Davolta Solutions

📊 Davolta Solutions | Data & AI –👇 🇫🇷.🇺🇸 | Expertise : data analysis, visualisation, predictive modeling. Donner du sens à vos données, simplement.

[Davolta Solutions @DavoltaSol]

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Chez Davolta Solutions, on ne commence jamais par une solution.
On commence toujours par un besoin. 👇

[Davolta Solutions @DavoltaSol]

Demo for #SFx402Hackathon 🚀
Built an Autonomous Energy Capital AI Agent: buys premium forecasts only when EVPI > cost (x402-style pay-per-call), manages NAV/HWM/drawdown, and settles on-chain via SKALE.
Live: https://t.co/7i1MjB0ztt
Video: https://t.co/8ggncLlypc
@SkaleNetwork

[Davolta Solutions @DavoltaSol]

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Envie d’échanger avec nous sur vos besoins ?
Prenez rendez-vous sur X ou via notre site (lien en bio).

[Davolta Solutions @DavoltaSol]

1/
Chez Davolta Solutions, on ne commence jamais par une solution.
On commence toujours par un besoin. 👇

[Davolta Solutions @DavoltaSol]

Le besoin est roi. Le reste s’aligne.

#DavoltaSolutions #ÉcouteClient #InnovationUtile
#BesoinsClients #TransformationDigitale
#Data #DataAnalyse #DataScience #DataEngineer #IA

[Davolta Solutions @DavoltaSol]

@BowTied_Raptor True, subqueries can get wild. But you don’t start there. The first wins in SQL come fast. Simple things like SELECT, WHERE,etc... For someone just entering data work, that kind of quick feedback builds confidence. R and Python have a steeper learning curve right from the start.

[Davolta Solutions @DavoltaSol]

@leadgen_x @matt_gray_ So true. Once your writing’s on point, half the work’s done

[Davolta Solutions @DavoltaSol]

@DataChaz @MongoDB @OpenAI @langchain @llama_index Fire. Thanks for sharing,this is gold for anyone building with RAG.

[Python Programming @PythonPr]

Python Libaries and Frameworks

[Davolta Solutions @DavoltaSol]

@matt_gray_ That last one? It’s what keeps the fire alive when the hustle gets real.
Because if you’re not building your dream, what’s the point, right? Haha.

[Davolta Solutions @DavoltaSol]

@driscollis Any arithmetic operation with nan results in nan !!!!!

[Brady Long @thisguyknowsai]

I replaced my marketing assistant with Grok 3.

• 80% tasks automated
• 10x output
• $0 cost

You can do it too. Here’s how:

[Hamza Tahir @htahir111]

Trying to classify documents using LLMs and running into trouble?

Check out this blog by @mar1zrb where he goes through a deep-dive of how to build a pipeline that classifies whether an article is a good fit for our internal LLMOps database or not.

The idea is super easy:

-> Input document X
-> Answer: Is this worth being in the LLMOps database?
-> If yes, insert it into the database, if no, flag it as so

What I really enjoyed is how went through a process from improving with ReAct-Style Prompting to Fine-Tuning ModernBERT. He ended up showcasing that for this task our fine-tuned ModernBERT model outperformed Claude Haiku by over 30% in accuracy, ~100× faster, and is  ~500× more cost-effective.

So for some tasks, fine-tuning makes all the sense in the world.

Encourage you to read the blog, Marwan is a great writer and goes deep on the research + tech side: https://t.co/Udl9KMDrhC

I'm sure people building similar pipelines will benefit!

[Davolta Solutions @DavoltaSol]

L’IA générative a un vrai potentiel, mais sans base solide, elle ne sert à rien.

Votre entreprise exploite-t-elle déjà correctement ses données avant de vouloir intégrer de l’IA générative ?

[Davolta Solutions @DavoltaSol]

Ne vous précipitez pas sur l’IA générative. Dans la plupart des entreprises, c’est l’IA tabulaire qui est la plus utile et qui apporte le plus de valeur.

Explication sur pourquoi elle est plus fiable, plus rentable et plus simple à intégrer. 🧵👇

[Davolta Solutions @DavoltaSol]

Trop d’entreprises veulent intégrer de l’IA générative simplement parce que la technologie impressionne.

Mais sans une stratégie claire, ce n’est qu’un gadget. L’innovation doit servir un objectif précis.