TRY MORE is an engineering and technology team focused on water infrastructure — water supply networks, sewer systems and technological facilities including WWTPs.
TRY MORE is an engineering and technology team focused on water infrastructure — water supply networks, sewer systems and technological facilities including WWTPs. Our strength lies in combining hydraulic and process modelling with data integration and an operational perspective.
We do not stop at a “study for the drawer”: we design models to be maintainable, calibratable and usable in everyday decisions. In practice this means that alongside the calculations we also address input data quality, data models, GIS/SCADA integration and outputs in the form of interpretable KPIs.
This approach is the foundation of modern digital twin solutions, where the virtual model serves as “live” operational support. We take inspiration from the sector’s leading firms: connecting models with operational data, decision support, and an emphasis on tools that are actually used by operators and planners, not only by modellers.
Our projects span the full range from “classic” hydraulic models to systems fully connected with data and dashboards. In water supply networks we typically address pressure conditions, peak-demand supply, fire flows, operational scenarios and the impacts of urban development.
In sewer systems we focus on capacity, rainfall-runoff processes, surcharging, overflows and alternative mitigation measures. For WWTPs the work covers process assessments, optimisation of operating parameters and design of measures that account for energy use and process stability.
A large share of the value we add lies in working with data — we prepare inputs, clean and validate data, set up update rules and use them for calibration and continuous model improvement. This way of working mirrors how “digital water” teams today combine models, data and decision-making in a single flow — from the data source to an actionable output.
We always start with the decisions the client needs to make: investment (CAPEX), operational (OPEX), emergencies, urban development or compliance. We then run a rapid data audit — establishing what is in GIS, what is in SCADA, what measurements exist and what can be used for calibration and validation.
We build the model with clearly defined boundaries and assumptions so it can be updated easily when the network or the data change. In calibration we are not after “pretty charts” but a stable match with measurements at key nodes and operating regimes, so the model can be used for scenarios and predictions.
We deliver outputs as a technical report + data package + KPI interpretation, so they can be defended before management, designers and regulators alike. Where it makes sense, we prepare decision-support outputs (dashboards, reports, scenarios) usable by non-modellers.
We present references so they are comparable and technically legible: the scope of the network or facility, the model type, the data sources and the key decisions the outputs supported. For utilities the most common impacts are a better understanding of network behaviour, improved intervention planning and more accurate estimates of measure effectiveness.
For developers, fast and defensible connection and capacity assessments are decisive, including alternative solutions and their consequences. We make sure the results are auditable — clear assumptions, data sources, methodology and reproducible calculations.
Handover always includes a clear interpretation, so the model does not “stay with the supplier” but becomes the client’s own internal tool.
We work where GIS, modelling and operational data meet — which is why we support cross-system integrations and open data formats. For us, GIS is the fundamental “truth about assets” and the geometric topology of the network, on top of which we build hydraulic and analytical layers.
In digital twin projects, integration with operational data (SCADA/telemetry) — and often with maintenance records (CMMS/asset register) — is essential for the outputs to translate into practice. Technically, we prefer solutions that can be maintained long term: clear data pipelines, documentation and update procedures.
