Software and App Development Feasibility Studies

The Role of Validation in the Software Ecosystem

Software and App Development Feasibility Studies Back in 2015, on a unrelated consulting engagement for Borger Group (Alberta’s largest underground civil engineering contractor) we saw the opportunity for a software and mobile app based solution that would revolutionize the heavy civil construction industry. Today, the solution can be found at Vizzn.ca. Vizzn currently provides 15 software automation solutions to common construction bottlenecks and areas of cost overruns. In short, Vizzn is a civil construction project management software set of tools. The modern digital economy is characterized by a paradox of accessibility and attrition. The barriers to entry for starting a software company have never been lower, driven by the ubiquity of cloud computing, open-source frameworks, and no-code tools. Yet, the probability of long-term success remains statistically negligible. Industry data consistently indicates that approximately 90% of technology startups fail, with a staggering proportion of these failures occurring within the first two years of operation. This high mortality rate is rarely the result of a single catastrophic technical failure. Rather, it is the cumulative effect of unverified assumptions, misaligned market fit, and underestimated operational complexities—factors that are theoretically identifiable before a single line of code is written. The software feasibility study serves as the primary prophylactic against these systemic failures. It is a structured, investigative process designed to assess the viability of a proposed project from multiple critical dimensions—technical, economic, legal, operational, and scheduling. Unlike the business plan, which acts as a strategic roadmap for execution, the feasibility study serves an antecedent function: it asks the fundamental question of whether the journey should be undertaken at all. It is an exercise in risk management, designed to transform uncertainty into calculated probability.

The Software Crisis and the Necessity of Feasibility

Historically, the software industry has been plagued by what is known as the “software crisis”—a term coined in the late 1960s to describe the difficulty of writing useful and efficient computer programs in the required time. Decades later, despite advances in methodologies like Agile and DevOps, the core issues remain: projects run over budget, miss deadlines, and fail to deliver value. Research from the Standish Group and others indicates that a significant percentage of IT projects are challenged or failed, often due to poor requirements definition—a direct output of inadequate feasibility analysis. In the context of a startup, where resources are finite and time-to-market is critical, the margin for error is non-existent. A feasibility study acts as a “reality check,” a filter that separates visionary innovation from practical delusion. It forces founders to confront the hard constraints of their environment, whether those are the processing limits of mobile devices, the stringent requirements of data privacy laws like GDPR, or the brutal economics of customer acquisition.

Defining the Feasibility Study in Software Contexts

A software feasibility study is a comprehensive evaluation conducted during the initial phase of the Software Development Life Cycle (SDLC), typically following the high-level specification of business requirements. Its objective is to determine whether a software project can be implemented within the constraints of technology, budget, time, and legal regulations while delivering the intended value to the organization. The study is inherently investigative. It does not seek to prove that a project will succeed, but rather to uncover the reasons it might fail. It involves deep research into the competitive landscape, technical proof-of-concepts (PoCs), financial modeling, and legal auditing. For a software startup, this involves probing beyond the surface-level appeal of an application idea to understand the underlying architectural requirements, the total cost of ownership (TCO), and specific legal hurdles.

Feasibility Study vs. Business Plan

A common source of confusion for early-stage founders is the distinction between a feasibility study and a business plan. While both are critical planning documents, they serve distinct phases and purposes in the venture creation lifecycle.
Feature Feasibility Study Business Plan
Primary Question Is this project viable? Should we proceed? How will we execute the approved project?
Focus Risk assessment, viability, “Go/No-Go” decision Growth strategy, marketing, revenue projections
Timing Conducted before development or commitment Conducted after the project is deemed feasible
Nature Investigative, analytical, skeptical Strategic, roadmap-oriented, persuasive
Audience Founders, internal stakeholders, seed investors Venture capitalists, partners, employees
Key Output Feasibility Report (Viable/Not Viable) Operational Plan, Marketing Plan, Financial Forecasts
Table 1: Comparative Analysis of Feasibility Studies and Business Plans. The feasibility study serves as the gatekeeper. Only once a project passes this filter does the business plan come into play to map out the execution. Bypassing the feasibility phase often leads to the creation of detailed business plans for products that are technically impossible to build within budget or legally impermissible to operate.

The Anatomy of Startup Failure: The Consequence of Unchecked Assumptions

To fully appreciate the necessity of feasibility studies, one must examine the pathology of failure in the software industry. The high attrition rate of startups is rarely due to external market crashes but rather an accumulation of internal misjudgments and unverified assumptions.

The Prevalence of “No Market Need”

The most prevalent cause of startup failure, accounting for approximately 42% of cases, is the lack of market need. This represents a catastrophic failure of Market Feasibility. In the software world, this often manifests as the “solution in search of a problem.” Engineers and developers, driven by technical curiosity, may build complex applications using cutting-edge technology (e.g., blockchain, AR) without validating that a user base exists willing to pay for the utility. For example, the high-profile failure of Quibi, a short-form video streaming service that raised nearly $1.75 billion, was largely attributed to a miscalculation of user behavior. The founders assumed that users wanted high-production-value content in 10-minute chunks for mobile consumption. A robust feasibility study—including pilot testing and competitive analysis against free alternatives like YouTube and TikTok—might have revealed that the “need” was illusory, saving billions in capital.

Financial Mismanagement and Unit Economics

The second most common reason for failure is running out of cash, cited by 29% of startups. This is a direct failure of Economic Feasibility. Startups frequently underestimate the “burn rate”—the rate at which they spend capital before generating positive cash flow. Feasibility studies require detailed financial modeling that includes not just development costs but customer acquisition costs (CAC), lifetime value (LTV), and the runway required to reach profitability. Without this rigorous analysis, startups engage in “premature scaling,” spending heavily on marketing for a product with negative unit economics—where the cost to acquire a customer exceeds the revenue that customer generates.

The “Ready, Fire, Aim” Fallacy and Technical Debt

Many entrepreneurs view feasibility studies as an impediment to speed. In the “move fast and break things” culture, there is a tendency to skip due diligence. However, this approach often leads to the accumulation of insurmountable technical debt. Technical debt refers to the implied cost of additional rework caused by choosing an easy or limited solution now instead of using a better approach that would take longer. When feasibility is skipped, fundamental architectural decisions (e.g., database selection, scalability limits) are made based on guesswork. As the user base grows, these flaws become apparent. The cost of fixing errors increases exponentially as a project progresses, a phenomenon known as the “Cost of Quality” or the “Rule of 100”.

The Cost of Correction Curve

Data from the IBM Systems Sciences Institute illustrates the financial imperative of early feasibility analysis:
  • Requirements/Design Phase: A defect or misconception caught here might cost $100 to fix.
  • Implementation (Coding) Phase: Fixing the same issue during coding costs approximately $1,000.
  • Integration Testing: The cost rises to $10,000.
  • Post-Release (Production): Fixing the bug after launch costs $100,000+ due to downtime, data migration, reputational damage, and emergency engineering rates.
The feasibility study is the mechanism for “shifting left”—identifying risks at the earliest possible stage where they are cheapest to mitigate. It keeps the project in the low-cost “Requirements Phase” until viability is assured.

The TELOS Framework: A Holistic Methodology for Assessment

To conduct a systematic feasibility study, industry experts rely on established frameworks that ensure no critical dimension is overlooked. The most widely recognized of these is the TELOS framework, an acronym standing for Technical, Economic, Legal, Operational, and Scheduling feasibility. For a software startup, each of these pillars represents a critical vector of risk that must be analyzed deeply.

Technical Feasibility: The Engineering Reality Check

Technical feasibility assesses whether the proposed software can be built given the current state of technology and the resources available to the organization. It asks: “Is this solution technologically possible, and do we have the capability to execute it?”

Technology Stack and Infrastructure Assessment

The selection of the technology stack—the combination of programming languages, frameworks, and databases—is a feasibility constraint, not just a technical detail.
  • Maturity of Technology: Does the project require nascent technology that is not yet commercially stable? For example, building an app entirely on a beta-version blockchain platform introduces the risk that the underlying platform might fail or change drastically.
  • Scalability Requirements: Can the chosen architecture handle the projected user load? A social media startup projecting millions of concurrent users faces fundamentally different feasibility constraints than a B2B internal tool. The feasibility study must validate that the proposed database (e.g., NoSQL vs. SQL) can handle the read/write throughput required at scale.
  • Integration Risks: How will the new software interact with legacy systems or third-party APIs? Integration challenges are a leading cause of project delays. If an app relies on a third-party API (e.g., OpenAI’s GPT-4 or Twitter’s Firehose) for core functionality, the feasibility study must assess the risk of that API changing its terms of service, pricing, or rate limits.

Hardware and Device Dependencies

For mobile app startups, technical feasibility includes a rigorous assessment of hardware limitations. Mobile devices, despite their power, have strict constraints regarding battery life, thermal management, and network connectivity.
  • Battery and Processing Power: Apps requiring real-time data processing or complex 3D rendering (e.g., high-fidelity AR/VR apps) may be technically feasible on high-end devices (iPhone Pro models) but unfeasible for the mass market due to battery drain or thermal throttling on mid-range devices. This limits the Total Addressable Market (TAM).
  • Sensors and Connectivity: Does the app require constant GPS access, Bluetooth Low Energy (BLE), or NFC? These requirements dictate the minimum OS versions and hardware specifications. A feasibility study must determine the fragmentation of the target market—if 40% of the target audience uses devices that lack the necessary sensors, the project may be technically feasible but commercially non-viable.

The Skills Gap Analysis

Technical feasibility is inextricably linked to human resources. A project requiring advanced Artificial Intelligence (AI) or Machine Learning (ML) capabilities is not feasible if the startup cannot afford or attract data scientists with that specific expertise. The study must perform a “skills gap analysis”—measuring the difference between the team’s current capabilities and the project’s requirements. If the gap is too wide and the budget for hiring specialist consultants is unavailable, the project is technically unfeasible. This is particularly relevant for startups attempting to build proprietary encryption or complex algorithmic trading platforms without senior domain experts.

Economic Feasibility: Viability and Return on Investment

Economic feasibility, often termed cost-benefit analysis, determines whether the financial returns of the project justify the investment. For a startup, this goes beyond simple profitability to include cash flow analysis, funding viability, and long-term financial sustainability.

Total Cost of Ownership (TCO)

Startups often miscalculate by focusing solely on development costs (the cost to build version 1.0). A comprehensive economic feasibility study estimates the TCO, which includes:
  • Development Costs: Salaries for developers, designers, and project managers; costs for software licenses and hardware.
  • Operational Costs: Cloud hosting fees (AWS/Azure bills can scale unexpectedly with usage), API usage fees, domain maintenance, and third-party service subscriptions (e.g., Stripe for payments, Twilio for SMS).
  • Marketing and Sales: Customer Acquisition Cost (CAC) is often the largest expense for B2C apps. The feasibility study must estimate the marketing spend required to reach the critical mass of users.
  • Contingency: A financial buffer (typically 15-20%) for the inevitable “unknown unknowns”.

ROI and Break-Even Analysis

The study must project revenue streams to calculate the Return on Investment (ROI) and the break-even point.
  • Revenue Models: The feasibility of the business depends on the revenue model—Subscription (SaaS), Freemium, Ad-supported, or Transaction fees. For example, an ad-supported model is often economically unfeasible for niche apps because the user base is too small to generate significant impressions to cover development costs.
  • Break-Even Point: This calculation determines how many active users or subscribers are needed to cover fixed and variable costs. If the break-even point requires a market share that exceeds the Total Addressable Market (TAM), or requires an unrealistic conversion rate, the project is economically unfeasible.

Economic vs. Financial Feasibility

It is crucial to distinguish between economic and financial feasibility, although they overlap in the startup context.
  • Financial Feasibility: Focuses on the investor’s perspective—profitability, cash flow, and commercial ROI. This is the primary concern for VCs and founders.
  • Economic Feasibility: Often broader, assessing the project’s impact on the wider economy or society. This is critical for government grants or social enterprises. For example, a startup building software to optimize local supply chains might demonstrate economic feasibility by showing how it reduces waste and increases local employment, which can be leveraged to secure non-dilutive government funding.

Legal Feasibility: 

Legal feasibility assesses whether the project conflicts with legal requirements, including data privacy laws, intellectual property rights, and industry-specific regulations. In the digital age, where data is the new oil, this is one of the most volatile areas of risk.

Data Privacy and GDPR Compliance

For any software handling personal data, compliance with regulations like the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and others is mandatory.
  • Privacy by Design: Legal feasibility requires that privacy mechanisms (e.g., data encryption, user consent management, right to be forgotten, data portability) be baked into the architecture from day one. Retrofitting these features is often prohibitively expensive.
  • Non-Compliance Costs: Fines for GDPR violations can reach €20 million or 4% of global revenue. A feasibility study must determine if the startup has the resources to implement the necessary compliance infrastructure. If the core business model relies on selling user data in a way that violates these laws, the project is legally unfeasible.

Intellectual Property (IP) and Patents

The study must examine whether the proposed software infringes on existing patents. Conversely, it should assess the ability of the startup to protect its own IP.
  • Freedom to Operate: A “Freedom to Operate” search helps ensure the startup isn’t unknowingly replicating a patented algorithm or business process.
  • Open Source Licensing: Many startups build on open-source libraries. Legal feasibility involves checking license compatibility. For instance, using a GPL-licensed library in a proprietary product may legally force the startup to open-source its entire code, destroying its proprietary business model. This “viral” nature of copyleft licenses is a critical check.

App Store Guidelines and Platform Risk

For mobile apps, “legal” feasibility also extends to the private laws (Terms of Service) of Apple’s App Store and Google Play.
  • Review Guidelines: Apps can be rejected for “objectionable content,” “spam,” or simply having limited utility. A feasibility study must verify that the app’s core functionality does not violate these guidelines. For example, apps that mine cryptocurrency on the device are banned by Apple, rendering such a business model unfeasible on iOS.
  • In-App Purchase (IAP) Rules: Trying to bypass the 30% commission fee is a common reason for rejection. The business model must account for this fee to be feasible. If the margin is too thin to absorb the 30% cut, the project is economically unfeasible on mobile platforms.

Operational Feasibility: The Human Element

Operational feasibility focuses on the “how” of implementation and usage. It assesses whether the organization can support the product and whether users will actually adopt it.

Organizational Capability and Structure

Even if a project is technically possible and profitable, it may fail if the organization is dysfunctional or ill-equipped to manage it.
  • Team Dynamics: Do the founders have complementary skills? Is there a clear decision-making hierarchy? Dysfunctional leadership and founder conflict are known causes of startup failure. Operational feasibility asks if the human infrastructure exists to execute the vision.
  • Resource Allocation: Can the startup support this project without cannibalizing other critical operations? For a small team, launching two major products simultaneously is often operationally unfeasible due to lack of focus and bandwidth.

User Adoption and Change Management

For B2B software, operational feasibility asks if the target customers’ employees will actually use the software.
  • Process Fit: Does the software align with existing workflows, or does it require a radical change in behavior? Solutions that require massive behavioral shifts often face high resistance and low adoption, rendering them operationally unfeasible.
  • Support and Maintenance: Operational feasibility includes the capacity to provide customer support. If a startup sells a mission-critical tool to enterprise clients but cannot offer 24/7 support (due to small team size), the operational model is flawed.

Scheduling Feasibility: The Time Factor

Scheduling feasibility estimates how much time the project will take and whether deadlines are realistic.

The Critical Path and Time-to-Market

In the software world, timing is everything.
  • Window of Opportunity: If a competitor is launching a similar product in six months, a twelve-month development timeline may render the project unfeasible. The study must align the development schedule with market windows.
  • Estimation Optimism: Software projects are notorious for overrunning estimates. A rigorous schedule feasibility study uses techniques like PERT (Program Evaluation and Review Technique) or Monte Carlo simulations to account for uncertainty. If the project relies on a hard deadline (e.g., a Black Friday launch) and the critical path has no buffer, it is likely unfeasible.

Resource Availability

Scheduling is inextricably linked to resources. The study must verify that key personnel (e.g., the lead backend engineer) are available for the duration of the project. If the only person capable of building the core algorithm is booked on another project for three months, the schedule is unfeasible.

Alternative Frameworks: PIECES and SWOT

While TELOS is the dominant framework, others provide complementary perspectives, particularly for analyzing existing systems or internal startup initiatives.

The PIECES Framework

Developed by James Wetherbe, PIECES is useful for identifying problems in an existing context that the new software aims to solve. It categorizes feasibility through the lens of:
  • Performance: Does the current system lack throughput or response time?
  • Information: Is data inaccurate, untimely, or irrelevant?
  • Economics: Are costs too high or revenues too low?
  • Control: Is there a lack of security or auditability?
  • Efficiency: Are resources wasted?
  • Service: Is the system difficult to use? Using PIECES helps validate the Operational Feasibility by ensuring the new software actually solves a defined problem.

SWOT Analysis

A Feasibility Study often incorporates a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats).
  • Internal Factors (Strengths/Weaknesses): Team skills, proprietary tech, funding status.
  • External Factors (Opportunities/Threats): Market trends, competitor moves, regulatory changes. SWOT helps in synthesizing the findings of the TELOS analysis into a strategic narrative.

Market Feasibility: The Validation of Demand

While often subsumed under Economic Feasibility in general project management, Market Feasibility deserves distinct attention in software startups due to the high rate of “market-need” failures. It answers the existential question: “Does anyone actually want this?”.

Assessing Demand and Competition

A technical feat is not a product. Market feasibility involves:
  • Target Audience Analysis: Creating detailed user personas and segmenting the market. Who is the “ideal customer profile” (ICP)?
  • Competitor Analysis: Identifying direct and indirect competitors. If a market is saturated with free alternatives (e.g., a simple to-do list app), a paid version may be unfeasible unless it offers a significant, validated differentiator. The study should map competitors on a matrix of Price vs. Value.
  • Trend Analysis: Is the market growing, stable, or declining? Building a tool for a dying technology (e.g., Flash-based games) is unfeasible regardless of technical ease.

Validation Methods: Moving Beyond Guesswork

Market feasibility relies on data, not intuition.
  • Surveys and Interviews: Gathering qualitative data from potential users to validate pain points.
  • Prototyping and MVPs: Creating a “smoke test” (landing page) or a low-fidelity prototype to gauge interest before full development. High bounce rates or low signup numbers at this stage are early indicators of poor market feasibility. This iterative validation is central to the “Lean Startup” methodology.

Detailed Technical Feasibility: Mobile and AI Specifics

The rapid evolution of technology has increased the complexity of technical feasibility studies. Modern startups are rarely building simple CRUD (Create, Read, Update, Delete) applications; they are often integrating Artificial Intelligence (AI), utilizing blockchain, or relying on complex mobile sensor networks.

Mobile App Feasibility Challenges

For mobile startups, technical feasibility checklists must be granular due to the constraints of the mobile ecosystem.
  • Device Fragmentation: Android fragmentation (thousands of device variations with different screen sizes, CPUs, and RAM) poses a massive feasibility challenge. A study must determine if the startup has the resources to test and support this variety. If not, can the business survive by targeting only the top 20% of devices?.
  • Cross-Platform vs. Native Development: The choice between Native (Swift/Kotlin) and Cross-Platform (Flutter/React Native) is a critical feasibility decision.
  • Native: Offers best performance and access to latest APIs but requires two separate codebases and teams ($$$$).
  • Cross-Platform: Cheaper and faster ($$), but may suffer from performance issues or delayed access to new hardware features. The feasibility study quantifies this trade-off based on the app’s specific needs (e.g., a high-performance game vs. a data-entry app).
Feature Native Development Cross-Platform (Flutter/React Native) Feasibility Implication
Performance High Medium-High Critical for games/AR; less so for business apps.
Development Cost $$$$(2 Teams) $$ (1 Team) Economic feasibility constraint.
Access to Hardware Full/Immediate Delayed/Wrapper Dependent Technical feasibility constraint for sensor-heavy apps.
Maintenance High Effort Medium Effort Operational feasibility constraint.
Table 2: Technical Feasibility Trade-offs in Mobile Development.

AI and Machine Learning Feasibility

For AI startups, data is the primary feasibility constraint, often overshadowing code.
  • Data Availability: Does the startup have access to the dataset required to train the model? “We will find the data later” is a common cause of failure. If the data doesn’t exist, is proprietary to a competitor, or is prohibitively expensive to license, the project is technically unfeasible.
  • Data Quality and Labeling: Even if data exists, is it labeled? The cost of manual data labeling can be astronomical. Feasibility studies must account for the “human-in-the-loop” costs.
  • Computational Cost: Training large models (LLMs) requires significant GPU resources. The economic feasibility of the AI technical stack must be scrutinized—cloud GPU costs can burn through seed funding in months. The study should analyze the trade-off between training a proprietary model vs. using a pre-trained API.

Economic Modeling: Startup Specifics

Economic feasibility for startups is unique because it often involves operating at a loss for a significant period. The analysis must focus on unit economics and funding dynamics.

Burn Rate and Runway

  • Burn Rate: The net negative cash flow per month.
  • Runway: Cash in Bank divided by Burn Rate. A feasibility study must show a runway that allows the startup to reach the next “fundable milestone” (e.g., product launch, 10,000 active users). If the TCO analysis shows a burn rate that yields only a 3-month runway with projected seed funding, the project is unfeasible without securing more capital or radically cutting scope.

Unit Economics: The Golden Rule

  • CAC < LTV: The Customer Acquisition Cost must be significantly lower than the Lifetime Value of the customer.
  • If the feasibility study shows that acquiring a customer (via Facebook ads, sales team commissions) costs $50, but the customer only generates $40 in lifetime profit, the business model is fundamentally broken. This is a common finding in on-demand service startups (e.g., food delivery) where operational costs are high. The study allows founders to model these variables—”What if we reduce churn by 5%? What if ad costs rise by 20%?”—to test the resilience of the model.

The Feasibility Report: Structure and Decision Making

The culmination of this process is the Feasibility Study Report (FSR). This document synthesizes findings into a recommendation. It is the artifact that communicates the “truth” of the project to stakeholders.

Report Structure

A professional FSR typically follows this structure:
  1. Executive Summary: A high-level overview of the findings and the final recommendation (Go/No-Go). This is often the only part investors read in detail.
  2. Project Scope: Detailed definition of what is being analyzed, including the boundaries of the system.
  3. Market Analysis: Data on industry trends, customer segmentation, competition, and demand validation.
  4. Technical Analysis: Assessment of architecture, hardware requirements, technology stack, and skills gap.
  5. Financial Analysis: TCO modeling, ROI projections, break-even analysis, and sensitivity analysis.
  6. Organizational/Operational Analysis: Evaluation of team structure, change management, and support capabilities.
  7. Legal/Regulatory Analysis: Compliance audit (GDPR, IP, etc.) and risk assessment.
  8. Risk Analysis Matrix: A table of potential risks (e.g., “Main API shuts down”) with probability, impact, and mitigation strategies.
  9. Recommendation: A clear, data-driven conclusion.

The “Go/No-Go” Decision

The output of the report is not always binary.
  • Go: The project is viable; proceed to the business plan and development.
  • No-Go: The project is fatally flawed (e.g., no market need, legal impossibility). This is a success, as it saves capital and time.
  • Pivot/Modify: The project as defined is not feasible, but could be if specific variables changed (e.g., switching to a different tech stack, targeting a different market segment). This is the most common outcome, driving the iterative nature of startup strategy.

The Foundation of Sustainable Innovation

The romanticized image of the software startup involves a garage, a brilliant idea, and sheer willpower. In reality, the most successful software companies—from Microsoft to Google—are built on a foundation of rigorous analysis and calculated risk-taking. The feasibility study is the architectural blueprint of this foundation. By systematically interrogating the Technical, Economic, Legal, Operational, and Scheduling (TELOS) aspects of a project, the feasibility study transforms vague concepts into viable strategies. It serves as a financial firewall, preventing the allocation of resources to doomed initiatives, and acts as a strategic compass, guiding founders toward the most efficient path to market. In an industry where 90% of ventures fail—often due to avoidable errors in market fit and financial planning—the feasibility study is not an optional administrative step. It is a vital survival mechanism. For the software entrepreneur, the cost of a feasibility study is an investment in certainty; the cost of ignoring it is often the company itself.

Appendix: Detailed Checklists for Feasibility Analysis

To assist practitioners, the following checklists synthesize the core requirements for a robust software feasibility study.

A. Technical Feasibility Checklist

  • [ ] Core Functionality: Are the “must-have” features technically possible with current technology? 
  • [ ] Skills Gap: Does the team possess the necessary languages/frameworks expertise (e.g., Python, Swift, React)? 
  • [ ] Scalability: Can the architecture handle 10x, 100x, and 1000x user growth? 
  • [ ] Integration: Are all necessary third-party APIs available, documented, and stable? 
  • [ ] Security: Can the system meet required encryption and authentication standards (e.g., OAuth2, AES-256)? 
  • [ ] Legacy Compatibility: Will the new software work with existing company hardware/software? 
  • [ ] Mobile Constraints: Have battery life, data usage, and offline capabilities been assessed? 

B. Economic Feasibility Checklist

  • [ ] Development Cost: Accurate estimation of developer hours x hourly rate. 
  • [ ] Infrastructure Cost: Projections for cloud hosting (AWS/Azure), domains, and SSL. 
  • [ ] Software/Tooling Costs: Licenses for IDEs, JIRA, Slack, Adobe Suite, etc.
  • [ ] Marketing Budget: Estimate of CAC and total marketing spend required for launch.
  • [ ] Revenue Projection: Realistic forecast of sales/subscriptions based on market analysis.
  • [ ] Break-Even Analysis: Time required to recoup the initial investment.
  • [ ] Funding Availability: Do we have the cash (or access to investors) to cover the burn rate?

C. Legal & Compliance Checklist

  • [ ] Data Privacy: Compliance with GDPR, CCPA, HIPAA, etc.
  • [ ] Intellectual Property: Freedom to operate check (patent infringement).
  • [ ] Licensing: Are all open-source libraries compatible with the commercial model? 
  • [ ] Terms of Service: Are we violating the ToS of any platform we rely on (e.g., scraping LinkedIn)?
  • [ ] Accessibility: Does the app meet WCAG or ADA standards (often a legal requirement)? 

D. Operational Feasibility Checklist

  • [ ] Team Availability: Are the right people available at the right time? 
  • [ ] Organizational Culture: Will the organization accept/adopt the new software? 
  • [ ] Support Structure: Is there a team to handle customer queries and bugs post-launch?
  • [ ] Training Needs: Do employees/users need training to use the system?
  • [ ] Process Impact: Will this software disrupt critical existing workflows negatively?

E. Market Feasibility Checklist

  • [ ] Problem Validation: Is there a verified problem that this software solves?
  • [ ] Market Size: Is the TAM large enough to support the business goals?
  • [ ] Competitor Analysis: Who are the competitors, and what is our USP (Unique Selling Proposition)? 
  • [ ] Pricing Strategy: Is the market willing to pay the price required for profitability?
  • [ ] Trends: Is the market growing, stable, or declining? 

F. Scheduling Feasibility Checklist

  • [ ] Timeline Reality: Is the deadline based on work estimates or arbitrary external dates?
  • [ ] Critical Path: Have dependencies been identified (e.g., design must finish before frontend begins)? 
  • [ ] Buffer: Is there a contingency buffer (e.g., 20%) for inevitable delays? 
  • [ ] Milestones: Are there clear, measurable milestones to track progress? 

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