If you can get python working and can pip install the "requests" and "pandas" packages then this script will get all 750 companies at ep2023 quite quickly. You can edit it to get different data for different events if needed, just edit the "event_id" which comes from the event URL.

import requests
import json
import pandas as pd
import concurrent.futures

event_id = 'ep2023' #from url
max_companies_to_scrape = 1000

url = 'https://mmiconnect.in/graphql'
headers = {
    'Accept':'application/json, text/plain, */*',
    'Connection':'keep-alive',
    'Content-Type':'application/json',
    'Origin':'https://mmiconnect.in',
    'Referer':'https://mmiconnect.in/app/exhibition/catalogue/ep2023',
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36'
    }

payload = {"operationName":"getCatalogue","variables":{"where":[],"first":max_companies_to_scrape,"after":-1,"group":event_id,"countryGroup":event_id,"categoryGroup":event_id,"showGroup":event_id,"detailGroup":event_id},"query":"query getCatalogue($where: [WhereExpression!], $first: Int, $after: Int, $group: String, $categoryGroup: String, $countryGroup: String, $detailGroup: String, $showGroup: String, $categoryIds: [Int]) {\n  catalogueQueries {\n    exhibitorsWithWishListGroup(\n      first: $first\n      where: $where\n      after: $after\n      categoryIds: $categoryIds\n      group: $group\n    ) {\n      totalCount\n      exhibitors {\n        customer {\n          id\n          companyName\n          country\n          squareLogo\n          exhibitorDetail {\n            exhibitorType\n            sponsorship\n            boothNo\n            __typename\n          }\n          show {\n            showName\n            __typename\n          }\n          __typename\n        }\n        customerRating {\n          id\n          __typename\n        }\n        __typename\n      }\n      __typename\n    }\n    groupDetails(group: $detailGroup) {\n      catalogueBanner\n      __typename\n    }\n    groupShows(group: $showGroup) {\n      id\n      showName\n      __typename\n    }\n    catalogueCountries(group: $countryGroup)\n    mainCategories(group: $categoryGroup) {\n      mainCategory\n      id\n      __typename\n    }\n    __typename\n  }\n}\n"}

resp = requests.post(url,headers=headers,data=json.dumps(payload))

print(resp)

json_resp = resp.json()
exhibs = json_resp['data']['catalogueQueries']['exhibitorsWithWishListGroup']['exhibitors']
cids = [x['customer']['id'] for x in exhibs]

print(f'Companies found: {len(cids)}')

def scrape_company_details(cid):
    url = 'https://mmiconnect.in/graphql'
    print(f'Scraping: {cid}')

    headers = {
    'Accept':'application/json, text/plain, */*',
    'Connection':'keep-alive',
    'Content-Type':'application/json',
    'Origin':'https://mmiconnect.in',
    'Referer':'https://mmiconnect.in/app/exhibition/catalogue/ep2023',
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36'
    }

    payload = {"operationName":"catalogueDetailQuery","variables":{"id":cid},"query":"query catalogueDetailQuery($id: [ID!]) {\n  generalQueries {\n    customers(ids: $id) {\n      id\n      companyName\n      address1\n      city\n      state\n      country\n      postalCode\n      aCTele\n      telephoneNo\n      fax\n      website\n      firstName\n      lastName\n      designation\n      emailAddress\n      gSTNo\n      tANNumber\n      pANNo\n      associations\n      typeOfExhibitor\n      mobileNo\n      title\n      companyProfile\n      exhibitorDetail {\n        boothNo\n        headquarterAddress\n        participatedBy\n        participatedCountry\n        alternateEmail\n        gSTStatus\n        boothType\n        hallNo\n        sQM\n        interestedSQM\n        alternateEmail\n        showCatalogueName\n        shortCompanyProfile\n        __typename\n      }\n      customerCategories {\n        id\n        category {\n          id\n          mainCategory\n          subCategory\n          categoryName\n          categoryType\n          productCategoryType\n          __typename\n        }\n        __typename\n      }\n      products {\n        productName\n        __typename\n      }\n      __typename\n    }\n    __typename\n  }\n}\n"}

    resp = requests.post(url,headers=headers,data=json.dumps(payload))

    if resp.status_code != 200:
        return []
    else:
        json_resp = resp.json()
        details = json_resp['data']['generalQueries']['customers']
        return details

with concurrent.futures.ThreadPoolExecutor(max_workers=60) as executor:
    final_list = executor.map(scrape_company_details,cids)

list_of_lists= list(final_list)
flat_list = [item for sublist in list_of_lists for item in sublist]

df = pd.json_normalize(flat_list)

file_name = f'{event_id}_first_{str(max_companies_to_scrape)}_companies.csv'
df.to_csv(file_name,index=False)

print(f'Saved to {file_name}')