Intelligent Automation. Scalable Solutions. Built for the Global Frontier.

pdpspectra has offices in Boston (US), London (UK), Sydney (Australia), and Kathmandu (Nepal). We serve clients worldwide across healthcare, finance, education, logistics, government, and technology sectors. Whoever you reach first hands off to whichever office best matches your timezone and domain.

Yes — in Nepal and internationally. We build patient data platforms, automate hospital admin workflows, and integrate AI into healthcare operations for hospitals and clinics. We work to local compliance standards (MoHP in Nepal, HIPAA-equivalent for international engagements) and integrate with existing hospital management systems rather than replacing them.

Yes. We help schools and universities — in Nepal and globally — modernize with student information systems, fee automation, attendance tracking, and academic analytics. We integrate with existing tools or build custom platforms tailored to the local education sector. Nepal is a particular specialty.

Yes. We help government agencies and public sector organizations in Nepal digitize paper processes, automate workflows, and build reporting dashboards. We understand local procurement processes and can work within government IT frameworks.

Yes. We help logistics companies eliminate manual processes and gain real-time visibility — fleet tracking, warehouse automation, supply chain analytics, route optimization, and shipment-status dashboards. Typical wins: faster dispatch decisions, lower fuel and labor costs, and one source of truth across the systems that don't currently talk to each other.

Yes. Our four offices — Boston, London, Sydney, and Kathmandu — span EST through AEST, so engagements are run from whichever office best overlaps with your working hours. Standups, code reviews, and incident response are scheduled around your timezone, not ours.

Absolutely. Many of our clients are traditional businesses — hospitals, schools, financial institutions, and government agencies — who want to modernize without hiring a full in-house tech team. We handle the technical complexity and communicate in plain language throughout. You don't need to understand data pipelines to work with us; you just need a clear business problem.

Yes — in fact, non-tech businesses often see the biggest ROI from data and automation work because the baseline is lower. If your team is still using spreadsheets, manual approvals, or disconnected software, there's significant opportunity to save time and reduce errors. We scope every engagement around your business outcomes, not technical deliverables.

AI can automate repetitive tasks, surface insights from your data, power intelligent customer experiences, and help your team make faster decisions. We implement practical AI solutions — from LLM-powered internal tools and customer chatbots to predictive analytics and automated workflows — that deliver measurable ROI without years of R&D.

Data engineering is the practice of building systems that collect, store, and transform your data so it can be used for analytics, reporting, and AI. If your team spends time manually exporting spreadsheets, can't trust your data quality, or wants to use AI but doesn't have clean data to work with — you need data engineering. We build pipelines using Airflow, dbt, Spark, Snowflake, Databricks, and ClickHouse.

We automate anything that involves repetitive manual steps — data syncs between tools, report generation, approval workflows, alert systems, and scheduled jobs. We use n8n, Apache Airflow, custom APIs, and native integrations to connect your existing stack without replacing it. Most automation projects save teams 10–30 hours per week.

MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining ML models in production reliably. Most businesses can train a model — the hard part is keeping it running accurately in the real world as data changes. We set up ML pipelines, model registries, drift monitoring, and retraining workflows using MLflow, Kubeflow, and AWS SageMaker.

Both. We work with early-stage startups building their first data platform or integrating AI quickly, and with growing companies scaling their infrastructure or modernising legacy systems. Our lean team structure means we move fast without the overhead of a large consultancy. If you have a specific data, AI, or automation problem, we can help regardless of company size.

We work across AWS, Google Cloud Platform (GCP), and Microsoft Azure. We also support hybrid and multi-cloud setups. Most of our data and AI tooling is cloud-agnostic — we choose the right platform based on your existing stack, budget, and workload requirements.