How the work actually gets done.

Most consultants run software. We write the calculation — so that when a drawing changes, everything that depends on it changes with it, and every number can be traced back and signed for.

One

The calculation is written, not repeated.

An estimate for a culvert is a parametric workbook: change the span, the cell size or the wall thickness and the measurement sheet, the bar bending schedule and the abstract all follow.

Two

Quantities are linked to the drawing.

Geometry is read from the DWG or DXF rather than keyed in from it. When a drawing is revised, the quantities are regenerated, not patched.

Three

Every number has a source.

Each item carries its schedule-of-rates reference; each quantity traces back to a dimension on a drawing.

3D model of an RCC box culvert with every reinforcement bar modelled in place — Reinforcement modelled with the structure — the bar bending schedule is generated from this geometry.

Where AI Fits

One tool among Python, MATLAB, VBA and Excel.

We use AI-assisted tooling for drafting, document checking and routine conversion work, and we say so plainly. It is useful for speed, never a substitute for judgement. No output leaves this office without an engineer checking it and taking responsibility for it. Client drawings and project data are not used to train anything.

Where It Pays

Three working areas for computation and automation.

Computer visionStructural health monitoringCrack and distress detection from photographs and drone footage — quantified, mapped and tracked over time.

ForecastingFlood & demand forecastingANN / LSTM models on rainfall, river gauge and consumption records — with stated accuracy and limits of applicability.

OptimisationGenetic-algorithm design optimisationMillions of design combinations evaluated against cost, code compliance and performance — the engineer picks from the best few.

Asset managementPredictive maintenanceCondition data turned into maintenance schedules before failure, for plants, pipelines and structures.

InventoryInventory intelligenceReorder alerts, expiry tracking and dead-stock identification for traders and small manufacturers.

Cash flowReceivables that chase themselvesReceivables ageing with automatic WhatsApp / SMS reminders — polite, persistent, documented.

DashboardOwner's one-screen morningSales, cash, dues and exceptions on a single daily screen — the owner's five-minute briefing.

VisionSafety & PPE checksComputer-vision checks for PPE compliance and unsafe acts on sites and shop floors.

SatelliteChange detectionMulti-temporal satellite imagery to track construction progress, encroachment and land-use change.

ClassificationLand-use mappingSupervised classification of crop, water, built-up and fallow land for planning and water budgeting.

HydrologyEvapotranspiration from orbitSatellite-derived ET feeding irrigation scheduling — measurement at the scale of a taluka, not a test plot.

Web GISLiving dashboardsWeb GIS dashboards for local bodies — assets, works and grievances on one map, updated continuously.

Responsible Use of AI

Four rules we do not bend.

These principles are printed in our reports and enforced in our workflow — they are policy, not marketing.

TransparencyWe state which parts of a deliverable used AI, which model, and on what data it was trained.

Validated performanceEvery predictive model is reported with accuracy measures and limits of applicability. A number without an error bar is an opinion, not a result.

Data protectionNo confidential client data is ever placed into public AI services. Client data is never used to train models for other clients.

Honest scopingIf a problem doesn't need AI, we say so and propose the simpler fix. Advisory that could cause loss is issued with its uncertainty stated.

Data Security

Confidentiality, in writing.

Written non-disclosure agreement offered as standard on every engagement.
Client data stored within India and never shared with third parties.
Access restricted to the individuals working on the assignment.
Full data export and deletion available to the client on request.
Farmer and village data treated as belonging to the community that generated it.

Good Fit

What makes an automation project worth doing

The best candidates have three things: a repetitive decision, data that already exists (or sensors worth installing), and a measurable cost of being wrong slowly. If your problem has those, the payback is usually quick.

If it doesn't, we will tell you — and quote the conventional engineering solution instead.

Discuss a use case →

The Toolchain Behind It

Bring us the boring, repetitive decision.

The one your team makes a thousand times a month on gut feel. That is where automation pays back fastest.

Start the conversation →