Delivering trusted financial data across 5,300+ global tickers that powers AI agents, LLMs, and leading financial institutions worldwide. Analysts at the top investment firms use our workflow solutions to save valuable time and accelerate their decision making. We also provide the core AI data infrastructure that underpins the best financial agents and are the partner of choice for the leading global AI companies, including OpenAI and Anthropic. Get in touch at…
Profil d’entreprise sauvegardé
Renseignements historiques provenant d’un profil The Jobs API sauvegardé (champs LinkedIn/Crunchbase). Ils ne sont pas vérifiés en temps réel; les chiffres peuvent avoir changé. Les valeurs manquantes restent inconnues.
The financial data layer for the agentic era
Description complète de l’entreprise
Delivering trusted financial data across 5,300+ global tickers that powers AI agents, LLMs, and leading financial institutions worldwide.
Analysts at the top investment firms use our workflow solutions to save valuable time and accelerate their decision making. We also provide the core AI data infrastructure that underpins the best financial agents and are the partner of choice for the leading global AI companies, including OpenAI and Anthropic.
Get in touch at [email protected].
Abonnés LinkedIn
31 109
Personnes associées sur LinkedIn
495
Bureaux déclarés
New York, NY 10004, US
Noida Greater Noida Expressway, Noida, Uttar Pradesh, IN
Catégories d’activité
Software
Analytics
Financial Services
Artificial Intelligence (AI)
Big Data
Autres noms enregistrés
Daloopa
Le profil indique une agence de recrutement
Non
Investissement total déclaré
101 400 000
La devise et la date d’observation ne sont pas fournies; ce n’est ni une valorisation actuelle ni une ronde de financement.
Secteur
Software Development
Taille
51-200 employees
Siège social
New York, NY
Type d’organisation
Privately Held
Daloopa, Inc. a 1 offres ouvertes au Canada en date du 10 novembre 2026. Principaux lieux : Toronto.
Candidates need experience leading engineers and shipping client-facing products built on ML or LLMs, with production practices like evals, observability and cost control. Backend, full-stack or AI product experience is expected, along with measurable results from past work.