HR AI Index
Index Vendors › Talla · September 2026 Edition
2 categories · Named, not ranked

Talla

4Judge labels
0First choices
0Negative labels
3 of 12Models named it
2Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.
Standing
3 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Talla was named 3 times in HR AI assistants and 1 other category, where Workativ Assistant led with 12%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In hr ai assistants · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Talla for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
HR AI assistantsOnboarding and employee experience0%38 of 1190%2under 10 labels · led by Workativ Assistant at 12%
Employee onboardingOnboarding and employee experience0%69 of 1040%1under 10 labels · led by BambooHR at 29%

Movement

This is the first edition on this tier, so no move can be computed for Talla yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Talla across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00101
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash01001
Kimi K200000
GLM 4.7 FlashX01001
MiniMax M2.500000

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct0 labelsNone
Paraphrase2 labelsNone
Comparative0 labelsNone
Budget-constrained0 labelsNone
Scale-constrained2 labelsNone
Negative0 labelsNone
First choiceAlternativeMentionNegative4 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Specializes in turning HR docs into conversational answers; excellent Slack/Teams integration” Qwen 3.7 Flash · HR AI assistants · paraphrase prompt · alternative
“Talla might be better if: Your primary need is knowledge management” GLM 4.7 FlashX · HR AI assistants · paraphrase prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as Talla, over the 4 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Talla was named but was not.
ProductSame answerTook the first choice insteadHead to head
Workativ Assistant2 of 42Not in the top three
BambooHR2 of 40Not in the top three
Culture Amp2 of 40Not in the top three
Caliper1 of 40Not in the top three
Capacity1 of 40Not in the top three
ChartHop1 of 40Not in the top three
Gusto1 of 40Not in the top three
HiOperator1 of 40Not in the top three
Humantelligence1 of 40Not in the top three
Lessonly1 of 40Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Talla. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 2 of the 4 answers that named Talla and are not a share of its labels.

Domains cited

intervue.io2
simular.ai2
workativ.com2
agentiveaiq.com1
aichief.com1
capterra.co.uk1
capterra.in1
cascadeinsights.com1
dmly.io1
easychatdesk.com1

Thirteen of the thirteen domain citations in answers naming Talla came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Talla's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Talla, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at talla.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.