Website and Mobile App Developers for Dedicated Product Teams
A dedicated development team from Mixcore Studio is a small, named group of engineers who write, review and verify the code for your web or mobile product, working to your backlog rather than filling seats on a timesheet. We have shipped production software for more than 8 years, with a team of about 20 people and more than 320 projects delivered.
We build in SvelteKit and Svelte, ASP.NET Core, TypeScript and PHP on the web, and cross-platform mobile alongside native where the platform demands it. That has not changed. What changed between 2024 and 2026 is what a developer actually does with the day, how the work is priced, and what a buyer is entitled to ask for before signing. This page describes the current version rather than the version that was true when the site first advertised it.
Team shape changed before team size did
Gartner predicts that 60% of organisations will run small software engineering teams at scale by 2029, up from 15% in 2026, and describes the typical shape as four or five people including a product manager, a UX designer and at least one AI-native engineer (Gartner press release, July 2026). Gartner principal analyst Aliyah Camacho frames this as a restructuring of teams to take advantage of AI and human expertise, not as headcount elimination, and Gartner is explicit that engineering leaders should not stop hiring and developing junior talent: it predicts that by 2028, organisations that lean on AI to cut junior roles will damage their own engineering talent pipeline.
The job inside that team has moved. First-draft code is no longer the scarce activity. Deciding what should be built, scrutinising what was produced, and proving it is correct and secure is the work we staff for, and it is why our teams are senior-weighted rather than pyramid-shaped.
The classic offshore pyramid, with a large junior bench billed at leveraged margins, is not something we will quote you. The tasks that historically trained and monetised juniors, such as boilerplate, simple bug fixes and manual regression passes, are the tasks AI absorbed first. Stanford Digital Economy Lab's August 2026 update to 'Canaries in the Coal Mine', using ADP payroll data to June 2026, finds employment for workers aged 22 to 25 in highly AI-exposed occupations sitting about 19% below its counterfactual, with the adjustment happening through reduced hiring rather than layoffs. The authors are explicit that these are descriptive patterns, not causal estimates, and there are real counter-signals: the US Bureau of Labor Statistics still projects about 16% growth in software developer employment from 2024 to 2034. Juniors are not obsolete. Selling them to you as leveraged capacity is.
What the evidence on AI-assisted delivery actually says
We do not quote vendor benchmarks at you, because the independent evidence is genuinely contested and any buyer who has read it will notice.
- METR — in a randomised controlled trial run February to June 2025, experienced open-source developers took 19% longer to complete tasks on their own repositories when allowed to use AI tools, while believing they had been about 20% faster. METR's February 2026 follow-up reported a speedup of -18% for returning developers and -4% for new recruits, both negative point estimates and neither statistically significant. METR calls its own data only very weak evidence, notes severe selection bias, and has redesigned the experiment. The strongest good-faith objection is that the tooling tested was Cursor with Claude 3.5 and 3.7 Sonnet, frontier at the time and materially behind August 2026 models.
- DORA — Google's 2025 State of AI-assisted Software Development reversed its own 2024 finding and now associates AI with improved delivery throughput, but simultaneously with increased delivery instability. DORA's framing is that AI amplifies existing organisational strength or dysfunction.
- Stack Overflow — in the 2025 Developer Survey of around 49,000 respondents, 84% use or plan to use AI tools, but only 3% highly trust the output, 46% actively distrust its accuracy, and 45.2% say debugging AI-generated code takes more time.
The consistent reading across all three is that AI moves the bottleneck rather than removing it. It shifts effort from writing code to reviewing, verifying and stabilising it. A vendor promising you a flat multiplier on delivery speed is describing a benchmark, not a delivery record. What we will give you instead is our measured cycle time, review latency and change failure rate on your account.
Review, security and maintainability are staffed functions
Veracode's 2026 GenAI Code Security Report, published 28 July 2026, measured an average security pass rate of 56% for AI-generated code, essentially unchanged from 55% a year earlier, with roughly 44% of code-generation tasks introducing a risky vulnerability. Log injection passed 12% of the time and cross-site scripting 15%, while cryptographic algorithm selection passed 87% and SQL injection 83%. The same report finds AI now authors roughly half of all committed code in organisations that have adopted AI coding tools.
Treating pull-request review as free residual capacity is therefore a pricing error, not a process preference. We cost it explicitly.
- Named reviewers — the seniors on your account are the ones reading the diffs, and we publish the reviewer-to-author ratio and review latency rather than leaving you to infer them.
- No unreviewed merges — AI-authored changes go through the same gates as hand-written ones, with static and dynamic analysis in CI, and a human approver on every merge to a protected branch.
- Category-aware scrutiny — the Veracode failure categories tell you where to spend review time. Input handling, output encoding and logging get more attention than cryptographic library selection.
- Maintainability watched, not assumed — the standing risk with high-volume AI-authored code is that duplication accumulates faster than anyone refactors it away. We track duplication and churn on your repository so a fast first year does not buy an expensive third one.
- Quality engineering, upstream — Capgemini's World Quality Report 2025-26 finds 43% of organisations experimenting with generative AI in QA but only 15% scaled enterprise-wide, and 60% still struggling with secure, scalable test data. Our testers work on test data strategy, synthetic data and verification of AI-authored code, not on running a manual regression pass.
What we put in the contract
Enterprise buyers now write these terms into master agreements as standard. We would rather agree them at signature than argue about them after an incident.
- AI usage disclosure — we state where and how AI was used in producing deliverables. Undisclosed use is no longer defensible commercially, and EU AI Act Article 50 independently imposes disclosure and machine-readable marking duties on AI-generated content from 2 August 2026, with a grace period to 2 December 2026 for systems already on the market. Article 50 was not postponed by the AI Omnibus.
- IP warranty that names the AI-generated portions — rather than a blanket claim that all deliverables are original work, with indemnity separated into an IP track and a data-misuse track.
- Training-data exclusion — your code, data and documents are excluded from any third-party model training, with the tooling named and the inference location stated.
- Agent autonomy boundaries — which actions an automated agent may take unsupervised in your repositories, CI, cloud accounts and ticketing, and which require a named human approver.
- Audit and decision logs — retained past termination in machine-readable form, carrying provenance, the distinction between planned and executed actions, and policy version stamps.
- Exit and portability — knowledge-base ownership, prompt and agent configuration handover, and runbook transfer, negotiated while you still have leverage.
On regulatory flow-down, the 2026 map differs from what most vendors prepared for. Regulation (EU) 2026/1744, the AI Omnibus, was published in the Official Journal on 24 July 2026 and entered into force on 27 July 2026, moving Annex III stand-alone high-risk obligations from 2 August 2026 to 2 December 2027 and Annex I product-embedded obligations to 2 August 2028. That is a deferral, not a repeal. Separately, the EU Cyber Resilience Act requires manufacturers to report actively exploited vulnerabilities to ENISA and the national CSIRT within 24 hours from 11 September 2026, including for products already on the EU market, with the Annex I SBOM requirement following on 11 December 2027. We keep a component inventory from the first sprint, using CycloneDX, because the 24-hour duty is unmeetable without already knowing what is in the build. Under NIS2, regulated entities must impose cybersecurity obligations on direct suppliers by contract, backed by penalties up to EUR 10 million or 2% of global annual turnover, and we sign those clauses rather than arguing we are out of scope.
As a Vietnamese supplier we also operate under Vietnam's own regime, which your counsel may not have met. Vietnam's Law on Artificial Intelligence took effect on 1 March 2026, classifying AI systems by risk level with the heaviest obligations on high-risk systems, and imposing Article 11 duties to make AI systems recognisable as AI to the people interacting with them and to apply machine-readable markers to AI-generated audio, image and video. The Law on Digital Technology Industry, passed on 14 June 2025 by 441 of 445 National Assembly deputies and in force since 1 January 2026, is the first standalone law for the sector anywhere. We will document which duties sit with us and which sit with you rather than leaving the boundary undefined. Where a contract asks for a certification such as SOC 2, ISO/IEC 42001 or documented NIST AI Risk Management Framework alignment that we do not hold, we say so plainly and work to the control set and evidence requirements it specifies.
How a team is priced
Selling bodies per month is a shrinking business and we will not pretend otherwise. HCL's chief executive told investors to expect 3% to 5% annual revenue deflation from AI, TCS's chief executive called it degrowth, and reported FY2026 figures show TCS annual revenue down 0.5% year on year while HCL grew 11.2% with headcount up only around 2% (The Register, 28 April 2026). Buyer demand is moving the same way, towards output- and outcome-based contracting and away from pure headcount supply.
- Time and materials — still correct for genuinely open-ended discovery, and we quote it, but never as the only option on a proposal.
- Output-based — priced per feature or per resolved ticket, which is the tier most buyers ask for once scope is stable enough to define a unit.
- Outcome tier — written against metrics you already own, such as defect escape rate, reopen rate or first-contact resolution, rather than KPIs we invented for the proposal.
- Escalation caps — Vietnamese IT wage inflation and short average engineer tenure are both real and both get priced somewhere, so we agree a cap and a continuity plan up front instead of reopening the rate at every renewal.
For calibration, published Vietnamese rate cards vary widely by seniority and by city, with Hanoi and Da Nang generally quoted below Ho Chi Minh City. Those figures are vendor-published rather than independently audited, ours included, and should be treated as a range rather than a benchmark — ask us for our current rate card in writing and compare it against two others. Cost is in any case no longer the only reason buyers outsource; access to scarce specialists is now cited at least as often.
When a dedicated team is the wrong choice
Several situations are better served by something other than what this page sells.
- A single well-defined deliverable — if the scope genuinely will not move, a fixed-scope project costs less and carries less overhead than a standing team.
- One specialist for a few weeks — a short, sharp engagement with a named individual is cheaper and faster than standing up a team around them.
- No internal product owner — a dedicated team needs someone on your side who can make decisions weekly. Without that, the team will either idle or build the wrong thing quickly. Fix that before hiring anyone, including us.
- A stable system with low change volume — a support and maintenance retainer fits better than a team you are paying to stay assembled.
- Lowest hourly rate as the deciding factor — we are not the cheapest option in Vietnam, and a vendor that wins purely on rate will usually staff the pyramid we described above.
Agent washing — relabelling ordinary automation as agentic AI — is common enough that industry analysts have given it a name, and a large share of agentic AI projects are still expected to be abandoned on cost, unclear value and weak risk controls. If a prospective vendor's differentiator is an AI demo rather than production data from a comparable account, ask for the account.
Our expertise
- Small senior teams
- Code review and security gates
- Web and mobile engineering
- Test data and quality engineering
- Compliance and SBOM readiness
- Output and outcome pricing
Frequently asked questions
Am I paying human rates for AI-generated code?
It is the fairest question a buyer can ask in 2026, and the honest answer is that AI leverage shows up in the commercial model rather than being quietly pocketed. We quote an output-based tier priced per feature or per resolved ticket alongside time and materials, and an outcome tier written against metrics you already measure. Where AI genuinely reduces the effort on a unit of work, the unit price reflects it; where the saving is absorbed by extra review, we will show you the review data rather than assert it.
Who owns code your developers generate with AI, and who indemnifies an IP claim?
You own the deliverables. We give an IP warranty that explicitly addresses the AI-generated portions rather than a blanket claim that everything is original work, because a blanket clause is one no vendor can honestly stand behind now. Indemnity is split into an IP track and a data-misuse track so the two risks are priced and handled separately.
Will our code or data be used to train an AI model?
No. A training-data exclusion covering all client materials is standard in our contracts, and we name the specific tooling in use and where inference runs so your security team can assess it rather than take our word for it. If a tool we want to introduce changes that position, we ask first.
How big should a dedicated development team be, and how senior?
Gartner's projection is that small teams of four or five, typically a product manager, a designer and one or more AI-native engineers, become the dominant shape by 2029. That matches what we see working. The number to probe is not headcount but the reviewer-to-author ratio and review latency, because with AI authoring roughly half of committed code according to Veracode, a team that generates faster than it can genuinely read is producing liability rather than progress.
Can you meet EU Cyber Resilience Act and AI Act obligations as a subcontractor?
We build to them. The CRA's 24-hour reporting duty for actively exploited vulnerabilities applies from 11 September 2026, including to products already on the EU market, so we maintain a CycloneDX component inventory from the first sprint. EU AI Act Article 50 disclosure and machine-readable marking duties apply from 2 August 2026 and were not deferred by the AI Omnibus, so any generative feature we build ships with them. We also accept NIS2-grade security clauses flowed down from your contracts rather than claiming to be out of scope.
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