Our Expertise: Advanced Web Development Solutions

PWAs that have to keep working when the network does not — built for agronomists in the field, not analysts at a desk.

Our deepest body of work here is an East African agricultural data-collection programme where field workers operate around fuelled cell towers and intermittent connectivity. We build the data layer in the browser using sql.js, OPFS and IndexedDB, run mutation queues through service workers, and treat sync as a first-class feature — not an afterthought. The architectural decisions that matter are rarely about frameworks: HDOP filtering on captured GPS, whether to resolve write conflicts via CRDTs or last-write-wins (we have shipped both, depending on whether the field model is additive or supervisory), how aggressively to compress payloads for low-bandwidth uplinks, and how to surface sync state to a user who genuinely cannot tell whether their phone has signal. The deeper write-up of how we approach this is on the offline-first apps page, and the engineering walk-through lives in building offline-first applications.

Operator-grade UIs where positions, prices and audit trails move while the user is looking at them.

The anchor engagement here is the TR Capital portfolio management web app — a web application used by an investment team to manage client portfolios, where the same screen has to show live positions, accept analyst edits, and preserve a full audit trail. The interesting decisions are rarely "use WebSockets". They are: where polling beats sockets because the upstream system only refreshes every few seconds anyway; where SSE is the right call because the flow is one-way and survives proxies better; how to virtualise a data grid so 10,000 rows do not collapse the main thread; and how to design an audit-trail UI that lets a reviewer see who changed what, when, without burying the operational view underneath. Real-time is a UX problem first and a transport problem second.

Web UIs that put a model in the loop without putting a hallucination in front of the user.

Our reference engagement is documented on the AI/ML success story page — an intelligent document processing pipeline for a sustainability certification body, combining a scikit-learn classifier with OCR and NLP, surfaced through a reviewer-facing web app. The UI patterns that matter when a model is involved are specific: streaming LLM responses so the user sees tokens arrive rather than staring at a spinner; RAG-backed search where the answer cites the source documents inline and the user can verify in one click; human-in-the-loop review interfaces where low-confidence predictions are routed to an operator rather than auto-applied; and confidence-threshold UI patterns that visibly distinguish "model is sure" from "model is guessing — please check". On the agent side, our agentic AI work runs on FastAPI with LangChain / LangGraph and RAG, which is what we wire these interfaces to.

Map-based dashboards where the data pipeline behind the pixel is as important as the pixel itself.

The data engineering success story is a geospatial PWA over an Airflow and PostgreSQL backbone, with NDVI and EVI indices computed off satellite imagery to support a sustainability certification programme. The frontend decisions here are not about charting libraries — they are about how you overlay satellite tiles on a basemap without sandbagging mobile devices, how you let an operator scrub a time-series of vegetation index values across seasons, how you visualise polygon-level metrics without turning the screen into spaghetti, and — critically — how you communicate the gap between collection time (when the field worker captured the data) and sync time (when it actually reached the server). For audit-grade workflows that distinction matters; we put it on the UI rather than hiding it behind a timestamp.

Serverless is a tool, not a religion. We pick it when the workload suits it, and we do not when it doesn't.

Cloud-native, for us, means containerised services with sensible boundaries, CI/CD, and event-driven glue where it earns its keep. Serverless functions are a good fit for bursty, stateless work — webhook ingestion, image resizing, scheduled jobs, lightweight APIs in front of a managed store. They are a poor fit when cold-start latency would be felt by an end user on every request, when a job needs to run for many minutes (we move those onto containers or worker queues), when concurrency limits would throttle a known-bursty workload, or when local-state caching would genuinely have helped. We do not claim a partner tier we do not hold; we claim that we have shipped enough mixed-architecture systems to know which parts deserve Lambda / Cloud Functions and which parts deserve a long-running process behind a load balancer. That call gets made per workload, not per pitch deck.