How we work with product teams

We're a good fit if you own or build a serious digital product, whether SaaS, platforms, internal tools or complex content sites, and you care about architecture, quality and long-term maintainability. Most of our clients are funded startups, product teams and organisations in domains like healthcare, education, government, SaaS and media who want a senior partner, not the cheapest possible developers.

We're usually not a fit for very low-budget brochure sites, "cheapest hourly rate" comparisons or large armies of juniors. If you're unwilling to define basic constraints like goals, budget range and timeline, or expect us to "just execute tickets" without any product or architecture input, we will typically recommend other options.

Yes. A common pattern is a Codepixel squad owning a product stream or module while working closely with your internal team on roadmap, architecture and reviews. We can plug into your existing stack and processes, or help you evolve them where needed.

Occasional small scopes are fine when they fit into a broader product relationship, but we're not set up for random ticket-only work. Our best results come from engagements where we own a meaningful slice of the product with clear outcomes.

Yes. We integrate with your existing stack and third-party systems, whether CRM, ERP, payments, registries, data sources and internal tools, and we plan the unknowns and failure modes up front instead of discovering them in production.

Every engagement is led by a senior triad, Tech Lead, Design Lead and Product/Delivery Lead, supported by engineers, QA and DevOps. That squad owns outcomes, architecture and day-to-day delivery instead of just closing tickets, with regular demos, clear documentation and direct access for your stakeholders.

We usually start with a 30–45 minute discovery or triage call, followed by a short paid discovery or scoping sprint to clarify goals, scope and feasibility. From there, we move into delivery in well-defined phases with milestones, demos and clear success criteria, ending with launch, handover and, optionally, ongoing development or DevOps.

For new products or complex work, yes: a focused discovery or scoping sprint saves time and risk for both sides. For smaller, well-understood increments on existing systems, we can sometimes go straight into a small delivery phase after a structured triage call.

We use predictable cadences: weekly or bi-weekly check-ins, written updates, demo sessions and shared roadmaps. You always know who is working on what, what's shipped recently and what's coming next, with transparent conversations if trade-offs or changes are needed.

Most teams start with a short discovery or scoping sprint to clarify goals, scope and constraints, then pick the right delivery service from there. If you already have a clear MVP scope and budget, jumping straight into MVP development is fine; for AI features or platform rebuilds, a short discovery usually pays for itself many times over.

Yes, and most real engagements do. Discovery + UX/UI + MVP development often run together; custom software development + AI integration + DevOps run as one ongoing squad. We'll recommend the combination that best fits your goal rather than padding scope you don't need.

Real products. In discovery we can turn an idea into a clickable prototype fast, to validate with users or investors, but it is built to grow into production, not thrown away. From there we ship in milestones you approve, with the same quality gates regardless of speed. We do not ship demos that fall apart the moment real users arrive.

Both. A lot of our work is on products that already exist, whether adding to a React, Node, NestJS or .NET app, untangling a stalled build or rebuilding the parts that are holding you back. We start by reading the code and the data model, agreeing what stays, what gets refactored and what gets replaced, then shipping a first real slice before any large rewrite. We don't insist on a greenfield restart to feel comfortable, and we won't hand you back a black box.

Yes, and the way to avoid that rewrite is to decide tenancy early, not late. Most SaaS pain we see traces back to tenant isolation, roles and billing being bolted on after launch. We choose row- or schema-level isolation up front so one customer can never reach another's data, design access control that survives new tenant types and admin roles, and we model plans, seats and usage limits into the product rather than patching them in later. We default to a modular monolith and split out services only when real load justifies it.

Yes. We can step into an existing AWS or Azure environment rather than insisting on a rebuild. We start by mapping the current setup, the integration risks and the security and uptime requirements, then harden and automate from there: CI/CD, monitoring, access control and encryption, with environments that rebuild the same way every time. We can also integrate with the CI/CD and tooling your stack already uses. The goal is a setup you can run yourself, with documentation and runbooks, not a dependency on us.

Both are on the table, and you choose. Many relationships continue as a retainer where we operate the product: monitoring, alerts and health checks on long-running systems, backup and restore strategies that cover disaster recovery, cost control and security hardening as it grows. We don't sell a fixed uptime guarantee or 24/7 on-call as a default. We agree the operational scope and response expectations with you up front. If you'd rather run it in-house, we hand over with documentation and runbooks so you're not dependent on us for basic operations.

Not if the migration is planned around it. Before we move anything we map every URL we cannot afford to lose and build a redirect map, so old links and rankings carry over to the new platform. Content moves with its structure and metadata intact, not flattened, and we check Core Web Vitals and indexing against the old site with real numbers. The cutover happens behind the redirects and stays reversible, so a bad surprise is a rollback, not weeks of lost traffic.

A plan you can build from, not a research deck. Depending on the track that means an MVP scope or a sitemap and key-page wireframes, a phased roadmap with milestones, an architecture and tech-stack direction, the open risks written down, plus a ballpark budget and timeline for the build that follows. Where it applies, you also get a prioritised AI and automation opportunity map.

No. Producing that is what discovery is for. A rough goal, your real constraints and a first milestone are enough to start. If you arrive with a detailed brief we will happily pressure-test it; if you arrive with a one-line idea, we will help you shape it into a scope your stakeholders agree on and your engineers can build from.

Buildable. Because we're engineers as well as designers, we design with real constraints in mind: component states, edge cases, empty and error states, plus the data your screens actually have to show. You get a developer-ready handoff with spacing, tokens and behaviour documented, not a set of static frames that fall apart the moment someone starts coding.

That depends on what you need, and we'll be honest about it. If you're shipping a handful of screens once, a full design system is overkill. If you're scaling a product, we build a tokenised component library: type, colour, spacing and reusable components, so your team can ship new screens consistently without redesigning from scratch every time.

A prioritised list of what's hurting you and what to fix first, not a vague slide deck. We review your real flows against how people actually use the product, flag the friction costing you conversions or support tickets, then rank fixes by impact versus effort. You leave with a clear plan you can hand straight to a team, whether that's ours or yours.

No. A clear goal, your constraints and a first milestone are enough to start a discovery or scoping call. We turn that into a tight first slice rather than waiting on a 40-page document. The spec firms up as we build, because the fastest way to clarify scope is usually working software in front of real users, not more planning up front.

Yes. That is most of what this work is. We start with an audit: we read the codebase, map the risky parts, check what is and isn't tested, then look at how it's deployed, monitored and backed up. You get an honest picture of its state and a prioritised list before we touch anything.

From there we stabilise first: security updates, the bugs that hurt most, a release process you can trust, then settle into steady delivery. You don't need the original team around for us to own it.

Investor-ready means you can put something real in users' hands and walk into a raise with more than a deck: a working product, early usage signals and a clear scope, roadmap and budget for what comes next. We build on a clean architecture with tests and CI/CD from day one, so technical due diligence finds a foundation, not a pile of throwaway code.

We won't pretend traction we can't deliver. The MVP gets you something credible to demo and a defensible plan, not vanity metrics.

No. The same senior triad that built the MVP can carry straight on as a dedicated squad once you've validated direction or closed a round, usually on a retainer rather than another fixed-scope phase, since the roadmap is moving by then. There's no handover to a different, more junior team.

If you'd rather take it in-house, the clean architecture, documentation and runbooks are built so your own engineers can pick it up without a rewrite.

It means working software lands in a real environment every week, not a status update. We slice the roadmap into thin vertical features so you see and use something each week, demoed as it ships rather than saved for a big reveal at the end.

That cadence is the point. It keeps feedback tight, surfaces the risky integrations early and means progress is something you can click on, not a percentage on a Gantt chart.

Yes. That's the point of designing against engineering reality rather than in a side room. A tech lead reviews feasibility as the work goes, so flows and screens stay buildable, and you get developer-ready handoff: specs, design tokens and component notes, not just pretty Figma frames. The result is fewer design-to-build rounds, because the structure was feasible from the start.

You get a system, scoped to what your product needs. Alongside the flows and high-fidelity screens we build a UI kit and component library once, so the interface stays consistent as features and data grow. Your engineers build against components, not one-off mockups. For a small product a lighter kit is enough, and we'll tell you when a full system would be over-engineering.

Urgent issues on a live, revenue-generating product jump the queue. We agree response and resolution targets up front and hold ourselves to them, with a clear escalation path so you are never waiting on a ticket while customers are affected. Day-to-day, non-urgent work flows through the monthly plan rather than ad-hoc fire drills. If your product genuinely needs round-the-clock cover, we will tell you honestly whether a retainer is enough or whether you need dedicated on-call.

We plan each month together: a slice of capacity is ring-fenced for keeping the product healthy: fixes, dependency upgrades, performance and observability, and the rest goes to the roadmap features you want to ship. You set the priorities; we are opinionated about what is risky to defer and will say so. Nothing important quietly rots in the background just because a shiny feature is louder.

Often WordPress is fine, and we will tell you when it is. Headless earns its place when you need real design control, multiple channels or an API-first front end, and we pair Strapi with Next.js for that. When the priority is a familiar editor and speed to launch, WordPress usually wins, and Webflow suits design-led marketing sites. We match the platform to your team and your content, not to a trend, because headless wins less often than people think.

We use AI to accelerate research, UX exploration, coding, testing and documentation, under clear human-owned standards. Seniors still make the calls on architecture, security, trade-offs and what actually ships; AI reduces busywork and iteration time instead of replacing judgment.

We operate under a written AI Use Policy that defines which tools we use, what data can go where and which decisions must remain human-owned. Sensitive production data and secrets are never casually fed into external systems, and all AI-assisted work goes through the same reviews, testing and quality gates as everything else.

By default, no. We pseudonymise or abstract where needed and follow per-client rules about what may or may not leave your environment. For clients with stricter requirements, we can use self-hosted or tenant-isolated AI options, defined together during onboarding.

We treat quality and reliability as first-class concerns: automated tests where they add value, clear performance budgets, logging/monitoring and incident practices. Part of our role is to leave your systems healthier than we found them, not just ship features.

Senior leads own architecture and reviews; AI accelerates the busywork: scaffolding, tests, documentation. The same quality gates run regardless of speed: code review, automated tests, performance budgets and observability. Speed comes from removing friction, not skipping checks.

We plan data handling, access control and auditability into the build from week one, and our quality and security management are certified to ISO 9001 and ISO 27001. For regulated work we have shipped systems with staged verification, encryption at rest, web application firewalls and full logging. Where we process personal data we sign a data processing agreement and you stay the data controller.

We default to retrieval over your own data, so answers are grounded in your sources and cite where they came from instead of being generated from the model's memory. Before launch we build evals on real cases with acceptance thresholds, so we measure answer quality rather than hoping it's good, and we add input and output guardrails plus a human-owned fallback for when the model is unsure. Where we can't reach a quality bar we can stand behind, we'll tell you the feature isn't ready rather than ship a confident guess.

No lock-in by design. We treat the model as a swappable component behind your own retrieval layer and structured-output contracts, and we work across providers like OpenAI, Anthropic, Google Gemini, Azure OpenAI and AWS Bedrock so you can move if pricing, latency or policy changes. You own the same things you own everywhere else with us: the code, the prompts, the eval suite and the retrieval index over your data, handed over with documentation, so the feature isn't dependent on us or on a single vendor to keep running.

Yes. That's what retrieval-augmented generation (RAG) is for. We connect the model to your own content: docs, tickets, product data, knowledge base, so answers are grounded in your sources rather than the model's general training, and we cite where each answer came from so you can check it. The hard part is rarely the model; it's getting your data clean, chunked and permission-aware, which is where most of the engineering goes.

We treat AI like any other production system: measured, not assumed. Before launch we build an evaluation set that scores answers against known-good cases, and once live we monitor quality, cost and latency, with logging and alerts when behaviour drifts. That way a regression from a new model version or a change in your data shows up on a dashboard, not in an angry customer email.

Honestly, for many teams the data needs work first, and we'll tell you if it does rather than bolting AI onto a shaky foundation. We can assess what you have, sort out the pipelines, structure and access controls, then build the data layer the AI actually depends on. Sometimes the most valuable thing we ship in the first phase isn't a model at all; it's a clean, reliable foundation that makes everything after it possible.

Usually, yes. We start by reviewing what you have: the prompts, the retrieval setup and where it breaks, and keep whatever earns its place. The gap with most prototypes isn't the happy path; it's evaluation, guardrails, monitoring and a human fallback for when the model is unsure, which is exactly what this package adds. If the foundation is sound we build on it; if it'll fight us past week two, we'll tell you honestly and rebuild the part that needs it.

We ship with monitoring and an eval suite, so accuracy is measured in production, not assumed. When answer quality drifts or a provider changes a model, you see it instead of finding out from a user. You own that eval suite and the retrieval index over your data, so your team can re-run the checks and tune prompts without us. Where you'd rather we stayed on, we can keep the feature maintained on a light retainer; either way, it's handed over with runbooks and the limits written down.

We work through a small set of proven models: discovery and scoping sprints, fixed-scope projects, ongoing retainers for product growth and platform care, plus dedicated senior-led product squads. Fixed-scope projects cover work like MVPs, rebuilds and AI integrations. We'll recommend the lightest model that still protects outcomes and risk for both sides.

Serious new builds usually start at a meaningful multi-week scope (roughly €25–30k+ for an MVP) and scale with complexity, integrations and team size. Ongoing retainers are designed to support a focused senior-led squad rather than "a few hours per month".

For a well-scoped MVP, 8–12 weeks from alignment to launch is typical, depending on complexity and how quickly we make decisions together. Larger platforms or rebuilds are usually phased into several releases so you can see value and reduce risk earlier.

We work in clear phases and keep trade-offs explicit. If new work appears, we either adjust the current phase, with conscious cuts or extensions, or carve out a follow-up phase, rather than quietly stretching timelines and budgets.

It depends on the service. Discovery sprints, MVPs and AI integrations are typically fixed-scope with clear pricing per phase. Long-term partnerships, dedicated squads and ongoing growth or maintenance work as monthly retainers. We pick the model that lowers risk for both sides.

A dedicated squad is a monthly retainer based on its size and shape, not a per-ticket rate. You are paying for a stable team that owns a slice of your roadmap. We usually start with a one to three month minimum so context can settle, then keep things rolling month to month.

You can scale the squad up or down with reasonable notice as the roadmap changes. We would rather right-size the team than bill you for capacity you are not using.

Fixed scope means we agree the must-haves up front so the 8 to 12 week timeline and budget hold. It doesn't mean we ignore what we learn. Small trades are easy: swap something of similar size out of scope to make room, and we'll tell you honestly when a change is a like-for-like swap versus genuinely new work.

If what we learn reshapes the product, we re-scope deliberately rather than quietly absorb it and miss the date. The point of fixing scope is to protect your runway, not to trap you in the wrong build.

Each phase is fixed against what we know now, with a clear deliverable and budget, so you are never committing the whole build on day one. We re-plan at every milestone, which is where new priorities get folded in. The scope moves between phases, not silently inside one.

When the roadmap is genuinely still in motion, we will tell you a dedicated senior-led squad is the honest fit instead, so the same people keep context and keep shipping without re-quoting every change.

Either works. This is a fixed-scope design phase, so you can take the flows, UI and design system and hand them to your own team. If you'd rather we keep going, the same senior people can continue into the build, which avoids re-explaining decisions and losing intent in handoff. We'll recommend whichever protects the outcome, not whichever keeps us on the clock.

One feature, deliberately. This package takes a single use case, an assistant, RAG search or one automation, from a prioritised idea to something running in production with guardrails, rather than an open-ended "add AI" programme. We start with a short discovery to score the use cases on value, feasibility and risk, agree the one to build first and keep the rest as a backlog. If a broader rollout makes sense later, we phase it, but we'd rather ship one feature your users rely on than five that only demo well.

A retainer is a commitment to availability, not a stopwatch, so quieter months go into reducing technical debt and hardening the product rather than disappearing. You can scale the tier up when you are pushing on a release and down when things are steady. We would rather you paid for what you actually need than over-buy. There is no lock-in trap: if the work no longer justifies a retainer, we will tell you.

We're based in Montenegro and Canada, working primarily with teams across Europe, the UK and North America. We design collaboration around overlapping hours, structured async updates and English-first documentation so communication stays predictable.

You work directly with senior leads and experienced ICs; we don't hide a junior factory behind them. The people you meet in early conversations are usually the ones leading or working on your project.

We adapt to your stack within reason, but typically use a mix of Slack or Teams, Jira or Linear, plus modern code and design tools. We keep everything documented, tickets, PRs, decisions, so your team can follow along and step in easily.

One squad. We build the web front end, the API behind it and a cross-platform React Native client together, and drop to native Kotlin or Swift when the platform genuinely needs it. Keeping web and mobile on the same senior team means a shared architecture and one set of people accountable, not a handoff and two codebases that drift apart.

A fractional CTO gives you senior technical ownership without a full-time hire: architecture and roadmap decisions, reliability and security calls, AI strategy, plus guidance on technical hiring and vendors. You get the judgement of someone who has shipped and run real products, on the days you need it rather than five days a week.

The honest trade-off: a fractional CTO won't live inside every standup or own your culture the way a full-timer would. When you reach the point where you need that, we'll tell you, and help you hire the permanent person.

Continuity is our problem to solve, not yours. The squad is senior-led and documents as it goes: decisions, architecture and runbooks live in your repositories, so knowledge sits with the team, not one person's head. If someone moves on, we hand over with overlap and keep the named senior owner accountable throughout.

You do. We usually work in your repositories and cloud accounts, or hand them over at the end, with clear documentation and runbooks so you're not dependent on us for basic operations.

Yes. We plan for eventual handover with clean architecture, documentation and joint sessions with your engineers or PMs. Many clients bring more work in-house over time, and we see that as a success.

Many relationships evolve into long-term retainers where we handle new features, refactors, performance and DevOps. We agree on a stable squad and scope so it feels like an extension of your own organisation rather than ad-hoc freelancing.

Yes. We sign your NDA before a discovery call if you want one, and confidentiality is built into every engagement agreement and survives the project. We use enterprise-controlled AI tools and never put your confidential or production data into them without your written agreement. You own the code and IP on full payment.

Yes. The IP in the custom work we build for you is assigned to you on full payment for that work, agreed in writing before we start. If you pause or end the engagement you keep everything you have paid for, with the source code and reasonable handover materials. There is no lock-in, and AI-assisted code is validated and assigned the same way.

No lock-in by design. We treat the model as a swappable component behind your own retrieval layer and structured-output contracts, and we work across providers like OpenAI, Anthropic, Google Gemini, Azure OpenAI and AWS Bedrock so you can move if pricing, latency or policy changes. You own the same things you own everywhere else with us: the code, the prompts, the eval suite and the retrieval index over your data, handed over with documentation, so the feature isn't dependent on us or on a single vendor to keep running.

Yes. It's yours either way. Discovery is a fixed-scope paid phase, and everything it produces, the scope, the roadmap, the architecture direction and the wireframes, belongs to you, written so another team could pick it up. We would obviously like to build it with you, but we deliberately make the discovery valuable on its own, not a hook that only works if you hire us next.

Yes. We often deliver under a partner's brand as a white-label engineering and product team, with clear agreements about communication, ownership and how we support your client relationships.

Yes. We work with investors and venture studios as a repeatable delivery partner for MVPs and product builds across a portfolio, using structured discovery, clear scopes and AI-augmented delivery to move fast without cutting corners.

We're explicit about roles, communication lines and who owns which relationship from the start. Our goal is to strengthen your position with your clients, not compete with it.