Finance AI Index
Index Equity and corporate Entity management › EntityKeeper vs Diligent
Entity management · September 2026 Edition

EntityKeeper vs Diligent

Zero of twelve models named EntityKeeper first on the direct prompt; zero named Diligent. EntityKeeper was named by eleven of the twelve models and Diligent by eight and EntityKeeper carries 31 labels and Diligent 11, so the shares are not directly comparable.

EntityKeeper

accepted challenger

Named in one category this edition.

Diligent

criticized challenger

Named in two categories this edition.

First-choice share16%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%64%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#8A position in a field of 12; printed, not drawn.
Labels3111A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, EntityKeeper reading right to left. Rank and label count are printed, not drawn.Diligent Entities was named alongside these two in eight of the twelve direct answers. Athennian vs EntityKeeper · Athennian vs Diligent · EntityKeeper vs Commenda

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the entity management page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
EntityKeeperFirst choices, of twelve modelsDiligent
Direct00
Paraphrase214 against Diligent
Comparative00
Budget-constrained401 against Diligent
Scale-constrained001 against Diligent
Negative001 against EntityKeeper · 1 against Diligent
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where EntityKeeper and Diligent stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
EntityKeeper Diligent first choice named as an alternative argued againstblank: not namedEach cell is one answer, EntityKeeper on the left and Diligent on the right.

The direct prompt

The plain question, one answer per model, grouped by where EntityKeeper and Diligent stood in it.

Neither was the first choice, one was named

3 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashAthennian alternatives: EntityKeeper, Filejet, MinuteBox
DeepSeek V4 FlashAthennian alternatives: CSC Entity Management, CT Corporation Entity Management, Commenda, Diligent Entities, EntityKeeper
GLM 4.7 FlashXAthennian alternatives: CSC Entity Management, Commenda, Diligent Entities, EntityKeeper

Neither was named

9 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Commenda alternatives: Athennian, CT Corporation Entity Management, Diligent Entities
GPT-5.4 miniDiligent Entities alternatives: Harbor Compliance
Perplexity SonarCommenda alternatives: Computershare GEMS, Fides
Grok 4.1 FastAthennian alternatives: Carta, Diligent Entities, Pulley
Mistral SmallContractZen alternatives: Commenda, GateWay Entities
Llama 4 MaverickOcorian alternatives: Coverpin, F6S
Qwen 3.7 FlashCommenda alternatives: Diligent Entities, LegalX
Kimi K2Filejet alternatives: Athennian, Commenda, Diligent Entities
MiniMax M2.5Commenda alternatives: Athennian, Diligent Entities, GateWay Entities

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
EntityKeeper leads by twenty-five points.
EntityKeeper25%#1 of 12
Diligent0%#12 of 12
The full small business standing →
Mid-marketThe figures above
EntityKeeper leads by thirteen points.
EntityKeeper16%#2 of 12
Diligent3%#8 of 12
The full mid-market standing →
Enterprise
The order flips: Diligent leads at enterprise.
Diligent9%#– of 8
EntityKeeper2%#6 of 8
The full enterprise standing →

What the models said about EntityKeeper

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.

“user reviews on Reddit specifically call out its "limited integration capabilities."” DeepSeek V4 Flash · negative prompt · soft negative
“For most small companies, EntityKeeper or Filejet are the best starting points due to their low cost” Mistral Small · budget prompt · first choice
“EntityKeeper stands out as the most budget-aligned option because it is described as affordable” Perplexity Sonar · budget prompt · first choice
“For a typical mid-sized B2B company, I'd recommend starting with EntityKeeper” Kimi K2 · paraphrase prompt · first choice

What the models said about Diligent

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“Legacy Systems (e.g., Diligent, CSC Entity Management): Often highly robust ... but can feel clunky, require heavy training, have longer implementation times, and carry higher price tags.” Gemini 3.5 Flash · scale prompt · soft negative
“Many legacy systems (like Diligent, Mitratech, or CSC) are designed for large multinationals, hide their pricing ... often start at tens of thousands of dollars per year.” Gemini 3.5 Flash · budget prompt · soft negative
“## Top Recommendation: Diligent” GLM 4.7 FlashX · paraphrase prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.