Six ways to build. Six service lines, one engineering practice underneath them. This page is the index over all of them — what each one is for, what it is not for, and which one to start with if you are not sure.
Introduction transcript. Client: I’ve got all these tools. Can they work together? Qubi: Ooh, a little tangle. I can help with that. AI agents. Automation. Custom software. We connect the pieces around the way you work. Client: So, I get the final say? Qubi: Of course. Your team. Your rules. Client: Now that’s my kind of clever. Qubi: And code you own. Ready when you are.
Start from the bottleneck rather than the technology. Pick the one that sounds like your week: the answer is the service line we would start from, and the pages to read first.
Most engagements end up crossing two of these. The line you start from decides the first pilot, not the shape of everything after it.
Pick the one that sounds like your week, and we will tell you where we would start.
Each answer points at one of the six service lines and the pages to read first. If none of them fits, that is worth a call too.
The knowledge exists but nobody can get an answer out of it — it is spread across a document store, a warehouse and a system nobody wants to touch. Start with architectures: retrieval, orchestration and the pipelines that reach the data where it already lives.
The work is well understood and there is simply more of it than the team can carry — support tickets, account research, recurring analysis. Start with agents that hold the role end to end and escalate what they cannot evidence.
The steps are known but they span systems that were never designed to talk to each other, and the rules have too many exceptions to encode. Start with automation that reasons about the exception rather than branching on it.
The offer works and distribution does not scale with headcount. Start with marketing systems: programmatic page structures built on real data, outreach that reads the news cycle, forecasting from your own history.
The model is good enough and people still will not use it, because nobody can tell what it is doing or when to trust it. Start with design: product interfaces built around an AI system, and the identity around them.
There is a plausible idea and no evidence either way, and the argument has been going on longer than the build would take. Start with a prototype scoped to answer one question, then decide.
Each service line opens onto its own services. Pick one to see what it is for and every page underneath it — 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 →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.
The service lines are six ways into the same practice. Underneath all of them is one job: getting a language model to do useful work on your data, repeatedly, in a way somebody can check. The differences between them are differences of shape — an agent that holds a role, a pipeline that runs unattended, an interface a customer touches — rather than differences of underlying craft.
That is why the split is by what you are buying rather than by technology. A retrieval system, a support agent and a lead-qualification workflow may share most of their code and all of their evaluation harness; they are separate pages because they are separate decisions, with different budgets, different owners and different ways of going wrong.
Four things are constant across every engagement, and they are the parts most likely to be missing when a project arrives here from somewhere else. Retrieval and citation, so an answer can be traced to the passage it came from. Evaluation that keeps running after launch, built from real examples of your task, because quality decays quietly. Observability, so a bad week is visible before a customer reports it. And an exit: the code is yours, the model is a swappable component, and nothing in the design assumes we are still here in a year.
What we do not do is sell a platform. There is no QQuantum runtime you have to keep paying for, and no architecture we prefer because we resell part of it. Model providers deprecate versions and change prices; a system that treats the model as a component survives that, and one built around a single vendor's quirks does not.
Retrieval with citation, so an output can be attributed to a source and a model version. This reduces and grounds model error; it does not eliminate it, and any page claiming otherwise is selling something.
Without it An answer nobody can trace to a source, so nobody can tell a right one from a confident one.
A regression gate built from real examples of your task, running after launch as well as before it. Delivered with the system, in your repository.
Without it Quality decays quietly, and nobody can say whether the last change made things better or worse.
Traces, costs and failure rates visible to your team, so a degradation is something you notice rather than something a customer reports.
Without it A bad week is discovered by a customer rather than by your team.
Source code in your repositories, deployment on infrastructure you can control, and a model layer designed for substitution when a provider deprecates or reprices a version.
Without it The system lasts exactly as long as one vendor relationship does, and a deprecated model version becomes your emergency.
Four stops, each ending in something you can check. Pick one, or let it play through.
A technical call about the workload: where the data is allowed to live, what the task actually demands, what latency the interface can absorb, and what a request may cost at your volume. Capability is rarely the binding constraint, so it is rarely where we start.
A fixed scope for a pilot that answers one question, with the acceptance criteria written down before the work starts. If a cheaper approach would settle the same question, the proposal says so.
A working system against your own data, with an evaluation harness built from real examples of the task. The harness is part of the deliverable, not a phase we skipped: without it, nobody can say whether the next change made things better.
Hardening, observability, access control and handover — or a documented reason not to continue. A pilot that answers no has done its job, and it costs a fraction of finding out later.
Design targets: properties we design and build to, not measured client outcomes.
Six lines. AI architectures (LLM orchestration, retrieval systems, legacy modernisation, secure data pipelines). Autonomous employees (agents for sales, support, analysis and internal operations). Intelligent automation (lead qualification, cross-platform workflows, financial automation, HR onboarding). Algorithmic marketing (programmatic SEO, journalist outreach, link building, predictive analytics). Creative design (web design, application UI/UX, brand identity, agent style training). And rapid prototyping (working software built to answer one question). Every one of them is a custom build, not a licence to a product we own.
Start from the bottleneck rather than the technology. If the knowledge exists but nobody can retrieve an answer, start with architectures. If a role has more work than the people in it, start with autonomous employees. If a process spans systems that do not talk to each other, start with intelligent automation. If distribution is what does not scale, start with algorithmic marketing. If people will not use what already works, start with design. If there is no evidence either way, start with a prototype.
Pilots are fixed-price against written acceptance criteria, because that is the part where a client is buying without evidence and should not also be carrying the estimate risk. Longer production work is usually scoped in phases. Indicative ranges are on the pricing page; a real number needs a call about the workload.
Two to six weeks to a pilot running against your own data, for most scopes. That is a design target rather than a guarantee: an engagement that depends on access to a system nobody has documented is slower, and we will say so during scoping rather than after.
Yes. Source code goes into your repositories from the beginning, and the system is designed to run on infrastructure you control if that is what you want. There is no runtime of ours you have to keep paying for, and no key we can revoke.
Whichever the workload argues for, benchmarked against your task rather than a public leaderboard. We work with frontier APIs (Anthropic, OpenAI, Google) and open-weight models you can self-host (Llama, Mistral, Qwen, DeepSeek). We resell none of them and earn no commission from any of them, which is the only reason a recommendation from us is worth anything.
Yes, including fully on-premise for workloads that cannot leave your building. The hosting and infrastructure section sets out the four options — your building, a private rack, your own cloud account, or a public API — and what each one costs and protects.
No. We hold no certifications or attestations, and we do not describe our work as aligned to one, because that reads as a credential at a glance. What we do instead is build so that your auditors can evidence the controls they test: data residency you choose, access control you own, and logs you keep.
Both, and the shape of the work differs. Enterprise engagements are built around security review, procurement and legacy systems; startup engagements are scoped around runway and the next investor milestone. There is a section for each.
Barcelona, working remotely across Europe. It matters for two reasons: European working hours for a European client, and GDPR as the default assumption rather than an afterthought, including keeping data inside the EU where a workload requires it.
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.
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.
