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MVP Development
Minimum viable products built for speed to market without the architectural shortcuts that force a full rebuild at your next funding round — the constraints of an MVP do not have to be the constraints of the product that follows it.
AI-Accelerated Software Development Solution for Startups
We are a software development company for startups helping founders at idea stage through Series A build the MVPs, products, and engineering foundations that validate their thesis, attract investors, and scale without the architectural rebuild that catches most fast-growing startups at the worst possible moment. From your first production release to your first enterprise customer, we build the software that moves your company forward on the timeline and budget that startup economics actually allow for.
13+
Years of Experience
50+
Experts in Our Team
40+
Happy Customers Worldwide
250+
Projects Delivered Successfully
4.9/5 ratings
5/5 ratings
What We Build for Startups
Solvios is a software development company for startups building MVPs, full product platforms, mobile applications, AI-integrated features, and the engineering infrastructure that early-stage and growth-stage companies need to move from concept to customers to capital. We work with pre-seed founders building their first product, seed-stage companies scaling past their first thousand users, and Series A companies handling the engineering complexity that comes with product-market fit and investor expectations. Across every stage, the goal is the same: software that moves the company forward without the technical debt that becomes the next fundraising problem.
Product Engineering
Full-cycle product development from architecture through launch and iteration — covering web, mobile, API, and the data infrastructure that product decisions depend on, delivered in the sprint cadence that startup timelines require.
AI-Integrated Product Development
Products where AI is part of the core value proposition from the first release — not a feature added after the initial build when retrofitting it requires rebuilding the data layer that should have been designed to support it from the start.
Mobile App Development
iOS, Android, and cross-platform mobile products for startups that need their software in the pocket of every user — built mobile-first rather than adapted from a desktop product that does not translate to a small screen.
Technical Architecture Consulting
Architecture decisions for founders and early engineering teams at the stage where the choices made in the next sprint shape the product's scalability, maintainability, and cost structure for the next three years.
Why Startups Come to Us
Building software as a startup is a different problem from building software as an established company. The runway is finite. The requirements are uncertain. The architecture decisions made at speed in month two become the scaling problems in month eighteen. The development partner who was right for the MVP is often not right for the growth phase. We have worked with startups across every stage and we know where the real problems are — not the ones that show up in pitch decks but the ones that show up at 2am when the staging environment is down the day before a demo.
MVPs Built for Demo Day, Not for What Comes After
The fastest MVP is not always the best MVP. Startups that optimise entirely for speed to demo day end up with a codebase that cannot be shown to a technical due diligence team without embarrassment, cannot be extended without a near-complete rewrite, and cannot handle the user load that a successful launch generates. The architecture decisions skipped in the interest of shipping fast are almost always the ones that create the most expensive problems six months later when the company has users depending on the product and investors watching the metrics.
The First Hire Architecture Problem
Most early-stage startups make their first technical architecture decisions with whoever is available, a co-founder who knows Python, a freelancer who knows React, a development agency that builds everything in the framework they happen to be most comfortable with. These decisions compound over time. By the time the company is raising a Series A and interviewing engineering candidates, the codebase reflects the knowledge and preferences of whoever happened to be available at the start rather than the architecture that was right for the product.
Scope That Grows Faster Than the Budget
The gap between 'what the product needs to do to validate the thesis' and 'what the product needs to do to make every stakeholder happy' is where most startup software projects lose their economics. Scope discipline in a startup MVP is not about building less — it is about being ruthlessly clear on what the first release is actually trying to learn and building only what is necessary to learn it. Development partners who say yes to every feature request without pushing back on whether it belongs in the MVP are not serving the startup's interests.
Finding a Development Partner Who Acts Like a Partner
Most startup founders who have worked with software development agencies describe the same experience: the agency builds what they were asked to build, delivers it on time, invoices correctly, and leaves the founder with software that is technically functional but does not quite solve the problem they were trying to solve — because the agency never pushed back on the spec, never asked why the feature was being built, and never engaged with the business logic behind the requirements. A startup needs a development partner who thinks about the product, not just a vendor who executes on a brief.
Technical Debt That Arrives at the Worst Possible Time
Technical debt in a startup does not announce itself during a quiet period. It announces itself during a growth spike when the infrastructure cannot handle the load, during a fundraising process when a technical due diligence reveals problems the team knew about but had been deferring, or during a critical integration when the API design from the MVP does not support what the enterprise customer needs. The startups that avoid this pattern are the ones that made slightly better architecture decisions under time pressure at the start — not the ones that spent more time on the MVP.
Software Development Services We Deliver for Startups
Every startup engagement starts with the thesis the product is testing, the timeline the runway allows, and the architecture decisions that will either compound in value or compound in debt. The software is built to validate fast without creating the rebuild that most fast-growing startups hit six months after their best quarter.
Minimum viable products built to validate the core product thesis with real users — on the timeline startup economics require and with the architecture that does not become the ceiling on what the company can build next. We have a specific point of view on MVP development that most agencies do not share: the MVP is the first version of a real product, not a prototype dressed up to look like one. The difference matters when you are scaling from 100 users to 10,000 and the code that got you to the first milestone determines how hard the next one is.
Thesis-first scope definition
Before the first sprint plan, we define what question the MVP is designed to answer, what user behaviour, what willingness to pay, what retention signal, what integration capability and build only what is necessary to answer it. Everything else waits for the validation signal that justifies building it.
Architecture that survives the pivot
Modular service boundaries, clean data models, API-first design, and the separation of concerns that makes the product extendable when the requirements change because in a startup they always change, and the architecture should make that cheap rather than expensive.
Speed without the shortcuts that matter
We move fast on the features and slow on the decisions that compound: database schema design, authentication architecture, API contract design, and infrastructure choices. The thirty minutes spent getting these right saves thirty days of migration work later.
Investor-ready code and documentation
Clean, documented, version-controlled codebase that survives technical due diligence because the Series A process involves someone senior looking at the code, and the quality of what they find shapes the terms of the round.
Full-cycle product development for startups that have validated their thesis and need to build the real product beyond MVP scope, beyond early adopter tolerance, ready for the mainstream users and enterprise customers that the next growth phase requires. This is the engagement where architecture decisions get revisited, technical debt from the MVP gets addressed on a measured timeline, and the engineering foundation gets rebuilt where it needs to be rebuilt before the company's growth makes the rebuild impossible.
Architecture evolution from MVP to production
Systematic assessment of what the MVP architecture can carry and what needs to change with a migration plan that addresses the most critical limitations first without stopping feature development while the foundation is being improved.
Feature development in parallel with debt reduction
The runway does not allow stopping the product to fix the codebase. We manage feature delivery and technical improvement in parallel, allocating sprint capacity to both, making the debt reduction visible on the roadmap, and measuring the operational impact as each improvement ships.
Performance and scalability engineering
Database query optimisation, caching layer design, background job architecture, CDN strategy, and the infrastructure scaling work that keeps the product performant as user volume grows from hundreds to hundreds of thousands.
Enterprise readiness and security hardening
SOC 2 Type II preparation, penetration testing, access control audit, data handling documentation, and the security posture improvements that enterprise sales cycles require because enterprise customers run security reviews before signing contracts, and the outcome of that review determines whether the deal closes.
iOS and Android mobile applications for startups building consumer or B2B products where mobile is the primary user interface. Most startup founders underestimate how different mobile development is from web development, the App Store and Play Store review cycles, the device fragmentation, the offline behaviour requirements, the push notification architecture, the in-app purchase mechanics, and the platform-specific UX conventions that determine whether a mobile product feels native or feels like a web page in an app wrapper. We build mobile products that feel like they belong on the platform they run on.
Cross-platform vs. native recommendation
Honest assessment of whether React Native or Flutter is right for the product and when native Swift or Kotlin is the correct choice. The recommendation is based on the product requirements and the startup's technical context, not on which approach generates more billable hours.
App Store and Play Store launch readiness
Review guideline compliance, privacy label accuracy, metadata optimisation, screenshot and preview asset production, and the pre-submission process that minimises the risk of rejection delays at a critical launch moment.
Mobile-specific feature architecture
Offline capability, background sync, push notification infrastructure, deep linking, biometric authentication, in-app purchase integration, and the platform-specific features that users expect from a mobile-first product.
Iteration velocity post-launch
CI/CD pipeline with TestFlight and Play Store internal testing tracks, feature flag infrastructure for controlled rollouts, crash monitoring, and the release cadence that keeps the mobile product evolving in response to user behaviour rather than stagnating between major updates.
Products where AI is part of the core value proposition from the first release, not a feature added after six months when the initial product has not differentiated enough from existing solutions. The startups building AI-integrated products today are not competing on whether they use AI everyone claims to. They are competing on whether the AI actually changes the user experience in a measurable way, whether the data architecture supports the AI capability they are promising, and whether the AI component is reliable enough to be the headline feature of the product rather than a disclaimer in the footer.
AI product strategy and architecture
Honest assessment of where AI creates genuine user value in the product versus where it is a feature that sounds impressive in the pitch deck but adds friction for real users. We push back on AI features that are not justified by the product thesis and help founders find the AI integration points that actually change the unit economics of the product.
LLM integration and prompt engineering
OpenAI, Anthropic Claude, Google Gemini, and open-source model integration with the production discipline AI workloads require — token cost monitoring, prompt versioning, output validation, fallback routing, and the evaluation framework that measures whether the AI is doing what the product claims it does.
Data infrastructure for AI
The data models, pipelines, and storage architecture that AI features depend on — because AI features built on top of a data architecture that was not designed for them require a rebuild of the data layer before the AI can be meaningfully improved.
AI product iteration and evaluation
Evaluation harnesses that measure AI feature performance quantitatively, A/B testing infrastructure for AI output quality, and the feedback loop from user behaviour back into the model that makes the AI component improve with use rather than degrading as edge cases accumulate.
Architecture advisory for founders and early engineering teams at the inflection points where technical decisions have outsized consequence — the pre-build architecture conversation that determines the data model, the pre-fundraise technical review that surfaces what a due diligence will find, the post-MVP assessment that maps what needs to change before the next growth phase, and the technology selection decision that will shape the engineering hiring pool for the next two years.
Pre-build architecture review
Two to four week engagement before development begins, reviewing the product requirements, the proposed architecture, the technology stack, and the data model with a documented recommendation and the specific decisions that need to be made before the first sprint starts.
Technical due diligence preparation
Assessment of the codebase, architecture, security posture, and operational practices against the criteria that Series A and growth-stage investors apply during technical due diligence — with a prioritised remediation plan and enough lead time to address the critical items before the process begins.
Technology stack selection
Vendor-neutral technology stack recommendation based on the product requirements, the team's existing skills, the hiring market for the technologies considered, and the long-term maintenance implications, not based on what the consulting team happens to be most familiar with.
Scaling and performance assessment
Load testing, database query analysis, infrastructure cost modelling, and the bottleneck identification that tells a growth-stage startup what will break first when the user volume increases by ten times before that volume arrives.
B2B SaaS products and platform businesses for startups building software for other businesses with the multi-tenant architecture, subscription billing infrastructure, customer onboarding workflows, and the product usage analytics that B2B SaaS companies need to manage churn, demonstrate value, and grow through expansion revenue. The architecture decisions that determine whether a B2B SaaS product can support enterprise customers, pass security reviews, and scale to a thousand tenants without re-engineering the data isolation model are made in the first six months of development and they are almost always made correctly or incorrectly based on whether the development team has built B2B SaaS before.
Multi-tenant architecture from day one
Data isolation model design, tenant-level configuration, and the access control architecture that supports both small business customers and enterprise accounts on the same platform — without a separate enterprise codebase that doubles the maintenance burden.
Subscription billing and payment infrastructure
Stripe integration with subscription tier management, usage-based billing logic, trial and freemium mechanics, failed payment recovery, and the billing portal that lets customers manage their subscription without contacting support.
Customer onboarding and time-to-value engineering
Onboarding flow design, empty state UX, guided setup workflows, and the activation metric instrumentation that tells the growth team which users are reaching the value moment and which are churning before they get there.
Product analytics and usage intelligence
Event tracking architecture, feature usage analytics, cohort analysis infrastructure, and the customer health scoring that gives customer success teams early warning on accounts at churn risk before they submit a cancellation request.
We Build for Founders at Every Stage
Startups are not a single buyer type, they are a spectrum from a founder with a napkin sketch and a Stripe account all the way to a Series B company with 50 engineers and a VP of Engineering. The software needs, the engagement model, and the right development partner are different at every point on that spectrum. We have worked across all of it.
Pre-Seed and Idea Stage Founders
Founders who have a validated idea, a clear user problem, and the need to build the first version of the product as quickly and cheaply as possible without creating the architecture problems that will slow the next stage down. At this stage we focus on thesis-first scope definition, the fastest path to a real user in the product, and the architecture decisions that need to be made correctly from the start even when everything else is done at speed.
Seed-Stage Startups With Early Traction
Companies that have validated the core thesis, have real users, and need to scale the product past early adopter tolerance, improving performance, adding the features the first cohort asked for, building the onboarding flows that convert new signups without founder hand-holding, and addressing the technical debt from the MVP that is starting to slow down feature development.
Series A Startups Scaling Engineering
Companies raising or post-Series A that need to scale the engineering function, address the architecture limitations that are becoming visible under growth load, prepare the technical foundation for enterprise sales cycles, and build the operational discipline — monitoring, alerting, deployment pipelines, on-call processes, that the product needs now that uptime is a commercial obligation rather than a best-effort target.
Non-Technical Founders
Founders without a technical co-founder who need a development partner that can serve as the technical counterpart not just executing on a brief but challenging the requirements, making architecture recommendations, explaining trade-offs in plain language, and functioning as the technical leadership the company needs until the first engineering hire is ready to take over. We have worked with non-technical founders across every industry and we know how to make the development process navigable without a CS degree.
Founders Rebuilding After a Failed First Build
Startups that hired the wrong agency, built with a freelancer who disappeared, or made architecture decisions at speed that cannot carry the next phase and need a development partner who can honestly assess what is salvageable, what needs to change, and how to get back on a productive development trajectory without losing another six months. We do not pretend this is unusual. We have taken on a number of these engagements and they are often the most clear-eyed and productive we run.
Bootstrapped Founders Managing Capital Efficiency
Founders without institutional funding who need to build a real product on the budget that revenue and savings allow with the scope discipline, the architecture efficiency, and the delivery model that makes every dollar of development spend move the company closer to the revenue that funds the next phase. Capital efficiency in software development is a skill, and not every development partner has it.
Why Founders Choose Solvios as Their Startup Software Development Company
Explore how we help startups build software that validates their thesis, attracts users, and scales without the rebuild that catches most fast-growing companies at the worst possible moment.
Building an AI-Powered Online Store for Egyptian Sacred Oils Brand
A seller of Egyptian sacred oils wanted a Shopify store that went beyond a standard product catalog educating visitors about origins and use cases, guiding purchase decisions and reducing pre-sale drop-off. We redesigned the storefront with story-led product pages, a custom AI-powered conversational assistant with NLP-engineered responses and a structured product knowledge base, and a clean brand-aligned UI/UX delivering a 30% increase in orders within the first few weeks and resolving 90% of customer queries without any human intervention.
Building a Startup? Talk to a Team That Will Push Back on Your Scope Before They Agree to Your Timeline.
We will map the core thesis your MVP needs to test, identify the architecture decisions that matter now versus later, and give you an honest build plan, before any code is written. Response within 24 hours.
What We Bring to Every Startup Engagement
These are not features, they are the capabilities that determine whether a startup software engagement produces a product that validates the thesis, scales with the company, and holds up under investor scrutiny.
Thesis-First Scope Discipline
Every sprint plan starts with the question the product is designed to answer, not the feature list. We help founders distinguish between what the MVP must do to validate the core assumption and what can wait because scope discipline in the first build is the most important capital efficiency decision a startup makes.
Architecture for Iteration Speed
Clean separation of concerns, API-first design, modular feature boundaries, and the code organization that makes a two-engineer team move at the same velocity six months into the build as they did in week one — rather than slowing down as the codebase grows and every change requires understanding a tangled dependency chain.
AI-Native Product Architecture
Data models, service boundaries, and API designs built with AI integration as a first-class consideration — so that when the AI feature is ready to build, the data it depends on is already structured correctly rather than requiring a migration from a schema that was not designed for it.
Mobile-First Engineering
Cross-platform delivery in React Native and Flutter, native Swift and Kotlin where the use case requires it, and the mobile-specific engineering depth — background processing, push notification infrastructure, in-app purchase mechanics, App Store optimisation — that consumer and B2B mobile products depend on.
Investor-Ready Technical Foundation
Clean, documented, version-controlled codebase, basic security posture, infrastructure-as-code, and the deployment hygiene that survives technical due diligence — because the quality of what a technical reviewer finds during a Series A process shapes the terms of the round.
Transparent, Founder-Friendly Delivery
Weekly progress reports in plain language, honest status on what is blocked and why, direct access to the engineers building the product, and the communication cadence that keeps a founder informed without requiring them to manage a development team on top of everything else they are managing.
Startup-Calibrated Commercial Model
Engagement structures that reflect startup economics — not enterprise contract terms applied to a seed-stage company. Milestone-based billing, scope flexibility as the requirements evolve, and the ability to scale the team up or down as the fundraising environment and product priorities change.
Growth Engineering After Launch
Analytics instrumentation, A/B testing infrastructure, feature flag deployment, performance optimisation, and the post-launch engineering work that separates a product that grows from a product that launches and plateaus. Most startup value is created after the first release, not before it.
AI-Integrated Startup Software Development, Built In from the First Sprint
The startup landscape has shifted: AI is no longer a differentiator that early adopters appreciate, it is a baseline expectation that investors and users bring to every new product. The startups that win in AI-adjacent categories are not the ones who added a ChatGPT integration to an existing product. They are the ones who designed the product around an AI capability from the beginning, where the data architecture, the user experience, and the business model are all built to take advantage of what AI can do rather than retrofitting AI onto a product that was designed without it. Every startup product we build is architected with AI integration as a first-class consideration from the first sprint.
01
AI features that change the user experience, not just the pitch deck
OpenAI, Anthropic Claude, Google Gemini, and open-source LLM integration with the production engineering that generative AI features require — token cost monitoring so the AI feature does not make the unit economics unsustainable, prompt versioning so the output quality does not regress between releases, output validation so the AI does not say something that creates a support ticket or a legal problem, and fallback routing so the feature degrades gracefully when the model API is unavailable. We have seen too many AI startups launch with an impressive demo and a production incident waiting to happen.
Highlights:
- Production-grade LLM API integration
- Token cost monitoring and optimisation
- Prompt versioning and regression testing
- Fallback routing and graceful degradation
01
AI features that change the user experience, not just the pitch deck
OpenAI, Anthropic Claude, Google Gemini, and open-source LLM integration with the production engineering that generative AI features require — token cost monitoring so the AI feature does not make the unit economics unsustainable, prompt versioning so the output quality does not regress between releases, output validation so the AI does not say something that creates a support ticket or a legal problem, and fallback routing so the feature degrades gracefully when the model API is unavailable. We have seen too many AI startups launch with an impressive demo and a production incident waiting to happen.
Highlights:
- Production-grade LLM API integration
- Token cost monitoring and optimisation
- Prompt versioning and regression testing
- Fallback routing and graceful degradation
Our Software Development Process
Startup software development is not slower enterprise development. It is a different discipline — one where the requirements are uncertain, the runway is finite, and the process needs to produce both working software and validated learning simultaneously. Our startup development process is designed to move fast on the right things and slow down only on the decisions that compound into the most expensive problems if they are made incorrectly.
Here's exactly how it works.
01
Discovery & Thesis Mapping
We define what the product is trying to learn from its first users, what the MVP needs to do to generate that learning, and what architecture decisions need to be made correctly from the start. Output is a scoped product brief, a technical architecture recommendation, and a sprint plan that gets working software in front of real users as fast as possible without sacrificing the decisions that compound.
02
Architecture & Design
Technical architecture finalized with the iteration speed and AI integration requirements of the product in mind. UI/UX design built for the activation and retention goals of the product — not just for visual appeal. Founder review and sign-off before development begins, because the cost of changing the architecture after the first sprint is an order of magnitude higher than changing it before.
03
Agile Development Sprints
Two-week sprints with working software delivered at the end of each cycle. The core user flow that validates the thesis ships first, not the admin panel, not the settings page, not the feature the co-founder added to the spec last Tuesday. Subsequent sprints layer on the features that the early user feedback justifies building.
04
QA & Launch Preparation
Functional testing across target devices and browsers, performance benchmarking against the load the launch is expected to generate, security review, App Store and Play Store pre-submission checks where applicable, and the analytics instrumentation that gives the founder day-one visibility into user behaviour.
05
Launch & Early User Feedback
Production deployment, launch monitoring, crash triage, and the rapid iteration cycle that responds to what the first cohort of real users actually does versus what the founder expected them to do. The first two weeks of real user data are the most valuable weeks in a startup product's lifecycle — and the process needs to be set up to act on that data quickly.
06
Iteration and Growth Engineering
Feature development based on validated user feedback, performance optimisation as user volume grows, technical debt reduction on a measured timeline, growth engineering — analytics, A/B testing, onboarding optimisation — and the ongoing product evolution that turns a validated MVP into a product that scales with the company.
Flexible Engagement Models Built for Startup Economics
Startup software engagements cannot be structured like enterprise contracts. The scope changes as users give feedback. The timeline compresses when a funding round closes or expands when it does not. The team size needs to flex with the product priorities. We structure startup engagements around the realities of startup economics rather than around what works for the vendor.
You need engineers who know your product, your users, and your roadmap as well as your co-founders and building for your thesis, not splitting attention across five other clients. The Dedicated Team model gives you a fully embedded product development unit that thinks about the business, not just the tickets.
- Right for you if
You have secured funding, your product thesis is validated, and you need to scale development faster than you can hire or you need a full-stack product team from the beginning because you do not have a technical co-founder and the product is the company.
- What you get
Hand-picked engineers, a QA specialist, and a technical lead working exclusively on your product. Sprint planning, standups, and demos run on your calendar. AI integration, mobile development, and growth engineering are all handled in-house. The team thinks about the product, not just the backlog.
- Economics
Monthly retainer. No surprise invoices. Team composition flexes up when you close a funding round and down when the roadmap is between major phases. Minimum team size is two engineers.
Typical profile
2–8 engineers
3-month minimum
Scales with 30-day notice
Not sure which model fits your startup?
Most founders start with Fixed Cost for the MVP and move to Time and Material as the product evolves based on real user data. Let's figure out the right starting point together.
Why Startups Choose Solvios
There are hundreds of development shops that will build your startup's MVP. Most of them will do it on time, bill you correctly, and hand over a codebase that works but that you will spend the next twelve months explaining to the engineers you hire after them. Here is what makes the difference in practice.
01
We Push Back Before We Build
The most valuable thing a startup development partner can do is challenge the scope before agreeing to the timeline. We ask why a feature is in the MVP before we estimate how long it will take to build. We flag when a requirement implies an architecture decision that will be expensive to change later. We tell founders when the v1 they are describing will take six months when they have four months of runway. That conversation is uncomfortable and it is the most important one we have.
02
Architecture Decisions That Compound in the Right Direction
We make the architecture decisions that matter: data model design, API contract design, service boundaries, authentication architecture carefully and explicitly, because these are the decisions that compound either in your favour or against you as the product grows. Everything else we do at startup speed. The thirty minutes we spend on the data model before the first sprint saves thirty days of migration work after the Series A.
03
AI-Integrated from the First Sprint
AI is not a feature we add to startup products when a founder asks for it. It is an architectural consideration we address in the discovery phase because the data models, service boundaries, and API designs that support AI features need to be built correctly from the start. We have built AI-integrated products across multiple verticals and we know where AI creates genuine user value versus where it creates impressive demos that real users do not actually use.
04
We Have Built Products Across Every Startup Archetype
Two-sided marketplaces, fintech products, on-demand services, B2B SaaS, consumer mobile apps, developer tools, AI-native products, the case studies on this page represent real products built for founders with real theses. We bring that cross-vertical experience into every startup engagement because startup problems recur across verticals: the marketplace chicken-and-egg problem, the SaaS onboarding activation gap, the AI cost curve that makes the unit economics unsustainable at scale.
05
Transparent, Honest, Available
Direct access to the engineers building your product. Weekly progress reports in plain language that a non-technical founder can act on. Honest status on what is blocked, what will slip, and what needs a decision from you before the sprint ends. We do not manage founders with optimistic status updates that turn into surprises at launch. The goal is for you to know as much about your product's development as we do.
06
US-Based Communication, Startup-Calibrated Cost
Project management and client communication on US business hours. The commercial model reflects the economic reality of building a startup — not enterprise contract terms applied to a seed-stage company. We have worked with founders who are counting every dollar and founders who have just closed a Series A, and the engagement model adapts to where you are in the capital cycle.
Frequently Asked Questions
Honest answers to the questions every founder asks before choosing a startup software development company. If something is not covered here, our team will walk you through it on a discovery call, no sales pitch, no fluff.
focused MVP with core user flows, basic backend, and a web or mobile interface typically starts at $20,000–$60,000 depending on complexity. A mid-complexity product with multiple user roles, third-party integrations, and a mobile app ranges from $60,000 to $150,000. A full-featured SaaS or marketplace product with AI features, complex data architecture, and multiple platforms is typically $150,000–$400,000. For bootstrapped founders, we can work within tighter budgets by scoping the MVP rigorously around the specific thesis the first version needs to validate. We provide detailed estimates after a discovery session. We will not give you a number we cannot stand behind.
tightly scoped MVP with a single core user flow, basic backend, and web interface can be delivered in 6–10 weeks. A standard MVP covering the main user journey, third-party integrations, and a mobile app takes 10–16 weeks. A complex MVP with AI features, multiple user roles, and a marketplace architecture takes 16–24 weeks. The most important variable is scope discipline, every feature added to the MVP brief adds time, and most of what gets added to a startup MVP spec in the weeks before development starts is not in the original MVP for a good reason.
It depends on the product, the founder's existing technical context, and the hiring market for the technology. For most web MVPs: React or Next.js on the frontend, Node.js or Python on the backend, PostgreSQL or MongoDB for the database, AWS or Vercel for hosting. For mobile: React Native or Flutter for cross-platform, Swift or Kotlin for native when the use case demands it. For AI-integrated products: OpenAI or Anthropic APIs for LLM features, Python for ML workloads, LangChain for agent workflows where applicable. We make technology recommendations based on the product, not based on what our team happens to be most comfortable with and we explain the trade-offs honestly.
For most early-stage startups, a development agency is faster to start, lower risk on a per-dollar basis, and more flexible as the product requirements change than an in-house team hired before the thesis is validated. The calculus changes after product-market fit when you have enough clarity to hire senior engineers for roles you can define precisely and enough stability to justify the hiring overhead. Most founders who work with Solvios start as agency clients and transition to a hybrid model keeping Solvios for specific workstreams while building their in-house team around the product areas that are most certain.
A good startup development partner pushes back on scope before agreeing to timelines, asks why a feature is in the MVP before estimating how long it takes to build, makes architecture decisions explicitly rather than implicitly, communicates honestly when something is blocked or will slip, and thinks about the business problem rather than just the technical brief. The most important question to ask a prospective development partner is not about their tech stack or their process. It is: tell me about a time you told a founder their MVP spec was wrong. If they cannot answer that question with a real story, they are not the right partner for a startup.
Pivots are expected in startup product development, the process should be designed for them, not surprised by them. On the commercial side, Time and Material engagements handle pivots naturally because the billing is against actual work rather than a fixed scope. On the technical side, we design MVPs with modular service boundaries and clean API contracts that make pivots cheaper than they would be in a tightly coupled architecture. We have managed through product pivots on multiple engagements and the difference in pivot cost between a well-architected MVP and a poorly architected one is typically measured in months, not days.
Yes. AI-integrated product development is one of our specific capabilities for startups. We integrate OpenAI, Anthropic Claude, Google Gemini, and open-source models with the production engineering that AI features require in a real product cost monitoring, prompt versioning, output validation, fallback routing, and the evaluation infrastructure that measures whether the AI is actually doing what the product claims. We also help founders assess honestly where AI creates genuine user value in the product versus where it creates an impressive demo that real users do not actually use because these are not always the same thing.
Three models: Dedicated Startup Team for funded startups that need a full product engineering unit working exclusively on their product; Time and Material for iterative development where the product direction is still evolving based on user feedback which is most startups, most of the time; and Fixed Cost for well-scoped MVP builds where the user flows and technical requirements are clearly documented before development begins. We recommend the right model based on where you are in the fundraising cycle and how much certainty you have in the product scope.
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