One engine, three build types
Our AI generative design engine is rule-based, not prompt-based. It reads your alignment, loads, codes and type libraries, then issues the model, the validation, the drawings and the BOQ from one source. Site Intelligence closes the loop on site. Nothing here asks you to model the whole project first.
Alignment-driven piers, caps, girders, viaducts and station systems. Validated on every change.
livdes.ai/metroCapacity in, hall out: rack grid, electrical and cooling rooms, skids, containment, cost per MW.
livdes.ai/datacentreProgress verified from imagery against the schedule, mapped to BOQ lines, approved by a person.
on LivSYTProblems
Move the alignment and the piers, drawings and quantities are all done again by hand.
Most Indian and Middle Eastern infrastructure has no BIM model, so tools that need one are out.
Work done on site and work billed are different numbers, argued monthly with photos and memory.
The rule-bound work fills the programme. It is exactly the work software should be doing.
Rule-bound work shouldn't eat your programme.LivSYT takes it. Engineers keep the judgement.
How it works · 01 / 03
Chainage, levels, spans, ground conditions. The inputs an engineer already has on day one, and the codes the project is built to.
Scroll to continuePiers, caps, girders and deck placed along the alignment, checked for code and clearance, re-checked every time the alignment moves.
Scroll to continueNo redrawing, no reconciliation. Quantities trace back to the element that produced them, and every revision is versioned.
Scroll to continue[01] · livdes.ai/metro
A viaduct is one set of decisions repeated a thousand times. AI generative design takes the alignment and the pier library, generates the structure, and re-validates it the moment the alignment moves. Engineers set the rules and sign the drawings. The repetition is what gets automated.
Exchanges data with MiDAS, Oasys, STAAD Pro and Excel
Viaduct generated from alignment + pier type library
[02] · livdes.ai/datacentre
Four levels generated from the capacity target: electrical, two data halls, cooling plant
Indicative for a four-level colocation build
Power, cooling, and how many racks fit before something breaks. Set the capacity you're chasing; AI generative design produces the hall, checks it against itself, and prices every version from the model. When the client moves the megawatt target on Friday, the drawing set moves with it.
SSO, role-based access, audit trail, on-premise or gov-cloud
[03] · Site Intelligence
That gap costs money. Most AI watches a site and guesses. Site Intelligence flips the question: the AI gets each frame together with what the schedule expects in that zone, and must confirm or dispute it with visual evidence. Claims it can't find in the pixels are dropped. An engineer reviews short episodes instead of thousands of frames, and every approval joins a permanent record mapped to BOQ items. No BIM model required.
Zone view: schedule expectation checked against located evidence
For skeptics: this pipeline ran side by side with the old system and took over only when the results agreed.
| Person | What they do | Time |
|---|---|---|
| Project admin | Sets it up once | ~30 min, once |
| Project engineer | Reviews what the cameras saw | ~30 min/day |
| Project manager | Approves claims and reports | at milestones |
| Leadership | Reads the digest, tracks slippage | ~5 min/week |
Cameras stream. Minutes later, the first detection.
The engineer opens the queue and clears it in 25 minutes.
Progress rolls up against BOQ lines.
The digest email lands with what slipped and why.
The system learns from the day's reviews. Nobody has to do anything.
Site Intelligence is part of LivSYT, the construction project-management platform. LivSYT AI comes with it: an assistant that answers questions like "give me today's DPR" or "what's slipping, and why?" straight from live project data.
Five steps · every claim earns its place
The AI sees the frame and the schedule together and reports what it found. It is answering a question about this zone, not describing a picture.
18 claims read · scroll to continueEvery claim must be found in the pixels, with a box around the evidence. If it can't be, it's dropped. This is where confident nonsense dies.
13 located · 5 dropped · scroll to continueIndependent passes challenge the result. They can relabel it or drop it. Agreement is earned, not assumed.
11 survived · scroll to continueOnly verified claims are written down: zone, activity, confidence, stage. Each one mapped to the BOQ line it belongs to.
11 recorded to BOQ · scroll to continueConfident results move forward. Everything else goes to a person, as a short episode with its evidence attached.
5 to an engineer · 1 disputed to PMWho it's for
Generate pier and girder families from the alignment instead of detailing each one.
Size power and cooling with the building, not after it.
Price alternatives before committing a scheme to the client.
One model issuing drawings, quantities and revisions, versioned with rollback.
Progress claims tied to BOQ lines and backed by dated evidence.
Approve verified progress at milestones instead of arbitrating photographs.
Clear the day's review queue in about half an hour.
A weekly digest of what slipped, and why, from live project data.
Gurugram metro · ten pier types · same scope both ways
Design time saved
0%less design time across ten pier types
Questions we get on stand
Generative design driven by engineering rules rather than prompts. You give the system the alignment, the loads, the codes and the type library; it produces the 3D model, validates it on every change, and issues drawings, reports and BOQ from the same source. Engineers stay the authors — they set the rules and approve the output.
On a Gurugram metro viaduct study covering ten pier types, a 190.5-hour manual workflow was completed in 66.5 hours — a 65% reduction. The saving comes mostly from repeat validation and documentation, not from skipping design decisions.
Yes. Set the target capacity, the rack profile and the site envelope. AI generative design produces the hall grid, electrical and cooling rooms, skids and containment, checks clashes across disciplines, and prices each alternative from the model so you can compare cost per megawatt before committing.
No. It works from a schedule and site imagery. Zones use the names your planner already uses — Pier P1, Span S3, Level 4 west wing. Most Indian and Middle Eastern infrastructure projects have no BIM model, which is why the product was built this way.
The AI receives each frame together with what the schedule expects in that zone, and must confirm or dispute it with visual evidence. Any claim that cannot be located in the pixels is dropped. Independent passes can relabel or drop a result, and an engineer reviews every reading before it counts.
Half a day to one day on site. You walk the site with the planner and record the zones; a six-step wizard configures zones, cameras and coverage, and the planner signs off before anything goes live. Start with a phone and a schedule; add fixed cameras later.
LivSYT exchanges data with MiDAS, Oasys, STAAD Pro and Excel, so analysis and documentation stay consistent with the generated model. Enterprise deployments support SSO, role-based access, audit trails, on-premise and gov-cloud hosting.
A pier type, a hall, or one zone of a live site. Forty-five minutes, your data, your codes. If the output doesn't hold up against your own drawings, you've lost an afternoon.