Custom AI Solutions & System Integration

Data security is designed into every custom AI and integration system we build — not appended after the fact. We implement least-privilege access controls so each component only has access to the data it genuinely requires, encrypt sensitive data at rest and in transit throughout all pipelines, use secrets management services rather than hardcoded credentials in application code, implement comprehensive audit logging of every data access and transformation event, and conduct security reviews at key architectural decision points. For AI systems handling particularly sensitive data — patient information, financial records, or legally privileged documents — we recommend on-premise or private cloud deployment with open-source models that keep all data processing within your controlled infrastructure, eliminating third-party data exposure entirely.

Timeline depends significantly on scope and complexity. Simple two-system integrations with clean APIs typically take three to six weeks including testing and documentation. Custom LLM applications with defined functionality take six to twelve weeks. Multi-system integration platforms connecting five or more systems with custom middleware take three to six months delivered in phased releases. Complex custom AI solutions involving model fine-tuning, agent architectures, or AI-native product development take four to nine months depending on the scope of novel engineering required. We always recommend phased delivery — releasing usable functionality at the end of each phase rather than waiting for the complete system — so your team gains value and provides feedback throughout the build rather than evaluating the full system for the first time at the end.

We select models based on task requirements, cost constraints, latency requirements, and data handling considerations for each specific project. For cloud-hosted deployments requiring state-of-the-art reasoning and language capability, we primarily work with OpenAI GPT-4o, Anthropic Claude Sonnet and Opus, and Google Gemini Pro. For cost-sensitive high-volume applications where accuracy requirements are lower, we use GPT-4o mini, Claude Haiku, or Gemini Flash. For on-premise or private cloud deployments where data cannot leave your infrastructure, we deploy and optimise open-source models including Llama 3, Mistral, Qwen, and Phi — selecting the model whose capability-to-compute-cost ratio best fits your hardware and accuracy requirements. Model selection is always a reasoned recommendation based on your specific situation, not a default that we apply uniformly across all projects.

You own it — completely. Custom software development at Webtech Nepal transfers full intellectual property rights to the client upon final payment. This means you own the source code, the integration configurations, the prompt architectures, the fine-tuned model weights if any were produced, the documentation, and every other deliverable produced during the project. You can host it wherever you choose, modify it with your own team or another provider, and use it without any ongoing licence fee or dependency on our continued involvement. We believe customers should own the technology built for them rather than renting access to it, because ownership is what makes custom development a genuine long-term asset rather than a recurring vendor relationship dressed up as bespoke engineering.

Yes — Nepal-specific system integration is a core part of our work. We have hands-on integration experience with Tally ERP (using the Tally ODBC interface, XML gateway, and TallyPrime API where available), eSewa, Khalti, ConnectIPS, IME Pay, and other local payment gateways, local banking APIs for direct transfer and reconciliation, Nepali government e-services where APIs exist, and the various locally-popular SaaS tools used by businesses across Nepal. For systems without formal API documentation, we work directly with vendors to understand available integration options rather than assuming the same approaches that work for global platforms will apply to locally-deployed or locally-developed software.

We design custom AI systems with provider dependency risk in mind — abstracting model API calls behind an interface layer that can be updated to point to a different model or provider without changing the application code that consumes AI outputs. This abstraction, combined with our AI model maintenance retainer, means that when OpenAI deprecates a model, Anthropic changes an API endpoint, or a better model becomes available at lower cost, we update the integration without disruption to your operations. For organisations with very high tolerance for AI provider dependency risk, we design systems from the outset to support multiple providers simultaneously with automatic failover — so a provider outage routes to an alternative rather than causing system unavailability.