Frequently Asked Questions

Timeline depends on scope and complexity. A focused internal tool typically takes 8 to 12 weeks from kickoff to production. Larger platforms with multiple integrations and user roles can take 4 to 6 months. We deliver working software incrementally so you see progress every week, not just at the end.

Costs vary based on complexity, integrations, and team size. A targeted business tool might range from $50,000 to $150,000, while a full platform can exceed $300,000. We scope every project in detail before quoting so there are no surprises, and we structure engagements to deliver value at each milestone.

Custom software makes sense when your workflow does not fit neatly into existing products, when you need a competitive advantage through proprietary technology, or when you are spending significant time working around the limitations of tools you already have. If an off-the-shelf solution covers 90 percent of your needs, we will tell you that instead.

Start by looking for companies with a local presence and a portfolio of relevant projects. In Los Angeles, LC Global Consulting works directly with clients — no offshore handoffs, no account manager layers. Schedule a call to discuss your project and see if we're the right fit.

It depends on the problem. If off-the-shelf tools force you to change your workflow or you're paying for features you don't use, custom software pays for itself. We help small and mid-size businesses scope a right-sized solution — not an enterprise system they don't need.

A website primarily displays information, while a web application lets users interact with data, complete workflows, and perform tasks. Examples include dashboards, booking systems, and project management tools. If your users log in and do work inside it, that is a web application.

We choose the stack based on your project requirements, not personal preference. Common choices include React, Vue, or Svelte on the frontend, and Node.js, Python, or Go on the backend. We evaluate factors like team familiarity, performance needs, and long-term maintainability before recommending anything.

Yes. Progressive Web Apps use service workers and local storage to cache data and function without an internet connection. When connectivity returns, the app syncs automatically. This is especially useful for field workers, retail environments, or any scenario where internet access is unreliable.

Native apps deliver the best performance and full access to device features, but require separate codebases for iOS and Android. Cross-platform frameworks like React Native or Flutter let you share most of the code across both platforms, reducing cost and time to market. We recommend native when you need heavy graphics, complex animations, or deep hardware integration, and cross-platform for most business and consumer apps.

Apple typically reviews apps within 24 to 48 hours, though first-time submissions or apps in regulated categories can take longer. Google Play reviews usually complete within a few hours to a few days. We handle the submission process, address reviewer feedback, and ensure your app meets all platform guidelines before submitting.

A straightforward single-platform app typically costs $40,000 to $100,000. A cross-platform app with backend infrastructure, push notifications, and third-party integrations ranges from $80,000 to $250,000. The biggest cost drivers are the number of screens, complexity of the backend, and whether you need real-time features like chat or location tracking.

Multi-tenancy means a single instance of your application serves multiple customers, with each customer's data isolated from the others. It matters because it dramatically reduces infrastructure costs and simplifies deployments compared to running a separate instance for each customer. Getting the tenancy model wrong early is one of the most expensive architectural mistakes in SaaS.

We integrate with payment processors like Stripe or Paddle to handle recurring billing, usage-based metering, plan upgrades and downgrades, proration, and failed payment recovery. The billing logic is built into the application from the start so your team can manage plans and pricing without developer involvement.

A focused SaaS MVP with core functionality, authentication, billing, and a basic admin panel typically takes 3 to 4 months. A full-featured platform with advanced workflows, integrations, and reporting can take 6 to 12 months. We structure the build so you can start onboarding early customers while development continues on additional features.

A prototype is a visual or interactive mockup used to test an idea with stakeholders. It is not functional software. An MVP is a working product with real code, real data, and enough functionality for actual users to complete a core workflow. Prototypes validate the concept; MVPs validate the business.

Start with the one workflow that proves your value proposition to users. Everything else is scope for later. We work with you to identify the smallest set of features that lets real users complete that workflow end to end. If a feature does not directly support the core use case, it goes on the roadmap, not in the MVP.

Not if it is built correctly. We use production-grade architecture from day one, which means clean code, proper database design, and an infrastructure that can handle growth. When traction comes, you extend the existing codebase with new features instead of rewriting what is already working.

Shopify works well for straightforward online stores with standard product catalogs and checkout flows. Custom development makes sense when you have complex pricing rules, multi-vendor marketplace requirements, unique subscription models, or need deep integration with existing business systems. We evaluate your specific requirements and recommend the most cost-effective approach.

Headless commerce separates the frontend presentation from the backend commerce engine. This lets you build a completely custom shopping experience on any frontend technology while using a proven commerce backend for inventory, payments, and order management. It is a good fit when you need a highly customized user experience or want to sell through multiple channels like web, mobile, and in-store kiosks.

We integrate with PCI-compliant payment processors like Stripe, PayPal, or Adyen so your platform never directly handles raw credit card data. All transactions are encrypted in transit and at rest. We implement fraud detection, address verification, and 3D Secure authentication based on your risk tolerance and geographic requirements.

We use an incremental approach called the strangler fig pattern. New functionality is built alongside the existing system, and traffic is gradually redirected from old components to new ones. Your business keeps running on the current system throughout the migration, and you can stop or reprioritize at any point without losing progress.

A single high-priority module can be modernized in 2 to 3 months. A full system migration for a medium-sized application typically takes 6 to 18 months depending on complexity, data volume, and how many integrations need to be updated. We phase the work so each stage delivers usable improvements rather than requiring a big-bang cutover.

Full rewrites carry significant risk because you are rebuilding every feature while the old system continues to evolve. Incremental modernization lets you replace the most problematic components first while keeping everything else stable. We recommend full rewrites only when the existing codebase is truly unmaintainable and the business can tolerate the timeline and cost involved.

Any task that is repetitive, rule-based, and performed frequently is a strong candidate. Common examples include data entry between systems, invoice processing, report generation, employee onboarding checklists, and approval workflows. If your team follows the same steps the same way every time, a computer can likely do it faster and without errors.

ROI depends on the volume and cost of the manual work being replaced. Most businesses see payback within 3 to 6 months on their first automation. Beyond time savings, automation reduces error rates, improves compliance with auditable logs, and frees your team to focus on work that requires human judgment and creativity.

Automation handles the repetitive parts of jobs, not entire roles. In practice, it frees your team from tedious manual tasks so they can spend more time on higher-value work like customer relationships, strategic planning, and problem solving. Most companies that automate effectively redeploy their people rather than reduce headcount.

An API integration connects two software systems so they can exchange data automatically. Instead of your team manually copying information between your CRM, ERP, or e-commerce platform, an API integration keeps everything in sync in real time. This eliminates data entry errors, saves hours of manual work, and gives you a single source of truth across your business.

Yes. We build custom connectors for systems that only support file exports, database connections, or proprietary protocols. We have integrated with mainframe systems, legacy ERPs, and custom desktop applications that were never designed for modern connectivity. If data exists in a system, we can extract it and route it where it needs to go.

We design every integration with retry logic, dead letter queues, and error alerting. When a connected system goes down or an API changes, the integration queues the data and retries until the issue is resolved. Your team gets notified of failures that need attention, and no data is lost in the process.

The best choice depends on your existing ecosystem, team expertise, and specific workload requirements. AWS has the broadest service catalog, GCP excels at data and machine learning workloads, and Azure integrates tightly with Microsoft tools. In practice, most applications run well on any of the three. We help you evaluate based on cost, compliance needs, and the managed services that matter most for your use case.

Most companies we audit are overspending by 20 to 40 percent due to over-provisioned resources, unused instances, and suboptimal pricing plans. Common savings come from right-sizing compute instances, using reserved or spot capacity, implementing auto-scaling, and cleaning up orphaned storage and snapshots.

Infrastructure as code means defining your servers, databases, and networking in version-controlled configuration files instead of clicking through a cloud console. This makes your infrastructure reproducible, auditable, and testable. If a server breaks, you can recreate it in minutes from the same configuration. Tools like Terraform and Pulumi are the most common choices.

UI design focuses on the visual elements users interact with: buttons, typography, colors, and layout. UX design focuses on the overall experience: how easy it is to complete a task, whether the navigation makes sense, and how the product feels to use. Both disciplines work together. A beautiful interface that is confusing to navigate fails on UX; a logical workflow that looks unprofessional fails on UI.

A design system is a library of reusable components, patterns, and guidelines that ensure visual and functional consistency across your product. You need one if your product has more than a handful of screens or if multiple people work on the frontend. Without a design system, interfaces drift into inconsistency over time, which increases development cost and confuses users.

We test designs with real users through moderated usability sessions, where participants attempt to complete key tasks while we observe. This reveals navigation confusion, unclear labels, and missing functionality before any code is written. We typically run two rounds of testing: once on wireframes and once on high-fidelity designs.

A data warehouse stores structured, cleaned data optimized for querying and reporting. A data lake stores raw data in any format for later processing. Most businesses start with a data warehouse because it delivers answers faster. You only need a data lake if you have large volumes of unstructured data or plan to run machine learning workloads on raw data.

We work with tools like Looker, Metabase, Grafana, and custom-built dashboards depending on your needs. For teams that want self-service reporting, Looker and Metabase let non-technical users explore data on their own. For real-time operational monitoring, we build custom dashboards tailored to your specific metrics and alerting requirements.

A basic pipeline that extracts data from your primary systems, loads it into a warehouse, and powers a set of dashboards typically takes 4 to 8 weeks. More complex setups involving real-time streaming, multiple data sources, or data quality monitoring can take 2 to 4 months. We prioritize the dashboards your team needs most and build outward from there.