Software development, AI and automation, business development and marketing. Engage one when that is what the problem needs; connect them when the answer spans more than one.
01 / Technology Axis — Software Development
The Technology Axis turns a business goal into a working system: scoped honestly, built in visible increments, and handed over with the documentation and access that make it yours.
Most software does not fail in the code. It fails in the gap between what a business needed and what a brief described — a feature list agreed before anyone tested the assumption underneath it, a launch date set before the integration was understood, an admin screen nobody asked for and everybody paid for. We close that gap by treating discovery as delivery work rather than a formality before it.
What this includes
Requirements, user journeys, prioritisation and technical feasibility.
Customer-facing products, portals and responsive experiences.
APIs, payment, CRM, ERP and third-party service connections.
Operational visibility, reporting and decision-ready metrics.
Uptime tracking, error alerting and incident response built into the release.
Testing, documentation, launch readiness and production handover.
How we approach it
Work moves in short, demonstrable cycles. Every milestone ends in something you can open, click and argue with — not a status percentage. That rhythm catches the expensive misunderstandings while they are still cheap, and it means the roadmap reflects what has been learned rather than what was guessed in month one. Priorities, owners and dependencies stay visible in one place for the whole engagement.
We choose boring, well-supported technology unless there is a specific reason not to, and we write down the reason when there is. Your repository, your cloud account, your domain, your data. Environments, deployment path and secrets management are set up in the first weeks, not improvised at launch. When the engagement ends, nothing about running the system depends on us still being in the room.
Testing, accessibility and performance targets belong in the acceptance criteria of each milestone, which is the only place they reliably survive a deadline. We plan the test approach with the scope, keep a written record of risks with a named owner and a response for each, and treat launch readiness — monitoring, backups, rollback, support handover — as a milestone of its own rather than the week after the last one.
We build AI-enabled features the same way we build anything else: against a measurable job, with a human in the loop where the cost of being wrong is real, and with the evaluation method agreed before the feature is built. Retrieval over your own content, drafting and summarisation inside an existing workflow, classification and routing that saves an operations team hours a week — the useful kind. If a rules engine would do the job more cheaply and predictably, we will say so.
Questions we get asked
Yes. We start with a short technical review — architecture, dependencies, test coverage, deployment path and the real risks in it — and give you a written read on what it would take to extend it versus replace it, before either of us commits to a direction.
Yes. The embedded model exists for exactly that: we extend an existing team across an agreed scope, using your tooling and your conventions, with a named lead on our side accountable for the part we own.
Support and optimisation is a defined engagement rather than an afterthought — maintenance, monitoring, security updates and planned enhancement against an agreed response commitment. You can also take the system entirely in-house; the handover is built to make that a real option.
02 / Business Development Axis — Business Development
The Business Development Axis connects market evidence to the decisions that actually shape revenue: who to serve, what to offer, how to price it, and which channels can carry it at scale.
Commercial work goes wrong in a predictable way. A product is built, a launch happens, and only then does anyone ask who was supposed to buy it, at what price, through which route, and against which alternative. By that point the expensive decisions have already been made implicitly — in the feature set, in the positioning, in the cost base — and the commercial team inherits constraints nobody chose deliberately.
What this includes
Market sizing, competitor mapping, customer segments and opportunity prioritisation.
Business models, offer architecture, pricing logic and revenue assumptions.
Channel strategy, launch sequencing, target accounts and market-entry plans.
Partner propositions, pipeline development and channel enablement.
Pitch narratives, proposals, qualification tools and conversion journeys.
KPIs, pipeline rhythm, review cadence and actions tied to owners.
How we approach it
Sizing that distinguishes the market from the part of it you can realistically reach. Competitor mapping that captures how buyers actually substitute, including the spreadsheet and the status quo. Segment definitions specific enough to build a message around, and a prioritisation of which to pursue first based on reachability and willingness to pay — not on which is largest on a slide.
Business model, offer architecture and pricing logic, worked through together because they constrain each other. We put the revenue assumptions on the page — conversion, cycle length, retention, cost to serve — and stress the ones the whole model rests on. The output is a commercial structure you can explain in a sentence, defend in a negotiation, and change deliberately when the evidence moves.
Channel strategy grounded in where your buyer already looks, a launch sequence that does not depend on every channel working at once, target account definitions, and partner propositions built around what the partner needs rather than what you want from them. Where a channel is unproven, we design the smallest credible test of it before it becomes the plan.
The pitch narrative, proposal structure, qualification criteria and objection handling that let a small team sell consistently without the founder in every call. Then the operating rhythm around it: a short set of KPIs tied to the model, a pipeline review cadence, and actions with owners and dates. The point is a system that surfaces a problem in weeks rather than at the end of the quarter.
Questions we get asked
Both, and we prefer the engagements where it is both. Strategy that nobody has to implement tends to be untestable. Where you want execution, we can run the outbound, the partner conversations or the launch sequence with your team rather than handing over a document.
No — it is the cheapest point at which to be wrong. A short, focused first engagement can clarify the segment, the offer and the price assumption before a full build locks them in.
Directly. The commercial model tells the Technology Axis what has to exist and in what order, and it gives the Marketing Axis the positioning and proof points to build the message on. Run alone, the Growth Axis still delivers — it simply hands its conclusions to your own teams instead of ours.
03 / Marketing Axis — Digital Marketing
The Marketing Axis builds the story, the channels and the measurement system together, so attention turns into demand and demand turns into something you can read in a dashboard.
A brand that looks right and says nothing specific is a common and expensive outcome. So is a campaign that spends confidently into a channel nobody checked, measured against a metric that cannot move a decision. Both come from the same root: creative, channel and measurement treated as three separate briefs handed to three separate people.
What this includes
Value proposition, identity direction, messaging and market differentiation.
Campaign concepts, editorial plans, social content and sales-support assets.
Channel planning, creative coordination, landing journeys and activation.
Paid acquisition, testing, optimisation and disciplined budget allocation.
SEO foundations, content opportunities and conversion-focused pages.
Tracking plans, dashboards, insights and clear next actions.
How we approach it
A value proposition that names the buyer, the alternative and the reason to switch — in language your customers already use. From there, the identity direction, message hierarchy and tone rules that keep a small team consistent across a landing page, a deck and a founder post. Deliverables come as a system you can apply, not a one-off set of files that goes stale the first time someone needs a new asset.
Editorial plans built around the questions buyers ask on the way to a decision, campaign concepts that survive contact with a real channel, social and sales-support assets, and the landing journeys that receive the traffic. We plan production capacity honestly: a cadence you can actually sustain beats a launch burst followed by six quiet months, and it compounds instead.
Paid acquisition run as a series of tests with a stated hypothesis and a stopping rule, rather than a budget on autopilot. Audience and creative iterations, disciplined allocation across channels as evidence accumulates, and a clear read on cost per qualified outcome — not per click. Where the economics do not support paid acquisition yet, we will tell you that instead of spending the budget to find out.
Technical SEO foundations, content built against real search demand, and conversion-focused pages that are fast on a mid-range phone. Behind them, a tracking plan defined before launch, dashboards that report the actions that matter, and a regular review that ends in a decision — what to stop, what to scale, and what to test next.
Questions we get asked
Yes. Plenty of engagements start with an identity that is fine and a message that is not. We will say clearly if we think the identity is the constraint, but we do not rebrand as a reflex.
We can run the accounts, or set the strategy and testing framework for your team to run. Either way the accounts, pixels and data stay in your ownership.
Tracking and the first dashboard are set up before launch, so the early read is available immediately. How quickly it becomes conclusive depends on your traffic volumes and sales cycle — we will set that expectation with you from the specific numbers, not a generic timeline.
04 / AI Axis — AI & Automation
The AI Axis builds agents, automations and integrations against a specific job and a specific KPI — not a demo that never reaches production.
Most "AI strategy" stops at a slide. The gap is not ambition, it is the unglamorous middle: whose process actually changes, what data the model needs that you do not have yet, who checks its work, and what happens the day it is wrong. We start there, not with the model.
What this includes
Where AI actually pays off in your operation, ranked by effort and impact.
Customer-facing and internal agents scoped to a specific job, with a human escalation path.
Workflow automation across your tools with n8n, Make or custom pipelines.
Retrieval, drafting and classification wired into your existing systems and data.
The unglamorous prerequisite: clean, connected data an agent can actually act on.
Usage tracking, cost limits and human-in-the-loop checks tied to real KPIs, not vibes.
How we approach it
A short, structured look at where AI has a realistic shot at your operation: which queues, workflows or decisions are high-volume and well-documented enough to automate safely, and which are not — ranked by effort against expected impact, not by what is fashionable.
Customer-facing and internal agents scoped to a specific job with a defined escalation path to a human, not an open-ended assistant. Built against real conversation data where it exists, with the failure modes designed in from the start rather than discovered in production.
Workflow and operations automation across the tools you already run — n8n, Make, or custom pipelines where a no-code tool will not stretch far enough. CRM updates, document routing and cross-system syncs that used to need a person watching a queue.
Retrieval over your own content, drafting and summarisation inside an existing workflow, classification and routing — each one built against the metric it is meant to move, with an evaluation method agreed before the feature ships, not after.
Questions we get asked
Almost always existing foundation models, integrated against your data and workflow. Training a model from scratch is rarely the right call for the problems most SMEs and startups actually have — we will tell you plainly if your case is the exception.
We design the data flow with you before any integration — what leaves your systems, what stays in them, and which providers are in scope — and document it, rather than defaulting to whatever a demo happened to use.
Against the KPI the underlying job already had — resolution time, queue length, conversion, hours saved — agreed before the build starts, with a monitoring setup that flags drift or failure rather than assuming it keeps working forever.
The right structure for the decision in front of you.
Deliver an agreed outcome through defined releases and acceptance points.
Discuss the launch project modelExtend your team across an agreed mix of technology, commercial and marketing work.
Discuss the embedded growth partner modelProtect, maintain and improve delivered systems and campaigns after launch.
Discuss the support & optimisation modelFree 30-minute consultation
Bring the goal and the constraints. In thirty minutes we will tell you honestly what it would take, whether or not it starts with us.
Or write to info@qubed.io · +20 107 022 2349