co KG
  • textilchem.de
All are merged into a single entity.

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Scoring Engine

After enrichment, companies are ranked using a deterministic scoring model. Example scoring formula: 
FitScore =
  IndustryMatch        × 0.40
  DistributorProbability × 0.30
  CountryMatch          × 0.20
  CompanySize           × 0.10
Where:
factordescription
IndustryMatchdoes company operate in target industry
DistributorProbabilitylikelihood of distribution role
CountryMatchgeographic relevance
CompanySizeoperational capacity

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Example Scoring Table

companyindustry matchdistributor probscore
TextilChem GmbH0.950.850.90
ChemTex Solutions0.800.600.73
IndustrialTrade AG0.550.400.52

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Ranking

After scoring, companies are ranked. Example result set:
rankcompanyscore
1TextilChem GmbH0.90
2ChemTex Solutions0.73
3IndustrialTrade AG0.52
The product UI displays: Top 25 companies But visually highlights: Top 5 likely buyers

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Decision Maker Discovery

For top-ranked companies, the system attempts to identify decision makers. Sources:
  • LinkedIn
  • company websites
  • public directories
Target roles:
  • purchasing manager
  • import manager
  • procurement director
  • owner
Example contact record:
nametitleemail
Anna MüllerPurchasing Manageranna@textilchem.de

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Feedback Loop

Users can rate the relevance of companies. Options:
  • Relevant
  • Maybe
  • Not relevant
This feedback improves future ranking. Example learning signal: 
product: textile chemicals
country: Germany
company: TextilChem GmbH
feedback: relevant
Over time this builds a relevance dataset.

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Accuracy Framework

Discovery quality must be measured. Metrics:
metricmeaning
accuracy scorerelevance ratio
noise ratioirrelevant results
distributor recalltrue distributor coverage
Launch thresholds:
  • Accuracy > 0.70
  • Noise < 15%

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Pipeline Performance Targets

stagetarget latency
product analysis<2 sec
query expansion<2 sec
retrieval<5 sec
enrichment<5 sec
ranking<1 sec
Total discovery time: <15 seconds This ensures a fast user experience.

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Discovery Pipeline Summary

The discovery system converts: 
  Product description
         ↓
Structured buyer candidates
         ↓
   Ranked companies
         ↓
   Decision makers
This capability forms the core competitive advantage of the platform.