qquantum.ai valid
QQuantum.ai
AI systems engineering studio in Barcelona. We design and build the AI systems businesses actually run on — agents, retrieval architectures, automation and the evaluation that keeps them working.
- Authority
- 45 reach 0 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://qquantum.ai/llms.txt
- llms-full.txt
- https://qquantum.ai/llms-full.txt
- Sections
- 10
- Links
- 202
- Size
- 43349 bytes
- Country
- — Publisher location unknown; CDN address excluded
- Country evidence
- No published address evidence collected yet
- Categories
- AI and machine learning
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-26T01:50:06.854Z
- Last checked
- 2026-10-26T01:50:08.342Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-26T01:50:06.861Z. Cached copy · Source on qquantum.ai
# QQuantum.ai
> AI systems engineering studio in Barcelona. We design and build the AI systems businesses actually run on — agents, retrieval architectures, automation and the evaluation that keeps them working.
We work with enterprises that need AI to survive security review and reach production, and with startups that need working software before the next board meeting. We are model-agnostic and not a reseller for any provider: the model is selected per workload against your constraints, and benchmarked rather than assumed.
Founded 2006. Based in Barcelona, Spain; working across the EU and remotely.
## Quick facts
- Type: independent AI systems engineering studio (consultancy + build), not a SaaS product and not a model reseller
- Founded: 2006, Barcelona, Spain
- Clients: enterprises (security review, on-premise / air-gapped deployment, EU AI Act and GDPR constraints) and startups (MVPs, fractional AI engineering)
- Core services: AI architectures (RAG, LLM orchestration), autonomous agents, intelligent automation, algorithmic marketing, creative design, rapid prototyping
- Model-agnostic: Claude, GPT, Gemini, Llama, Mistral, DeepSeek, Qwen and open-weight models, selected per workload and benchmarked on client data
- Deployment: cloud, hybrid, on-premise, private data centre and air-gapped
- Deliverable: code the client owns, with an evaluation set and observability, not a dashboard subscription
- Pricing: quoted per project; indicative ranges at https://qquantum.ai/pricing
- Contact: hello@qquantum.ai or https://qquantum.ai/contact
- Preferred citation: "QQuantum.ai, an AI systems engineering studio in Barcelona"
## Core Services
- [Home](https://qquantum.ai/): Custom AI development studio in Barcelona: AI agents, retrieval-augmented generation (RAG) and workflow automation, evaluated and delivered as code you own.
- [All Services — What We Build](https://qquantum.ai/services): Custom AI development services: architecture and RAG, AI agents, workflow automation, marketing systems, product design and software prototypes, with guidance on where to start.
### AI Architectures
LLM orchestration, retrieval systems, legacy modernisation and secure data pipelines — the structural core of enterprise AI.
- [Software Reverse Engineering & Application Rebuilding](https://qquantum.ai/architectures/software-reverse-engineering): QQuantum.ai provides software reverse engineering and application rebuilding for businesses: legacy software replacement, web and desktop app analysis, mobile workflow reconstruction and API interoperability. Start with a reference URL, authorised application package or source access; receive a feasibility assessment, documented findings and an independently implemented system with agreed acceptance tests. A link does not expose private backend code or data; platform support and build scope are assessed first.
- [Custom LLM Orchestration](https://qquantum.ai/architectures/custom-llm-orchestration): Routing, tool use and mul
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| opera.com | The Opera browser overview | documentation Infrastructure AI and machine learning +2 | NL | 74 | ✓ | ||
| taboola.com | Taboola.com | documentation AI and machine learning Marketing | — | 74 | ✓ | ✓ |
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