Web, Mobile, and AI Solutions: What We Build and How

Web applications for internal operations and customer portals

We build web applications that run in a browser and serve two main audiences: internal operations teams and external customers. Internal tools often replace spreadsheets and manual handoffs. Examples include an inventory reconciliation dashboard for a 12-person warehouse team or a claims intake system for a regional insurance office. Customer portals typically handle account management, order history, or appointment scheduling. A common project is an e-commerce checkout portal for a specialty retailer that processes 200 to 500 orders per day. We use React for the user interface, Node.js for the API layer, and PostgreSQL for transactional data. These choices are well supported by major cloud providers. For deployment, we often use AWS Lambda for event-driven tasks and Amazon RDS or Azure Database for PostgreSQL for relational data. The AWS Well-Architected Framework and Google Cloud Architecture Framework both emphasize operational excellence and security, and we align design reviews with those principles. A typical internal web application for a team of 10 to 20 users takes 8 to 12 weeks with a team of 3 to 4 people (1 front end, 1 back end, 1 designer part time, 1 project lead part time). A customer-facing portal with authentication, payments, and reporting usually takes 12 to 16 weeks with a team of 4 to 5. Primary risks include unclear requirements, integration with legacy databases, and scope creep. We mitigate these with a two-week discovery phase, a clickable prototype, and a fixed list of acceptance criteria. Costs for these projects typically range from $40,000 to $120,000 (illustrative).

Mobile apps for field teams and customer engagement

Mobile apps serve field teams who need offline access and customers who expect native push notifications and camera use. Field team examples include a logistics tracking app for 30 drivers who scan barcodes and capture delivery signatures, or a healthcare scheduling app for home visit nurses who update patient records on site. Customer engagement examples include a loyalty app for a regional coffee chain or a service booking app for a home repair network. We build with React Native for cross-platform iOS and Android when the feature set is standard, or Swift and Kotlin when deep platform integration is required. Backend services often run on Node.js with PostgreSQL, deployed to AWS or Google Cloud. Offline-first sync is a common requirement, and we use SQLite on device with conflict resolution logic. A typical field team app for 20 to 50 users takes 10 to 14 weeks with a team of 3 to 4 people (1 mobile developer, 1 backend developer, 1 QA part time, 1 project lead part time). A customer engagement app with push notifications, in-app purchases, and analytics takes 12 to 18 weeks with a team of 4 to 5. Primary risks include device fragmentation, app store review delays, and battery or network constraints. We address these with a device test matrix, early beta distribution, and performance profiling. Costs typically range from $50,000 to $150,000 (illustrative).

AI and automation: where they help and where they do not

AI and automation can reduce manual effort in specific, well bounded tasks. We help clients apply them where data is available and the cost of errors is manageable. Examples include document classification for invoice processing, anomaly detection in logistics tracking data, and a customer support chatbot that answers common questions about order status. We use Python with scikit-learn for classical models and TensorFlow or PyTorch for deep learning. For deployment, we use Azure Machine Learning, Google Cloud Vertex AI, or AWS SageMaker, depending on the client's existing cloud. These platforms provide managed endpoints, monitoring, and retraining pipelines. AI does not help when the problem lacks historical data, when errors carry legal or safety consequences without human review, or when a simple rules engine would suffice. For instance, a rules-based system is often better for calculating shipping costs than a machine learning model. A typical AI proof of concept for a single use case takes 6 to 10 weeks with a team of 2 to 3 people (1 data scientist, 1 data engineer, 1 project lead part time). Production deployment with monitoring and retraining takes an additional 8 to 12 weeks with a team of 3 to 4. Primary risks include data quality, model drift, and unclear success metrics. We mitigate these with a data audit, a baseline model, and a defined accuracy threshold. Costs for a proof of concept typically range from $30,000 to $80,000 (illustrative).

Integration with existing systems and data

Most custom software must connect to existing systems such as CRM, ERP, accounting, or legacy databases. We design integrations using REST APIs, webhooks, or message queues. For example, a new e-commerce checkout portal might need to sync orders with an on-premise ERP system and update inventory in a PostgreSQL database. A healthcare scheduling app might need to read patient records from an HL7 FHIR endpoint and write appointment confirmations to a Microsoft Exchange calendar. We use Node.js or Python for integration services, and we deploy them as containers on AWS ECS, Google Cloud Run, or Azure Container Apps. When real-time sync is required, we use Apache Kafka or AWS SQS. When batch sync is sufficient, we use scheduled jobs. A typical integration project for two to three systems takes 4 to 8 weeks with a team of 2 to 3 people (1 integration developer, 1 backend developer, 1 project lead part time). Primary risks include undocumented APIs, rate limits, and data mapping errors. We mitigate these with an interface inventory, contract tests, and a staging environment that mirrors production. Costs typically range from $20,000 to $60,000 (illustrative).

Support and maintenance after launch

After launch, software requires ongoing support to remain reliable and secure. We offer maintenance plans that cover bug fixes, dependency updates, security patches, and minor enhancements. For a web application, this typically includes monitoring with AWS CloudWatch or Google Cloud Monitoring, log aggregation, and a monthly review of error rates and performance. For a mobile app, it includes OS version updates, app store compliance, and crash reporting. For an AI model, it includes data drift monitoring and periodic retraining. A typical maintenance plan for a single application takes 10 to 20 hours per month with a team of 1 to 2 people. Costs typically range from $2,000 to $6,000 per month (illustrative). We also provide a runbook and a knowledge transfer session so your team can handle first-level support. Primary risks include undocumented changes, dependency vulnerabilities, and key person dependency. We mitigate these with version control, automated tests, and cross-training. Support contracts are separate from project work and are billed monthly.

Before a discovery call, prepare a one-page description of the problem, a list of systems that must connect, and any existing data samples. Ask for a fixed scope and a phased timeline. Compare proposals on team composition, risk mitigation, and maintenance costs, not just the lowest bid.

Sources

Background for this page was drawn from the published guidance below. Both are independent of this site and were read in full before writing.

• AWS, cloud computing
• Google Cloud
• Microsoft Azure