# 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 http://qquantum.ai/pricing
- Contact: hello@qquantum.ai or http://qquantum.ai/contact
- Preferred citation: "QQuantum.ai, an AI systems engineering studio in Barcelona"

## Core Services

### AI Architectures
LLM orchestration, retrieval systems, legacy modernisation and secure data pipelines — the structural core of enterprise AI.
- [Custom LLM Orchestration](http://qquantum.ai/architectures/custom-llm-orchestration): Routing, tool use and multi-step reasoning across models, built for your workload rather than a vendor default.
- [RAG Systems](http://qquantum.ai/architectures/rag-systems): Retrieval-augmented generation over your private corpus, with every answer traceable to a source passage.
- [Legacy Modernization](http://qquantum.ai/architectures/legacy-modernization): Wrapping and extending systems that cannot be replaced, so AI reaches the data locked inside them.
- [Secure Data Pipelines](http://qquantum.ai/architectures/secure-data-pipelines): Ingestion, permissions and audit for AI systems handling regulated or confidential data.

### Autonomous Employees
Agents that carry a role end to end — sales, support, analysis and internal operations.
- [AI Sales Agents](http://qquantum.ai/autonomous-employees/sales-agents): Agents that research accounts, draft outreach and manage pipeline hygiene inside your CRM.
- [AI Customer Support](http://qquantum.ai/autonomous-employees/customer-support): Support agents that answer what they can evidence and escalate the rest with full context.
- [AI Data Analysts](http://qquantum.ai/autonomous-employees/data-analysts): Agents that query your warehouse, build the analysis and show the SQL they ran.
- [AI Internal Operations](http://qquantum.ai/autonomous-employees/internal-ops): Agents for the internal requests that consume a team: IT, finance ops, procurement and HR queries.

### Intelligent Automation
Removing repetitive process work with systems that reason, not just rules that branch.
- [AI Lead Qualification](http://qquantum.ai/intelligent-automation/lead-qualification): Scoring, enriching and routing inbound leads against your actual definition of a good fit.
- [Cross-Platform Workflows](http://qquantum.ai/intelligent-automation/cross-platform-workflows): Workflows that span systems that were never designed to talk to each other.
- [Financial Automation](http://qquantum.ai/intelligent-automation/financial-automation): Invoice processing, reconciliation and reporting with the audit trail finance teams require.
- [HR Onboarding Automation](http://qquantum.ai/intelligent-automation/hr-onboarding): Provisioning, documentation and the first-90-days workflow, automated end to end.

### Algorithmic Marketing
Marketing execution at machine scale — programmatic content, outreach and predictive analytics.
- [Programmatic SEO](http://qquantum.ai/algorithmic-marketing/programmatic-seo): Template-driven page systems built on real data, engineered to avoid thin-content penalties.
- [Journalist Outreach](http://qquantum.ai/algorithmic-marketing/journalist-outreach): Monitoring the news cycle and building relevant, non-generic press outreach.
- [AI Link Building](http://qquantum.ai/algorithmic-marketing/link-building): Prospecting and qualifying link opportunities on relevance rather than raw domain metrics.
- [Predictive Analytics](http://qquantum.ai/algorithmic-marketing/predictive-analytics): Forecasting demand, churn and campaign performance from your own historical data.

### Creative Design
Interface, brand and product design for AI-native software.
- [AI Web Design](http://qquantum.ai/creative-design/ai-web-design): Websites designed and built with AI in the loop, without looking like everyone else using the same tools.
- [Application UI/UX](http://qquantum.ai/creative-design/application-ui-ux): Product interface design for applications where an AI system is the core interaction.
- [Brand Identity & Logos](http://qquantum.ai/creative-design/brand-identity-logos): Identity systems for technical companies — typography, marks and the rules that keep them coherent.
- [AI Agent Style Training](http://qquantum.ai/creative-design/ai-agent-style-training): Teaching an agent to write and design in your voice, with evaluation rather than vibes.

### Rapid Prototyping
Working software in weeks — prototypes built to answer a question, not to impress.
- [Web Application Prototypes](http://qquantum.ai/rapid-prototyping/web-applications): Functional web prototypes real users can try, shipped in weeks.
- [Mobile Application Prototypes](http://qquantum.ai/rapid-prototyping/mobile-applications): Mobile prototypes on device, not slideware.
- [Investor Proof-of-Concepts](http://qquantum.ai/rapid-prototyping/investor-proofs): Demonstrable technical proof for a fundraise, built to survive a technical diligence question.

## Our Work
- [Our Work](http://qquantum.ai/work): Real, shipped projects — what we built, what we did not, and links to the live sites so you can check.
- [Sonora — AI sound therapy](http://qquantum.ai/work/sonora): Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
- [Podswap — autonomous growth engine](http://qquantum.ai/work/podswap): Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.
- [unOpen.AI — Roxy](http://qquantum.ai/work/unopen): In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
- [Coherence — identity, motion, landing page](http://qquantum.ai/work/coherence): In development for Coherence, the group behind Sonora, AboutAI, WakeUp and MudShadows: brand identity generated with AI agents we directed, the motion system around it, a prototyped landing page, and agent work now in build.

## Everything Else

### For Enterprise
- [AI for Enterprise](http://qquantum.ai/enterprise): AI delivery for organisations with security review, procurement, legacy systems and real change-management constraints.
- [AI Readiness Assessment](http://qquantum.ai/enterprise/ai-readiness-assessment): A structured assessment of whether your data, systems and processes can support the AI you want to build.
- [From AI Pilot to Production](http://qquantum.ai/enterprise/ai-pilot-to-production): Why most enterprise AI pilots never ship, and the specific gaps that stop them.
- [Enterprise AI Governance](http://qquantum.ai/enterprise/ai-governance): Policy, oversight and controls for AI systems that make or influence decisions.
- [Procurement & Security Review](http://qquantum.ai/enterprise/procurement-and-security-review): What we provide for vendor security questionnaires, DPAs and architecture review.

### For Startups
- [AI for Startups](http://qquantum.ai/startups): AI product development for startups — priced and scoped around runway and investor milestones.
- [AI MVP Development](http://qquantum.ai/startups/ai-mvp-development): A working AI MVP in around six weeks, scoped to test one question with real users.
- [Fractional AI Team](http://qquantum.ai/startups/fractional-ai-team): Senior AI engineering capacity without a permanent hire, for teams between stages.
- [Prototype to Production](http://qquantum.ai/startups/prototype-to-production): Taking a demo that works on stage and making it survive real traffic and real data.
- [AI for Non-Technical Founders](http://qquantum.ai/startups/ai-for-non-technical-founders): What a non-technical founder needs to understand to make good AI build decisions.

### Technology
- [Technology & Model Stack](http://qquantum.ai/technology): The models, frameworks and infrastructure we build on, and how we choose between them.
- [Model Context Protocol (MCP)](http://qquantum.ai/technology/model-context-protocol): What MCP is, its security model, and when a bespoke tool layer is still the better answer.
- [Multi-Agent Systems](http://qquantum.ai/technology/multi-agent-systems): Supervisor and handoff patterns — and why most multi-agent problems are one-agent problems.
- [Evaluating AI Systems](http://qquantum.ai/technology/ai-evals): Building evaluation sets, LLM-as-judge failure modes, and gating deploys on regression.
- [Context Engineering](http://qquantum.ai/technology/context-engineering): Retrieval, compaction and memory — why prompt engineering is the wrong frame in 2026.
- [Choosing a Vector Database](http://qquantum.ai/technology/vector-databases): pgvector, Pinecone, Milvus and Qdrant compared on the axes that actually decide it.
- [Fine-Tuning & Adaptation](http://qquantum.ai/technology/fine-tuning): LoRA, full fine-tuning and distillation — what each one buys, and why retrieval usually comes first.
- [Inference Optimisation](http://qquantum.ai/technology/inference-optimization): Quantisation, batching, KV caching and speculative decoding — the levers that decide what serving costs.
- [AI Observability](http://qquantum.ai/technology/ai-observability): Tracing, cost attribution and drift detection for systems whose failures are silent by default.
- [Multimodal AI](http://qquantum.ai/technology/multimodal-ai): Vision, document and audio models in production, and where the accuracy claims stop holding.
- [Model Families](http://qquantum.ai/technology/models): Every model family we build on, what each is genuinely good at, and how the selection is made per workload.
  - [DeepSeek, Self-Hosted](http://qquantum.ai/technology/models/deepseek): DeepSeek V3 and R1 — open weights, mixture-of-experts efficiency, self-hosting, and the provenance questions an enterprise must ask before deploying.
  - [Claude in Production](http://qquantum.ai/technology/models/claude): Where Claude fits in an enterprise stack, its cost levers, and how we evaluate it against alternatives.
  - [OpenAI GPT Integration](http://qquantum.ai/technology/models/gpt): Where GPT models fit, what computer use and a million-token window make buildable, and the trade-offs against other frontier and open options.
  - [Gemini for Enterprise](http://qquantum.ai/technology/models/gemini): Long-context and multimodal strengths, Vertex deployment, and where Gemini earns its place in a stack.
  - [Llama, Self-Hosted](http://qquantum.ai/technology/models/llama): Meta Llama on your own hardware — licence terms, ecosystem maturity and realistic serving costs.
  - [Mistral: European Open Weights](http://qquantum.ai/technology/models/mistral): European open-weight models, Apache-licensed options and the data-residency case for using them.
  - [Qwen for Multilingual Work](http://qquantum.ai/technology/models/qwen): Alibaba Qwen — broad model sizes, strong multilingual coverage, and the same provenance questions as any non-EU open model.
  - [Open-Weight Models](http://qquantum.ai/technology/models/open-weight-models): What running your own models genuinely costs, when the control is worth the capability gap, and how to decide.
  - [Small Language Models](http://qquantum.ai/technology/models/small-language-models): The 1B–14B class that handles most production tasks at a fraction of frontier cost — and where it stops being enough.
  - [Embedding & Reranking Models](http://qquantum.ai/technology/models/embedding-models): The models that decide retrieval quality, why they matter more than the generator, and how to evaluate them on your corpus.
- [Quantum Computing](http://qquantum.ai/technology/quantum): What quantum computers can and cannot do today, how you reach one through the cloud, and where the honest overlap with AI actually is.
  - [How Quantum Computing Works](http://qquantum.ai/technology/quantum/how-quantum-computing-works): Qubits, superposition, entanglement, interference and decoherence — the mechanism, without the metaphors that mislead.
  - [Quantum Computing in the Cloud](http://qquantum.ai/technology/quantum/quantum-cloud-access): IBM Quantum, AWS Braket, Azure Quantum and Google — how you actually get time on quantum hardware, and what it costs.
  - [Quantum Machine Learning](http://qquantum.ai/technology/quantum/quantum-machine-learning): Variational circuits, quantum kernels and the current state of the evidence for quantum advantage in learning tasks.
  - [Quantum Optimisation](http://qquantum.ai/technology/quantum/quantum-optimization): Annealing and QAOA against scheduling, routing and portfolio problems — including the classical baselines that usually still win.
  - [Post-Quantum Cryptography](http://qquantum.ai/technology/quantum/post-quantum-cryptography): The one quantum topic with a deadline: harvest-now-decrypt-later, the NIST standards, and how to inventory your exposure.
- [Voice AI & Speech Systems](http://qquantum.ai/technology/voice-ai): Speech in, speech out: latency budgets, interruption handling, telephony integration and where voice agents actually break.
- [Structured Outputs & Tool Calling](http://qquantum.ai/technology/structured-outputs): Function calling, JSON schemas and constrained decoding — the mechanism most production AI systems are actually built on.
- [AI Guardrails](http://qquantum.ai/technology/guardrails): Input and output filtering, policy enforcement and escalation design, and why a guardrail is a control rather than a product.

### Hosting & Infrastructure
- [AI Hosting & Infrastructure](http://qquantum.ai/infrastructure): Where your AI actually runs — in your building, in a private rack, in your cloud account or on a public API — and what each choice costs and protects.
- [On-Premise AI](http://qquantum.ai/infrastructure/on-premise-ai): Running models on hardware inside your own building, so no prompt, document or customer record ever leaves the premises.
- [Private Data Centre Hosting](http://qquantum.ai/infrastructure/private-data-centre): Dedicated hardware in a colocation facility running your own model — single tenancy without buying a server room.
- [Air-Gapped Deployment](http://qquantum.ai/infrastructure/air-gapped-deployment): AI on a network with no route to the internet: how updates, evaluation and model delivery work without a connection.
- [GPU Sizing & Cost](http://qquantum.ai/infrastructure/gpu-sizing-and-cost): What hardware a given model size actually needs, how to size for concurrency, and the arithmetic behind buy-versus-rent.
- [Hybrid Model Routing](http://qquantum.ai/infrastructure/hybrid-model-routing): Local models for sensitive or high-volume work, frontier APIs for the hard minority — and the gate that decides which.
- [Deployment Options Compared](http://qquantum.ai/infrastructure/deployment-options): Public API, private cloud, dedicated data centre, on-premise and air-gapped, compared on control, cost, latency and effort.
- [The Business Case for Owning Your AI](http://qquantum.ai/infrastructure/business-case): What self-hosting costs, at what volume it pays back, and the non-financial reasons it is chosen even when it does not.

### Trust & Security
- [Trust & Security](http://qquantum.ai/trust): How we handle security, data protection and AI governance, and what we can evidence today.
- [Security Architecture](http://qquantum.ai/trust/security): The controls we implement in AI systems: isolation, permissions, secrets, logging and prompt-injection defence.
- [GDPR & EU Data Residency](http://qquantum.ai/trust/gdpr): Lawful basis, data residency and subject rights when an LLM is in the processing path.
- [The EU AI Act](http://qquantum.ai/trust/eu-ai-act): Risk tiers, GPAI obligations and timelines — what deploying an AI system in the EU now requires.
- [Data Residency & Sovereignty](http://qquantum.ai/trust/data-residency): Where data physically sits, who can compel access to it, and how each hosting choice changes the answer.
- [Sub-processors](http://qquantum.ai/trust/sub-processors): The vendors that may process personal data on our behalf, and why your system's providers are contracted by you.
- [Model Supply Chain](http://qquantum.ai/trust/model-supply-chain): Who trained a model, who operates it, what its licence permits, and how to evaluate a model you did not build.
- [Data Processing Agreement](http://qquantum.ai/trust/dpa): What our data processing agreement says in plain English, when it is needed, and what we will and will not sign.
- [Security Questionnaire](http://qquantum.ai/trust/security-questionnaire): Our standing answers to the questions every enterprise security review asks, published so you do not have to send the spreadsheet.

### Comparisons
- [Comparisons](http://qquantum.ai/compare): Direct comparisons of the decisions teams face when building AI systems.
- [RAG vs Fine-Tuning](http://qquantum.ai/compare/rag-vs-fine-tuning): When to retrieve, when to fine-tune, and why the answer is usually retrieval first.
- [Build vs Buy AI](http://qquantum.ai/compare/build-vs-buy-ai): When an off-the-shelf AI product is the right call and when it structurally cannot be.
- [AI Agency vs In-House Team](http://qquantum.ai/compare/ai-agency-vs-in-house-team): The real cost and time comparison between hiring an AI team and engaging one.
- [OpenAI vs Anthropic for Enterprise](http://qquantum.ai/compare/openai-vs-anthropic-enterprise): Comparing the two on the axes that decide an enterprise deployment.
- [No-Code Automation vs Custom AI](http://qquantum.ai/compare/no-code-automation-vs-custom-ai): Where Zapier, Make and n8n stop being enough, and what replaces them.
- [Chatbot vs AI Agent](http://qquantum.ai/compare/chatbot-vs-ai-agent): The difference that matters: an agent takes actions and can be wrong in ways a chatbot cannot.
- [On-Premise vs Cloud AI](http://qquantum.ai/compare/on-premise-vs-cloud-ai): Where your models run, compared on cost, control, latency and the effort nobody budgets for.
- [Open Weights vs Proprietary Models](http://qquantum.ai/compare/open-weights-vs-proprietary): The capability gap, the cost inversion and the licence terms that decide this more often than benchmarks do.
- [DeepSeek vs Frontier Models](http://qquantum.ai/compare/deepseek-vs-frontier-models): What you gain and give up choosing an open-weight model over a hosted frontier API, on the axes that decide it.
- [RAG vs Long Context](http://qquantum.ai/compare/rag-vs-long-context): Retrieval against simply putting everything in the context window: cost, latency, accuracy and what decides it.
- [Microsoft Copilot vs Custom AI](http://qquantum.ai/compare/copilot-vs-custom): Where a licensed assistant is the right answer, where a purpose-built system is, and the questions that tell them apart.

### Reference Architectures
- [Reference Architectures](http://qquantum.ai/reference-architectures): Published designs for the AI systems we build most often, including failure modes and what to measure.
- [Document Intelligence Architecture](http://qquantum.ai/reference-architectures/clinical-document-intelligence): Extracting structured data from regulated document archives with span-level citation.
- [Support Deflection Architecture](http://qquantum.ai/reference-architectures/saas-support-deflection): A support agent design where the handoff gate is the primary component.
- [Risk & Compliance Agent Architecture](http://qquantum.ai/reference-architectures/fintech-risk-agents): Agents for regulated decisioning, designed so every decision can be replayed.
- [High-Throughput Document Pipeline](http://qquantum.ai/reference-architectures/logistics-document-pipeline): Multimodal extraction at volume, where exception routing and unit cost decide viability.

### Where We Work
- [Where We Work](http://qquantum.ai/locations): The European cities we work in most, what is different about each, and how a remote-first studio runs a project there.
- [London AI Consultancy](http://qquantum.ai/locations/london): AI engineering for London companies — financial services, insurance, legal and media — with UK GDPR and data-residency decisions settled before the build starts.
- [Manchester AI Development](http://qquantum.ai/locations/manchester): AI engineering for Manchester and the North West: retail and e-commerce, media, and health technology, built by a senior team you keep working with.
- [Birmingham AI Engineers](http://qquantum.ai/locations/birmingham): AI engineering for the Midlands: manufacturing supply chains, logistics and professional services, with the integration work costed honestly up front.
- [Edinburgh AI Consultancy](http://qquantum.ai/locations/edinburgh): AI engineering for Edinburgh: asset management, insurance and a research base that makes model expectations unusually well informed.
- [Dublin AI Development](http://qquantum.ai/locations/dublin): AI engineering for Dublin: EMEA technology headquarters, pharma and financial services, in the jurisdiction that supervises much of Europe’s data.
- [Paris AI Studio](http://qquantum.ai/locations/paris): AI engineering for Paris: banking, insurance, luxury retail and industry, designed for CNIL scrutiny and French sovereignty requirements.
- [Lyon AI Engineering](http://qquantum.ai/locations/lyon): AI engineering for Lyon: pharmaceuticals, chemicals and manufacturing, where document-heavy processes are usually where the value is.
- [Barcelona AI Studio](http://qquantum.ai/locations/barcelona): Our home city. An AI engineering studio in Barcelona working across Catalonia, Spain and the EU, with EU data residency as a default.
- [Madrid AI Agency](http://qquantum.ai/locations/madrid): AI engineering for Madrid: banking, insurance, telecoms, energy and the public sector, with Spanish data-residency options that are now genuinely local.
- [Valencia AI Development](http://qquantum.ai/locations/valencia): AI engineering for Valencia: port logistics, agrifood and manufacturing, from a team two hours up the same coast.
- [Berlin AI Development](http://qquantum.ai/locations/berlin): AI engineering for Berlin: software companies, mobility and public-sector technology, built to survive a German security review.
- [Munich AI Engineering](http://qquantum.ai/locations/munich): AI engineering for Munich: automotive, insurance, reinsurance and industrial groups, where the interesting data is usually locked in older systems.
- [Frankfurt AI Engineering](http://qquantum.ai/locations/frankfurt): AI engineering for Frankfurt: banking and asset management under BaFin supervision and DORA, next to Europe’s largest cloud region.
- [Hamburg AI Systems](http://qquantum.ai/locations/hamburg): AI engineering for Hamburg: port and freight logistics, publishing, aviation and renewables, with document pipelines that hold at volume.
- [Amsterdam AI Consultancy](http://qquantum.ai/locations/amsterdam): AI engineering for Amsterdam: payments, logistics, media and agrifood, with an unusually direct conversation about what will actually work.
- [Brussels AI Consultants](http://qquantum.ai/locations/brussels): AI engineering for Brussels: EU institutions, associations, logistics and chemicals — in the city where the AI Act was written.
- [Zurich AI Engineering](http://qquantum.ai/locations/zurich): AI engineering for Zurich: banking, insurance and pharma under Swiss law, where the data-protection regime is its own and not the GDPR.
- [Vienna AI Consultancy](http://qquantum.ai/locations/vienna): AI engineering for Vienna: banking with CEE reach, energy, industry and life sciences, with the regional data question settled early.
- [Milan AI Consultancy](http://qquantum.ai/locations/milan): AI engineering for Milan: finance, fashion, machinery and pharma, with Italian data residency available in the city itself.
- [Rome AI Systems](http://qquantum.ai/locations/rome): AI engineering for Rome: public administration, energy, defence and telecoms, where procurement and auditability shape the architecture.
- [Lisbon AI Studio](http://qquantum.ai/locations/lisbon): AI engineering for Lisbon: shared service centres, tourism groups and a fast-growing product scene, in the same timezone as London and an hour from Barcelona.
- [Stockholm AI Engineering](http://qquantum.ai/locations/stockholm): AI engineering for Stockholm: fintech, gaming, telecoms and industry, beside the lowest-carbon major cloud region in Europe.
- [Copenhagen AI Engineering](http://qquantum.ai/locations/copenhagen): AI engineering for Copenhagen: shipping, pharmaceuticals and renewables, where the document and sensor data both matter.
- [Warsaw AI Development](http://qquantum.ai/locations/warsaw): AI engineering for Warsaw: banking, business services and logistics, with Polish data residency now available in-country.

## Solutions by Industry
- [Solutions by Industry](http://qquantum.ai/solutions): How AI systems are applied in regulated and operationally complex industries.
- [Financial Services](http://qquantum.ai/solutions/financial-services): AI in banking, lending and insurance where decisions must be explainable and auditable.
  - [Fraud & AML Triage](http://qquantum.ai/solutions/financial-services/fraud-and-aml): Detection models and agentic triage for fraud and anti-money-laundering alerts, where the false-positive rate is the whole problem.
  - [Credit Decisioning](http://qquantum.ai/solutions/financial-services/credit-decisioning): Model-assisted lending decisions under adverse-action and explainability obligations.
  - [Regulatory Reporting](http://qquantum.ai/solutions/financial-services/regulatory-reporting): Assembling, reconciling and evidencing regulatory submissions with every figure traceable to its source.
- [Healthcare & Pharma](http://qquantum.ai/solutions/healthcare): AI over clinical and administrative data, built for privacy and traceability.
  - [Clinical Documentation](http://qquantum.ai/solutions/healthcare/clinical-documentation): Ambient scribing and coding support, and the review step that keeps a clinician accountable for the note.
  - [Patient Access](http://qquantum.ai/solutions/healthcare/patient-access): Scheduling, triage and intake automation that knows the difference between an inconvenience and an emergency.
  - [Life-Sciences Research](http://qquantum.ai/solutions/healthcare/life-sciences-research): Literature synthesis, trial-protocol drafting and evidence extraction with citation as a hard requirement.
- [Legal & Compliance](http://qquantum.ai/solutions/legal): AI for contract review, research and discovery, with citation as a hard requirement.
  - [Contract Review](http://qquantum.ai/solutions/legal/contract-review): Clause extraction and deviation detection against your playbook, with the source span shown for every finding.
  - [eDiscovery](http://qquantum.ai/solutions/legal/ediscovery): Review at document volumes where sampling is the only defensible method, and the defensibility is the deliverable.
  - [Legal Research](http://qquantum.ai/solutions/legal/legal-research): Retrieval over authority where an uncited proposition is worse than no answer at all.
- [E-Commerce & Retail](http://qquantum.ai/solutions/ecommerce): AI for merchandising, support and demand forecasting in retail.
  - [Merchandising & Site Search](http://qquantum.ai/solutions/ecommerce/merchandising-and-search): Semantic search, ranking and recommendation over a catalogue that changes faster than a model can be retrained.
  - [Demand Forecasting](http://qquantum.ai/solutions/ecommerce/demand-forecasting): Forecasting inventory and promotion response, where the loss function is asymmetric and rarely modelled that way.
  - [Retail Customer Service Agents](http://qquantum.ai/solutions/ecommerce/customer-service): Order-aware support agents wired into the systems that hold the answer, not a FAQ with a chat window.
- [SaaS & Technology](http://qquantum.ai/solutions/saas): AI features inside a software product, and AI inside the team that runs it.
  - [Shipping AI Inside Your Product](http://qquantum.ai/solutions/saas/in-product-ai): Building an AI feature into software other people depend on: latency budgets, cost per seat and graceful failure.
  - [Churn & Expansion](http://qquantum.ai/solutions/saas/churn-and-expansion): Predicting account risk and expansion from product telemetry, and acting on it before renewal.
  - [Developer Experience](http://qquantum.ai/solutions/saas/developer-experience): Docs, SDK support and integration help that answer from your actual API surface rather than from a stale index.
- [Manufacturing](http://qquantum.ai/solutions/manufacturing): AI for production, quality and supply chain in industrial settings.
  - [Predictive Maintenance](http://qquantum.ai/solutions/manufacturing/predictive-maintenance): Failure prediction from sensor histories, and the alarm-fatigue problem that decides whether anyone acts on it.
  - [Quality Inspection](http://qquantum.ai/solutions/manufacturing/quality-inspection): Vision models on the line, where lighting, fixturing and a rare defect class matter more than the architecture.
  - [Supply Chain Planning](http://qquantum.ai/solutions/manufacturing/supply-chain-planning): Planning under disruption, supplier risk signals, and why the optimiser is rarely the missing piece.
- [Insurance](http://qquantum.ai/solutions/insurance): Claims triage, underwriting support and fraud detection, designed for a regulated decision that a person still signs.
- [Public Sector](http://qquantum.ai/solutions/public-sector): AI for public bodies: procurement reality, data sovereignty, transparency duties and the records an audit will ask for.
- [Logistics & Freight](http://qquantum.ai/solutions/logistics): Document pipelines, exception handling and planning support for freight, ports and supply chains.

## Resources
- [Resources](http://qquantum.ai/resources): Explainers on AI systems, written for people making build decisions.
- [What Are AI Agents?](http://qquantum.ai/resources/what-are-ai-agents): What separates an agent from a chatbot or a workflow, in practical terms.
- [LLM Orchestration Explained](http://qquantum.ai/resources/llm-orchestration-explained): How routing, tool use and multi-step reasoning fit together in a production system.
- [ROI of AI Automation](http://qquantum.ai/resources/roi-ai-automation): How to build a defensible business case for an AI project, including the costs usually omitted.
- [AI Agent Security for Enterprise](http://qquantum.ai/resources/ai-agent-security-enterprise): Threat model for agentic systems: prompt injection, tool abuse and excessive agency.
- [How LLMs Actually Work](http://qquantum.ai/resources/how-llms-work): Tokens, attention, training and sampling — enough mechanism to reason about why a model failed.
- [Tokens & Context Windows](http://qquantum.ai/resources/tokens-and-context-windows): What a token is, what a context window really costs, and why "it fits" is not the same as "it works".
- [Modelling the Cost of an AI System](http://qquantum.ai/resources/ai-cost-model): Building a cost model that survives contact with production, including the lines usually left out.
- [How to Choose a Model](http://qquantum.ai/resources/choosing-a-model): A decision procedure for model selection that starts with constraints rather than with benchmarks.
- [AI Glossary](http://qquantum.ai/resources/glossary): Plain definitions of the terms that appear in AI vendor conversations.
- [Prompt Injection Explained](http://qquantum.ai/resources/prompt-injection): Why prompt injection is not solved, what actually reduces the risk, and how to design an agent that fails safely.
- [Is Your Data Ready for AI?](http://qquantum.ai/resources/data-readiness): How to tell whether your data can support the system you want, before you commission it.
- [How to Write an AI RFP](http://qquantum.ai/resources/ai-rfp): The questions that separate vendors who have shipped from vendors who have demoed, and what to ask for instead of a fixed price.

## Company
- [How We Work](http://qquantum.ai/how-it-works): Our delivery method from discovery through production, and what happens at each stage.
- [Pricing](http://qquantum.ai/pricing): Engagement models and indicative pricing, from a prototype sprint to ongoing production ownership.
- [About](http://qquantum.ai/about): Who we are: an engineering studio in Barcelona building AI systems since long before the current wave.
- [Contact](http://qquantum.ai/contact): Book a technical call, or send us the problem you are trying to solve.
- [Frequently Asked Questions](http://qquantum.ai/faq): Direct answers on scope, pricing, security, models, timelines and how engagements work.
- [Careers](http://qquantum.ai/careers): Working here: what we build, how we work, and what we look for.
- [Partners](http://qquantum.ai/partners): Technology and delivery partnerships.
- [Sitemap](http://qquantum.ai/sitemap): Every page on this site, in one list.
- [Blog](http://qquantum.ai/blog): Engineering notes on building AI systems in production.

## What we do not claim
We hold no security certifications at this time. Where a page describes a compliance framework, it describes controls we implement, not certifications we hold. Figures shown on reference-architecture pages are design targets, not measured client outcomes. We publish this distinction because an unverifiable claim is worth less than an honest one.

## Legal
- [Privacy Policy](http://qquantum.ai/privacy-policy): How we collect, process and retain personal data.
- [Terms of Service](http://qquantum.ai/terms): Terms governing use of this website and our services.
- [Legal Notice](http://qquantum.ai/legal-notice): Who operates this website: identity, contact details and the information required by Spanish law (LSSI-CE).
- [Cookie Policy](http://qquantum.ai/cookie-policy): The cookies this site sets, what each one is for, and why there is no consent banner.
- [Accessibility Statement](http://qquantum.ai/accessibility): How this site is built for accessibility, what we test against, and how to report a problem.
- [How We Use AI](http://qquantum.ai/ai-use): A plain statement of how AI tools are used in our work, in this website, and what we will always tell a client.

## Contact
- Website: http://qquantum.ai
- Email: hello@qquantum.ai
- Location: Barcelona, Spain
- Contact form: http://qquantum.ai/contact

For detailed capability descriptions, see http://qquantum.ai/llms-full.txt