SM Stratagem builds computer vision development for Doha teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build computer vision development for teams in Doha that need working software, not a slide deck for next quarter's steering committee. Qatar's LNG-funded economy rewards teams who can act on data quickly, and Doha operators tell us the same thing every quarter: less theatre, more delivery. Our approach is computer vision systems for real environments — cameras, lighting, and hardware included, wrapped in evaluation and monitoring so quality is a number your team owns, not a vibe check. We handle infrastructure, evaluation, and handover so your team owns the system after we leave, not a black box only we understand. The team is remote-friendly but in-region, so travel to Doha for workshops and go-live is standard, not a favour we ask for. We keep computer vision development teams small on purpose — usually three to five people on your project — so the person building understands the full system, not just their slice. If you have a rough brief, we can turn it into a build plan without a two-month discovery phase that nobody remembers by launch.
Buyers in Doha are done with pilots. What they want now is one production system, measured, running, and reducing a real cost line or lifting a real revenue line. That's the frame we work inside.
Models trained and evaluated on your real cameras, lighting, and workflow — not a clean dataset that behaves nothing like the site. Real-world images make real-world models.
For high-throughput or low-latency use cases we deploy models at the edge (NVIDIA Jetson, Coral, or your own GPUs), then only send what matters to the cloud. Bandwidth and cost stay under control.
Vision systems that surface uncertain frames for a human to label, close the loop, and get better every week rather than getting stale after month one.
Camera health monitoring, retraining pipelines, drift detection, model rollback — the ops that keep a vision system alive after the launch buzz fades.
Vision models on the production line that flag defects with confidence scores and escalate ambiguous cases to a human inspector.
Footfall, dwell time, occupancy, and anomaly detection built on existing camera infrastructure, respecting privacy rules by default.
OCR plus computer vision that turns paper forms, invoices, and IDs into structured data your existing systems can consume.
For QatarEnergy contractors, QFC-registered firms, and ministries in Doha, we deliver computer vision inside existing vendor frameworks and procurement rules. Compliance is a delivery input, not a surprise at UAT.
Usually not to start. Most projects can use the cameras you already have, and we tune the model for those conditions. When accuracy demands a specific sensor (thermal, high-resolution, low-light) we spec what's needed and you decide whether it's worth the upgrade. Hardware choice is a cost/accuracy trade we make transparently, not an upsell.
Modern pre-trained vision models mean you often need hundreds to a few thousand labelled examples per class rather than the tens of thousands the textbook implies. For hard problems we use active learning to focus labelling effort on the frames that matter most, so labelling cost stays proportional to the value of the outcome.
Yes. For high-throughput industrial use cases and privacy-sensitive deployments we run models on NVIDIA Jetson, Google Coral, or your existing GPU hardware. The cloud is used for aggregation, retraining, and dashboards rather than for every inference. That keeps bandwidth costs down and latency predictable.
Two ways. First, train on your actual conditions — variable lighting, dirty lenses, occasional occlusion — so the model isn't surprised. Second, monitor for input drift in production so you know when the environment has changed (a camera moved, a light burned out) before the model quietly gets worse.
For a well-defined single-task project, expect eight to twelve weeks end-to-end: data collection, labelling, model training, integration with existing systems, edge or cloud deployment, and monitoring. Multi-task or multi-site projects run longer because rollout, physical calibration on each site, and support become a larger share of the work than the modelling itself.
We handle model, integration, and deployment. Physical camera install and networking is usually your team or a specialist we work alongside. We spec the setup so integration is straightforward, and we're on-site for calibration and go-live when the project needs it.
Yes. Public-sector and energy-adjacent work in Doha typically requires sitting inside an existing vendor framework and delivering under strict procurement rules. That's the shape we default to. We don't take POC-only work in Qatar — it wastes everyone's time — so we scope for production from the start.
SM Stratagem builds machine learning development in Doha, Qatar. ML that reaches production. Monitored for drift and quality. Retraining on a schedule.
SM Stratagem builds ai data engineering in Doha, Qatar. Data pipelines that don't rot. Quality measured, not assumed. Vector and structured, both.
SM Stratagem builds ai fine-tuning in Doha, Qatar. Fine-tuning that earns back cost. Smaller, cheaper, faster models. Evaluated against baseline.
SM Stratagem builds computer vision development in Riyadh, Saudi Arabia. Vision for real environments. Edge or cloud, your call. Handles lighting and drift.
SM Stratagem builds computer vision development in Sharjah, United Arab Emirates. Vision for real environments. Edge or cloud, your call. Book a scoping call.
We'll scope the first release, define the eval set, and give you a build plan you can hand to any engineering team — ours or yours.