AI agents. Code you own. We build AI agents that work inside your business. Connected to your systems, accountable to your rules, delivered as code you own.
Introduction transcript. Client: I’ve got ideas. And a lot of busywork. A little help? Qubi: Hi! I’m Qubi, from QQuantum. We build AI agents and automation. Connected to your business. Client: I’m still in charge? Qubi: Always. Your rules. Client: I like that. Qubi: Custom systems. Code you own. Less busywork. More room for ideas.
Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
Most real systems touch two or three of these. Pick one to see what's inside — or let it cycle.
LLM orchestration, retrieval systems, legacy modernisation and secure data pipelines — the structural core of enterprise AI.
Agents that carry a role end to end — sales, support, analysis and internal operations — and hand the hard cases to a person.
Explore Autonomous Employees →Removing repetitive process work with systems that reason, not just rules that branch — across systems that were never designed to talk.
Explore Intelligent Automation →Marketing execution at machine scale — programmatic content built on real data, outreach that is not generic, and forecasting from your own history.
Explore Algorithmic Marketing →Interface, brand and product design for AI-native software — and teaching agents to write and design in your voice.
Working software in weeks — prototypes built to answer a question, not to impress, and proof that survives a technical diligence question.
Explore Rapid Prototyping →Every system we ship has the same bones. Pick a job, then pick any stage to see what it does — and where it's documented.
Everything the system knows comes from your own documents and systems, ingested with their access rules — nothing it can see that the user couldn’t.
Retrieval pulls the exact passages that answer the question, so every claim can point back to where it came from.
A pinned model, read-only tools by default, a step limit, and retrieved content treated as data — never as instructions.
Policy checks and a confidence threshold agreed in discovery. Below it, the system stops and hands over instead of guessing.
Connected through official APIs or MCP, rate-limited, and every action logged and auditable.
The hard cases reach a person with a briefing — and that person can overrule anything the system did.
Cost, latency and quality are traced per step, so drift shows up on a chart before it shows up in a complaint.
We don't have a favourite. Tell us what the system has to do, and see which families we'd reach for first. Drawn from our model pages — a starting point, not a benchmark.
| Family | Long-running agents & code | Very long documents | Images & visual docs | Runs on your hardware | European provenance | Many languages | Cheapest at volume | Fit |
|---|---|---|---|---|---|---|---|---|
|
Claude
Enterprise coding and long-running agents
|
Yes | No | No | No | No | No | No | — |
|
GPT
Computer use and a million-token window
|
Yes | Yes | No | No | No | No | No | — |
|
Gemini
Long-context and multimodal strengths
|
No | Yes | Yes | No | No | No | No | — |
|
Llama
Self-hosted, on your own hardware
|
No | No | No | Yes | No | No | No | — |
|
Mistral
European open weights, Apache options
|
No | No | No | Yes | Yes | No | No | — |
|
Qwen
Broad sizes, strong multilingual coverage
|
No | No | No | Yes | No | Yes | No | — |
|
DeepSeek
Visual documents, coding, open weights
|
No | No | Yes | Yes | No | No | Yes | — |
|
Small language models
1B–14B: most tasks at a fraction of the cost
|
No | No | No | Yes | No | No | Yes | — |
Jev routes each task to the model that fits it, with typed outputs and confidence gates. Published benchmarks included.
Jev model routing →Move the sliders to match a process you run today. The estimate is yours, not a promise — it leaves out build and review time, which the ROI guide walks through.
Arithmetic on your inputs only: tasks × minutes × the share an agent handles × your hourly cost. Not a measured client outcome.
It finds the journalists who would actually write about you — staff and freelance, across national, business, broadcast and trade desks — reads what they've published, and writes each one an individual pitch.
What we built, what stage it is at, and a link to the live thing wherever there is one.
yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.
Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
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.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.
9 industries where a wrong answer has a cost. Pick one to see the problems we're asked to solve there.
AI in banking, lending and insurance where decisions must be explainable and auditable.
Explore Financial Services →AI over clinical and administrative data, built for privacy and traceability.
Explore Healthcare & Pharma →AI for contract review, research and discovery, with citation as a hard requirement.
Explore Legal & Compliance →AI for merchandising, support and demand forecasting in retail.
Explore E-Commerce & Retail →AI features inside a software product, and AI inside the team that runs it.
Explore SaaS & Technology →AI for production, quality and supply chain in industrial settings.
Explore Manufacturing →Claims triage, underwriting support and fraud detection, designed for a regulated decision that a person still signs.
Explore Insurance →AI for public bodies: procurement reality, data sovereignty, transparency duties and the records an audit will ask for.
Explore Public Sector →Document pipelines, exception handling and planning support for freight, ports and supply chains.
Explore Logistics & Freight →No pilot purgatory. Each phase ends with something you can check, and the quality bar is written down before the first line of production code.
agent = Agent(
model=MODEL_PINNED, # upgraded deliberately, behind an eval gate
tools=[
search_docs, # read-only
fetch_account, # read-only, scoped to the caller
create_ticket, # write, rate-limited
],
# Retrieved content is data, never instructions.
untrusted_sources=[DOCS, TICKETS],
max_steps=8, # bounded - no runaway loops
)- name: Evaluation suite
run: |
uv run evals/run.py \
--set golden \
--baseline main \
--fail-under 0.92
# Retrieval is measured separately. A generation score that
# drops because retrieval regressed is a misleading signal.
- name: Retrieval recall
run: uv run evals/run.py --set retrieval --fail-under 0.88Answer three questions and the table picks the deployment we'd start from. Every option is one we build.
| Where the model runs | The provider’s infrastructure | Your cloud account, in a region you choose | Hardware you own, or rent in a data centre | An isolated network |
|---|---|---|---|---|
| Data leaves your network | Yes — to the provider, under a DPA | It stays in your cloud tenancy | No | Never — no outbound connection at all |
| Models available | Frontier proprietary and hosted open models | Open-weight, plus models your cloud provider offers | Open-weight models | Open-weight models, updated deliberately |
| Who operates it | The provider runs the model; we build the system | You, with us | You, with us — or we run it | You, with our runbooks |
| Best when | Speed to value matters and the data allows it | Data must stay in your tenancy and region | Regulation or sovereignty rules out the cloud | Nothing may touch the internet |
| Deployment Options Compared → | Data Residency & Sovereignty → | On-Premise AI → | Air-Gapped Deployment → |
The questions teams argue about before they build. Flip a card for the short answer — the page has the long one.
When to retrieve, when to fine-tune, and why the answer is usually retrieval first.
When an off-the-shelf AI product is the right call and when it structurally cannot be.
The real cost and time comparison between hiring an AI team and engaging one.
Comparing the two on the axes that decide an enterprise deployment.
Where Zapier, Make and n8n stop being enough, and what replaces them.
The difference that matters: an agent takes actions and can be wrong in ways a chatbot cannot.
Where your models run, compared on cost, control, latency and the effort nobody budgets for.
The capability gap, the cost inversion and the licence terms that decide this more often than benchmarks do.
The studio is in Barcelona. The work reaches 24 cities — hover one to see it, click to read how we work there.
Facts about how we work, not measured client outcomes.
Whichever fits the workload. We are model-agnostic and route across Claude, GPT, Gemini, open-weight models and our own Jev — chosen per task against an evaluation set, not by habit.
You do. Everything is built in, and delivered to, your repositories, with runbooks and a documented handover so your team can own it.
Yes — on-premise, in a private data centre, or fully air-gapped. The table above shows where we would start.
No system can promise it never gets something wrong. We ground answers in your sources with citations, measure quality against real data before and after launch, and set guardrails that stop and escalate rather than guess.
Thirty minutes, no deck. You describe the problem; we tell you whether it is one worth engineering — and if it is not, we say so.
Tell us the job you would hand over if you trusted a machine with it. In one 30-minute call we will tell you whether it should, what it would take — and if it should not, we will say so.