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Notes from the Mistral AI Now Summit

koenvangilst.nl

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Re: Notes from the Mistral AI Now Summit

#3
> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.

Mistral leaning into on-prem and European-hosted models is very smart.

Re: Notes from the Mistral AI Now Summit

#4
> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app).

Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?

Re: Notes from the Mistral AI Now Summit

#5
I was at the event, and was impressed by the attendance, all the leaders from the major european listed companies were there.

Also interesting to note the number of partners they invited. Going from Microsoft, Accenture and EY to startups like alpic.ai or lingo.dev . Seems like they are ramping up their M&A game too

Re: Notes from the Mistral AI Now Summit

#6
post #3

> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers. Mistral leaning into on-prem and European-hosted models is very smart.

Lets hope the models can do a better KYC than the humans have been doing..because they are well known.

Or is this a case of the humans, now preparing for the excuse it was the AI failure?

"BNP Paribas Sentenced for Conspiring to Violate the Trading with the Enemy Act" - https://www.justice.gov/archives/opa/pr/bnp-paribas-sentence...

"BNP Paribas caught up in French money laundering investigation" - https://www.reuters.com/business/finance/bnp-paribas-caught-...

"BNP Paribas faces $246m fine in currency scandal" - https://www.bbc.com/news/business-40635070

"BNP Paribas caught in a Cypriot money laundering investigation" - https://www.lemonde.fr/en/les-decodeurs/article/2023/12/26/b...

In Money Laundering their track record is unmatched: https://violationtracker.goodjobsfirst.org/parent/bnp-pariba...

Re: Notes from the Mistral AI Now Summit

#7
post #4

> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?

Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.

Re: Notes from the Mistral AI Now Summit

#8
post #3

> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers. Mistral leaning into on-prem and European-hosted models is very smart.

Also Mistral did just the right thing by acquiring Koyeb, to beef up their deployment at scale expertise.

Re: Notes from the Mistral AI Now Summit

#9
OK, I'm 100% rooting for both Mistral and task focused small models.

But Mistral has fall really far behind since 2025Q3. It seems they can't get good reasoning models working at even medium context sizes, which is necessary to be at the table right now.

Gemma4 and Qwen3.6 are currently best in the small size; Mistral's "small" model has ~4x the parameter count at 120B and isn't even competing with models a quarter its size.

Back one year ago with Mistral Small 3.1 they were keeping up, but they've fallen into irrelevancy right now.

If Mistral seriously wants to play the on-prem and small task-specific model game, a decent proxy would be to build models that get the r/localLlama crowd excited

Re: Notes from the Mistral AI Now Summit

#10
post #4

> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?

European consumer focused businesses do not scale easily the same way US ones do, which is a major contributor to their problems developing tech businesses generally.

OTOH such things can be quite defensible, they just rarely become anything like as profitable.

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