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abacus.ai screenshot

Report Summary

  • 31

    Performance

    Renders faster than
    51% of other websites

  • 57

    Accessibility

    Visual factors better than
    that of 24% of websites

  • 83

    Best Practices

    More advanced features
    available than in
    55% of websites

  • 92

    SEO

    Google-friendlier than
    74% of websites

abacus.ai

Abacus.AI - Effortlessly Embed Cutting Edge AI In Your Applications.

Page Load Speed

1.9 sec in total

First Response

18 ms

Resources Loaded

1.2 sec

Page Rendered

710 ms

abacus.ai screenshot

About Website

Visit abacus.ai now to see the best up-to-date Abacus content for United States and also check out these interesting facts you probably never knew about abacus.ai

Abacus.AI is the AI Brain for your organization. Your very own AI Engineer will build applied AI systems across a wide variety of use cases including custom chatbots, ai agents, forecasting, personali...

Visit abacus.ai

Key Findings

We analyzed Abacus.ai page load time and found that the first response time was 18 ms and then it took 1.9 sec to load all DOM resources and completely render a web page. This is quite a good result, as only 40% of websites can load faster.

Performance Metrics

abacus.ai performance score

31

Measured Metrics

name

value

score

weighting

FCP (First Contentful Paint)

Value5.5 s

6/100

10%

LCP (Largest Contentful Paint)

Value6.9 s

6/100

25%

SI (Speed Index)

Value5.5 s

55/100

10%

TBT (Total Blocking Time)

Value790 ms

37/100

30%

CLS (Cumulative Layout Shift)

Value0.206

60/100

15%

TTI (Time to Interactive)

Value9.1 s

33/100

10%

Network Requests Diagram

abacus.ai

18 ms

abacus.ai

52 ms

gtm.js

114 ms

js

300 ms

62ff9bdbe2e537008ecab918

300 ms

Our browser made a total of 153 requests to load all elements on the main page. We found that 82% of them (125 requests) were addressed to the original Abacus.ai, 8% (12 requests) were made to Fonts.gstatic.com and 3% (5 requests) were made to Fonts.googleapis.com. The less responsive or slowest element that took the longest time to load (644 ms) relates to the external source T.co.

Page Optimization Overview & Recommendations

Page size can be reduced by 282.4 kB (27%)

Content Size

1.1 MB

After Optimization

767.8 kB

  • HTML
  • Images
  • JavaScript
  • CSS

In fact, the total size of Abacus.ai main page is 1.1 MB. This result falls beyond the top 1M of websites and identifies a large and not optimized web page that may take ages to load. Only a small number of websites need less resources to load. Javascripts take 487.4 kB which makes up the majority of the site volume.

HTML Optimization

-92%

Potential reduce by 158.0 kB

  • Original 171.4 kB
  • After minification 119.6 kB
  • After compression 13.4 kB

HTML content can be minified and compressed by a website’s server. The most efficient way is to compress content using GZIP which reduces data amount travelling through the network between server and browser. This page needs HTML code to be minified as it can gain 51.9 kB, which is 30% of the original size. It is highly recommended that content of this web page should be compressed using GZIP, as it can save up to 158.0 kB or 92% of the original size.

Image Optimization

-28%

Potential reduce by 39.8 kB

  • Original 144.5 kB
  • After minification 104.6 kB

Image size optimization can help to speed up a website loading time. The chart above shows the difference between the size before and after optimization. Obviously, Abacus needs image optimization as it can save up to 39.8 kB or 28% of the original volume. The most popular and efficient tools for JPEG and PNG image optimization are Jpegoptim and PNG Crush.

JavaScript Optimization

-1%

Potential reduce by 5.7 kB

  • Original 487.4 kB
  • After minification 487.3 kB
  • After compression 481.7 kB

It’s better to minify JavaScript in order to improve website performance. The diagram shows the current total size of all JavaScript files against the prospective JavaScript size after its minification and compression. This website has mostly compressed JavaScripts.

CSS Optimization

-32%

Potential reduce by 78.8 kB

  • Original 246.8 kB
  • After minification 246.8 kB
  • After compression 168.0 kB

CSS files minification is very important to reduce a web page rendering time. The faster CSS files can load, the earlier a page can be rendered. Abacus.ai needs all CSS files to be minified and compressed as it can save up to 78.8 kB or 32% of the original size.

Requests Breakdown

Number of requests can be reduced by 41 (31%)

Requests Now

134

After Optimization

93

  • Images
  • JavaScript
  • CSS

The browser has sent 134 CSS, Javascripts, AJAX and image requests in order to completely render the main page of Abacus. We recommend that multiple CSS and JavaScript files should be merged into one by each type, as it can help reduce assets requests from 25 to 1 for JavaScripts and from 13 to 1 for CSS and as a result speed up the page load time.

Network Requests Diagram

abacus.ai

abacus.ai

18 ms

abacus.ai

abacus.ai

52 ms

gtm.js

gtm.js

114 ms

js

js

300 ms

62ff9bdbe2e537008ecab918

62ff9bdbe2e537008ecab918

300 ms

uwt.js

uwt.js

61 ms

bundle.min.js

bundle.min.js

81 ms

css

css

92 ms

css

css

111 ms

css

css

147 ms

css2

css2

148 ms

jquery-3.6.3.min.js

jquery-3.6.3.min.js

36 ms

anime.3.2.1.min.js

anime.3.2.1.min.js

48 ms

lodash.min.js

lodash.min.js

53 ms

store.everything.min.js

store.everything.min.js

47 ms

js.storage.min.js

js.storage.min.js

50 ms

velocity.min.js

velocity.min.js

52 ms

moment.2.29.4.min.js

moment.2.29.4.min.js

56 ms

tilt.jquery.js

tilt.jquery.js

54 ms

popper.2.11.6.min.js

popper.2.11.6.min.js

55 ms

jquery.appear.js

jquery.appear.js

57 ms

rangeslider.css

rangeslider.css

74 ms

rangeslider.js

rangeslider.js

74 ms

bootstrap.5.2.3.css

bootstrap.5.2.3.css

76 ms

bootstrap.5.2.3.bsmodal.min.js

bootstrap.5.2.3.bsmodal.min.js

84 ms

semantic.2.5.0.css

semantic.2.5.0.css

91 ms

semantic.2.5.0.min.js

semantic.2.5.0.min.js

91 ms

uikit.3.15.22.min.css

uikit.3.15.22.min.css

87 ms

uikit.3.15.22.min.js

uikit.3.15.22.min.js

87 ms

uikit-icons.3.15.22.min.js

uikit-icons.3.15.22.min.js

106 ms

general.css

general.css

94 ms

general2.css

general2.css

95 ms

common.css

common.css

96 ms

newchanges.css

newchanges.css

104 ms

macros.js

macros.js

103 ms

general.js

general.js

102 ms

common.js

common.js

101 ms

api.js

api.js

101 ms

css

css

80 ms

logo_text80.webp

logo_text80.webp

217 ms

hp_chat_llm_s.webp

hp_chat_llm_s.webp

132 ms

hp_ai_agents_s.webp

hp_ai_agents_s.webp

128 ms

hp_forecast_s.webp

hp_forecast_s.webp

131 ms

hp_marketing_s.webp

hp_marketing_s.webp

126 ms

hp_anomalydetection_s.webp

hp_anomalydetection_s.webp

130 ms

hp_foundation_models_s.webp

hp_foundation_models_s.webp

130 ms

hp_nlp_s.webp

hp_nlp_s.webp

130 ms

hp_vision_s.webp

hp_vision_s.webp

132 ms

hp_discrete_optimization_s.webp

hp_discrete_optimization_s.webp

188 ms

hp_predicting_s.webp

hp_predicting_s.webp

186 ms

hp_user_eng_s.webp

hp_user_eng_s.webp

189 ms

hp_fraud_s.webp

hp_fraud_s.webp

188 ms

llm-api.png

llm-api.png

188 ms

rag-api.png

rag-api.png

189 ms

line-chart-small.png

line-chart-small.png

236 ms

data-model-small.png

data-model-small.png

235 ms

cloud-small.png

cloud-small.png

236 ms

shop-small.png

shop-small.png

235 ms

cyborg.png

cyborg.png

237 ms

aiAssisted.webp

aiAssisted.webp

236 ms

step1_1.webp

step1_1.webp

243 ms

step1_2.webp

step1_2.webp

241 ms

step2_1.webp

step2_1.webp

242 ms

step2_2.webp

step2_2.webp

242 ms

step3_1.webp

step3_1.webp

242 ms

hp_chat_llm.webp

hp_chat_llm.webp

243 ms

hp_ai_agents.webp

hp_ai_agents.webp

345 ms

hp_forecast.webp

hp_forecast.webp

343 ms

hp_marketing.webp

hp_marketing.webp

344 ms

hp_anomalydetection.webp

hp_anomalydetection.webp

342 ms

hp_nlp.webp

hp_nlp.webp

343 ms

hp_vision.webp

hp_vision.webp

343 ms

hp_discrete_optimization.webp

hp_discrete_optimization.webp

388 ms

hp_predicting.webp

hp_predicting.webp

389 ms

hp_user_eng.webp

hp_user_eng.webp

391 ms

adsct

adsct

644 ms

adsct

adsct

522 ms

adsct

adsct

536 ms

adsct

adsct

535 ms

KFOlCnqEu92Fr1MmSU5fBBc9.ttf

KFOlCnqEu92Fr1MmSU5fBBc9.ttf

127 ms

KFOkCnqEu92Fr1MmgVxIIzc.ttf

KFOkCnqEu92Fr1MmgVxIIzc.ttf

130 ms

KFOmCnqEu92Fr1Mu4mxP.ttf

KFOmCnqEu92Fr1Mu4mxP.ttf

238 ms

KFOlCnqEu92Fr1MmEU9fBBc9.ttf

KFOlCnqEu92Fr1MmEU9fBBc9.ttf

280 ms

KFOlCnqEu92Fr1MmWUlfBBc9.ttf

KFOlCnqEu92Fr1MmWUlfBBc9.ttf

278 ms

KFOlCnqEu92Fr1MmYUtfBBc9.ttf

KFOlCnqEu92Fr1MmYUtfBBc9.ttf

278 ms

zYX9KVElMYYaJe8bpLHnCwDKjXr8MIY.ttf

zYX9KVElMYYaJe8bpLHnCwDKjXr8MIY.ttf

284 ms

zYX9KVElMYYaJe8bpLHnCwDKjR7_MIY.ttf

zYX9KVElMYYaJe8bpLHnCwDKjR7_MIY.ttf

284 ms

zYXgKVElMYYaJe8bpLHnCwDKtdY.ttf

zYXgKVElMYYaJe8bpLHnCwDKtdY.ttf

285 ms

zYX9KVElMYYaJe8bpLHnCwDKjSL9MIY.ttf

zYX9KVElMYYaJe8bpLHnCwDKjSL9MIY.ttf

355 ms

zYX9KVElMYYaJe8bpLHnCwDKjQ76MIY.ttf

zYX9KVElMYYaJe8bpLHnCwDKjQ76MIY.ttf

286 ms

zYX9KVElMYYaJe8bpLHnCwDKjWr7MIY.ttf

zYX9KVElMYYaJe8bpLHnCwDKjWr7MIY.ttf

356 ms

MatterSQ-Bold.otf

MatterSQ-Bold.otf

268 ms

Matter-SemiBold.otf

Matter-SemiBold.otf

271 ms

Matter-Medium.otf

Matter-Medium.otf

266 ms

Matter-Regular.otf

Matter-Regular.otf

267 ms

Matter-Light.otf

Matter-Light.otf

338 ms

usecase_69d038e82.webp

usecase_69d038e82.webp

269 ms

usecase_9455a9149.webp

usecase_9455a9149.webp

339 ms

useCase_financial_metrics.webp

useCase_financial_metrics.webp

337 ms

useCase_SalesScoring.webp

useCase_SalesScoring.webp

338 ms

useCase_persPromotions.webp

useCase_persPromotions.webp

338 ms

useCase_CustomerChurn.webp

useCase_CustomerChurn.webp

338 ms

useCase_sales_forecasting.webp

useCase_sales_forecasting.webp

438 ms

usecase_anomalyTimeSeries.webp

usecase_anomalyTimeSeries.webp

433 ms

usecase_eventStream.webp

usecase_eventStream.webp

433 ms

recaptcha__en.js

recaptcha__en.js

330 ms

usecase_nlp_entities.webp

usecase_nlp_entities.webp

391 ms

usecase_nlp_search.webp

usecase_nlp_search.webp

387 ms

usecase_nlp_qa.webp

usecase_nlp_qa.webp

387 ms

usecase_nlp_sentiment.webp

usecase_nlp_sentiment.webp

390 ms

useCase_ChatLlmNew.webp

useCase_ChatLlmNew.webp

391 ms

useCase_AiAgents.webp

useCase_AiAgents.webp

392 ms

usecase_vision_clasification.webp

usecase_vision_clasification.webp

389 ms

usecase_vision_hybrid.webp

usecase_vision_hybrid.webp

386 ms

usecase_vision_segmentation.webp

usecase_vision_segmentation.webp

387 ms

useCase_UserRecommendations2.webp

useCase_UserRecommendations2.webp

388 ms

useCase_UserRelated2.webp

useCase_UserRelated2.webp

387 ms

useCase_UserRankings.webp

useCase_UserRankings.webp

388 ms

useCase_feedRecommend.webp

useCase_feedRecommend.webp

338 ms

cap0b.webp

cap0b.webp

289 ms

cap02.webp

cap02.webp

290 ms

shop.webp

shop.webp

291 ms

cap11.webp

cap11.webp

290 ms

cap04.webp

cap04.webp

291 ms

cap12.webp

cap12.webp

292 ms

cap09b.webp

cap09b.webp

249 ms

cap10.webp

cap10.webp

245 ms

cap05.webp

cap05.webp

245 ms

cap06.webp

cap06.webp

246 ms

cap07.webp

cap07.webp

243 ms

cap08.webp

cap08.webp

244 ms

screenAppArrowsLeft3.webp

screenAppArrowsLeft3.webp

243 ms

screenAppArrowsRight3.webp

screenAppArrowsRight3.webp

244 ms

featureNewLarge2.webp

featureNewLarge2.webp

174 ms

eweekbadge.webp

eweekbadge.webp

174 ms

forbesbadge.webp

forbesbadge.webp

123 ms

cbinsightbadge.webp

cbinsightbadge.webp

122 ms

gatbadge.webp

gatbadge.webp

119 ms

Icon_Twitter.png

Icon_Twitter.png

30 ms

Icon_Linkedin.webp

Icon_Linkedin.webp

28 ms

arrowLeftRA.webp

arrowLeftRA.webp

28 ms

hp_chat_llm_s.webp

hp_chat_llm_s.webp

31 ms

hp_forecast_s.webp

hp_forecast_s.webp

28 ms

hp_anomalydetection_s.webp

hp_anomalydetection_s.webp

28 ms

hp_nlp_s.webp

hp_nlp_s.webp

27 ms

hp_discrete_optimization_s.webp

hp_discrete_optimization_s.webp

27 ms

hp_user_eng_s.webp

hp_user_eng_s.webp

26 ms

hp_ai_agents_s.webp

hp_ai_agents_s.webp

27 ms

hp_marketing_s.webp

hp_marketing_s.webp

27 ms

hp_foundation_models_s.webp

hp_foundation_models_s.webp

26 ms

hp_vision_s.webp

hp_vision_s.webp

26 ms

hp_predicting_s.webp

hp_predicting_s.webp

26 ms

hp_fraud_s.webp

hp_fraud_s.webp

26 ms

Accessibility Review

abacus.ai accessibility score

57

Accessibility Issues

Internationalization and localization

These are opportunities to improve the interpretation of your content by users in different locales.

Impact

Issue

High

<html> element does not have a [lang] attribute

Names and labels

These are opportunities to improve the semantics of the controls in your application. This may enhance the experience for users of assistive technology, like a screen reader.

Impact

Issue

High

Image elements do not have [alt] attributes

Best practices

These items highlight common accessibility best practices.

Impact

Issue

High

[user-scalable="no"] is used in the <meta name="viewport"> element or the [maximum-scale] attribute is less than 5.

Best Practices

abacus.ai best practices score

83

Areas of Improvement

Trust and Safety

Impact

Issue

High

Does not use HTTPS

High

Includes front-end JavaScript libraries with known security vulnerabilities

Low

Ensure CSP is effective against XSS attacks

General

Impact

Issue

Low

Detected JavaScript libraries

High

Page has valid source maps

SEO Factors

abacus.ai SEO score

92

Search Engine Optimization Advices

Content Best Practices

Format your HTML in a way that enables crawlers to better understand your app’s content.

Impact

Issue

High

Image elements do not have [alt] attributes

Mobile Friendly

Make sure your pages are mobile friendly so users don’t have to pinch or zoom in order to read the content pages. [Learn more](https://developers.google.com/search/mobile-sites/).

Impact

Issue

High

Document uses legible font sizes

Language and Encoding

  • Language Detected

    EN

  • Language Claimed

    N/A

  • Encoding

    UTF-8

Language claimed in HTML meta tag should match the language actually used on the web page. Otherwise Abacus.ai can be misinterpreted by Google and other search engines. Our service has detected that English is used on the page, and neither this language nor any other was claimed in <html> or <meta> tags. Our system also found out that Abacus.ai main page’s claimed encoding is utf-8. Use of this encoding format is the best practice as the main page visitors from all over the world won’t have any issues with symbol transcription.

Social Sharing Optimization

abacus.ai

Open Graph data is detected on the main page of Abacus. This is the best way to make the web page social media friendly. Here is how it looks like on Facebook: