FlowThink · flowthink.ai
FlowThink builds AI-native software and computational engineering tools for water systems: continuous monitoring, advanced hydraulic modeling, network optimization, and expert language models, all grounded in physics.
What we do
Everything we build shares the same core: trustworthy field data, rigorous physical models, and AI that respects both.
Flow-monitoring programs taken end to end on our FlowER platform: sensor QA/QC cross-checked against hydraulic first principles, wet-weather event detection, dry-weather flow decomposition, RTK and HYMOD calibration by evolutionary optimization, design-storm capacity numbers, and real-time ingestion with forecast-driven alarms.
Numerical engines built in-house, beyond what desktop packages offer: implicit dynamic-wave solvers for full pipe networks, hydraulic grade line profiling and surcharge classification, SWMM-interoperable formats, and numerics validated against golden reference models on every change.
The network treated as a graph: connectivity tracing over utility GIS, maintenance-zone sequencing that follows flow direction, cleaning and inspection schedules optimized against operational constraints, and deliverables that drop straight into enterprise asset-management systems.
Large language models trained and fine-tuned into domain experts for water and civil engineering. Everything they produce is verified and grounded in truth, evaluated against engineering ground truth before it reaches you.
Expert AI
We train and fine-tune large language models into domain experts: systems that speak the language of hydrology, hydraulics, and utility operations, and know exactly where their knowledge ends and the physics begins.
Frontier models adapted to water and civil engineering, trained on domain corpora, terminology, standards, and workflows so answers sound like a senior engineer, not a search engine.
Everything our models produce is verified and grounded in truth. Answers trace back to real data and validated engineering results, never guesswork.
Expert models are only as good as their evaluation. We benchmark against engineering ground truth using golden datasets, parity suites, and adversarial review before anything reaches a decision-maker.
Applications
The same foundation of continuous data, physical models, and expert AI extends far beyond today's deployments.
Living models of sewer and stormwater networks that assimilate telemetry continuously: always calibrated, always current, queryable in plain language.
Capacity and overflow risk evaluated under future rainfall regimes and growth scenarios, so infrastructure built today still works in 2075.
Data assimilation across gauges, radar, and forecasts to predict system response hours to days ahead, from a single basin to an entire region.
Condition signals hiding in flow data, from infiltration trends to blockage signatures to sensor degradation, turned into prioritized maintenance.
Sensor fleets that QA themselves, flag their own drift, and schedule their own recalibration: trustworthy data with minimal field labor.
A fine-tuned assistant that has read the design manuals, knows the local standards, and can walk a calibration or a capacity study with you, end to end.
Machine learning augments hydraulic and hydrologic models. It never replaces them.
Software accelerates judgment; licensed professionals make the calls.
Every result is reproducible, versioned, and traceable to its inputs.
Multi-tenant AWS architecture with isolation, quotas, and audit trails built in.
About
FlowThink AI is a software startup working at the intersection of municipal water infrastructure, hydrologic and hydraulic modeling, and machine learning. The work spans utility-scale flow-monitoring programs, RDII calibration studies, custom hydraulic solvers, network operations optimization for city wastewater systems, and the training of domain-expert language models, with contributions presented at industry venues including WEFTEC and PNCWA.
We believe the water industry's hardest problems, from aging assets to wetter storms to tighter budgets, will be solved by teams fluent in both the physics and the software. That is the team we are building.
Whether it's a flow-monitoring program, a hydraulic model, an operations problem, or an AI initiative, we'd like to hear about it.
