Industry: Construction
We build Construction software with AI inside. Hazards flagged from site cameras, drawing sets read for you.
Overview
Most construction software development services begin with the workflow (estimating, project scheduling, progress tracking) and add AI later. But a model is only as good as its data, and construction data is awkward: drawings revised weekly, photos taken through dust and shadow. We learned this across a decade of computer vision projects, where models trained on clean images often fall apart. It-Jim is a construction software development company that covers the whole chain: capturing data from cameras, drones and drawings, training the models, and building the software around them.
Services
Some firms arrive with an idea for what AI could do on site; others with a system that has fallen behind. We cover both ends, from research sprint to live job.
We build models that read what cameras, drones and phones capture on site: protective gear, equipment and materials. Trained on your sites and camera angles, they return measurements teams can act on.
Schedules, cost records and field reporting sit in separate systems, so nobody sees the pattern until the delay has happened. We join them and flag early signs such as resource allocation conflicts, with each forecast's measured error attached.
Much of a project's office work is reading and retyping: RFIs, submittals, invoices. We build AI agents for construction, automating construction document intake and routing. They leave decisions to your people and trace every step to its source.
For many firms, construction IT services begin with a system nobody wants to touch, such as an on-premise estimating tool. We modernize it in stages, wrapping the old logic in APIs and adding AI while crews keep working.
A two-week research sprint checks feasibility on a sample of your data. A proof of concept then builds a working prototype in two to three months, and sometimes the honest result is that the technology is not ready yet.
An MVP is the first version of the software a site team would open every day, often mobile field apps: one task, one project type, one way to capture data. It reuses the validated models, so growing it means extending, not rebuilding.
Solutions
AI pays off where information arrives late: a hazard nobody saw, a revision nobody read, a machine that failed before its service. The solutions below close those gaps.
01 / 06
A missing hard hat is easy to detect; knowing whether it matters is the difficult part. We add camera calibration and site rules, so a detection becomes a safety compliance alert worth acting on.
Use cases
Ideal for
02 / 06
An inspector photographs what they notice; a model checks every frame with the same attention. As a construction software development company, we build software that finds cracks, surface defects and misplaced elements in site photos.
Use cases
Ideal for
03 / 06
Drawing sets, specifications, RFIs and submittals hold the data a project runs on, in layouts no two firms share. OCR and parsing feed language models that extract fields against a fixed schema; anything uncertain goes to a person.
Use cases
Ideal for
04 / 06
A breakdown mid-pour idles a crew along with the machine. A failure model needs a long service history to learn from, so before AI-enabled construction software development starts, we check whether your telematics and service records can support one.
Use cases
Ideal for
05 / 06
Field operations data is scattered: telematics in one portal, operator logs on paper. We join those sources and add cameras where telematics stops, recognizing whether a machine is digging, loading, hauling or idle.
Use cases
Ideal for
06 / 06
A photo shows one angle; a 3D model lets you measure from anywhere. We reconstruct sites from drone and phone photos into point clouds and meshes, the digital twins you compare with the design and master schedule. Scaffolding hides parts of a structure, so we validate on your type of site before promising numbers.
Use cases
Ideal for
A fixed quote for software that has never run on your sites is a guess. Price depends less on the number of screens than on one unknown: whether the model reaches the accuracy you need on your data.
Budgeting construction software development as one block means paying for every unknown in advance, or meeting one halfway through. We put the riskiest question first: will the model work on your data? If it will, the next stage is priced against measured results. If it will not, a two-week research sprint is all you spent.
Process
Typical vendors start once the data is ready. We start earlier, with the site and the decision the software must improve, and stay on after launch.
Technologies
Before you invest in development, we tell you plainly whether the AI will work on your site data.
Delays and hazards surface while there is still time to act.
Models are trained on dust, shadow and weather, with cameras planned for your sites.
Every model ships with its error rates on your data, so you know what to trust and what still needs a person.
The code and the trained models are yours and only yours, with no licensing strings attached.
Why It-Jim
Production deployments of computer vision and machine learning, including visual inspection and 3D reconstruction.
A decade of machine learning and computer vision, applied to measurement, inspection and reading documents.
Engineers founded this company, and engineers run it today. We promise only what the engineer who builds it has already tested.
The projects other teams turn down are the ones we ask for.
Advanced degrees in computer vision and physics let us model what a site does to an image.
Every project is applied research for us, so prototypes appear fast.
Questions
It is the design and engineering of custom software for building projects, covering estimating, inspection, safety and document control. At It-Jim, that means models that read photos, video, drawings and equipment data, and the software your teams use them in.
Yes, usually without replacing anything. We add detection, document reading or forecasting to your current system through APIs. A research sprint first confirms your data can support the model. Contact us with your task, and you'll get a concrete estimate in one day.
Yes. Many field operations have weak or no connectivity, so we run models on devices such as NVIDIA Jetson boards at the camera and send only results to the cloud, which also keeps raw video on site. We compress each model to meet your latency target on that hardware.
A technical research sprint takes two weeks, and a proof of concept typically takes two to three months. An MVP and a full product vary with scope, mainly with your data.
It extends safety monitoring to what no supervisor can follow continuously: missing hard hats and vests, entry into restricted zones, unsafe postures. OSHA says four hazards (falls, struck-by, caught-in or between, and electrocution) account for about 60 percent of construction fatalities every year, and cameras can flag some of the conditions behind them. Detection is the easy part. We calibrate each camera on your site so a zone drawn on screen matches the real area, then tune the rules so alerts stay rare enough to act on.
Services behind it
Every hour of footage, watched. Events flagged live and searchable in the archive.
Open the serviceObjects found, named, and counted in milliseconds. On a phone, a smart camera, or a server.
Open the serviceCustom computer vision development services and solutions that work outside the lab. Get an honest estimate.
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Industries
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