Capabilities / Systems, not silos
Tandemora combines applied AI, product engineering, and cloud operations so the critical path stays owned from evidence to handover.
Azure-first by default · portable where it mattersSomeone has to decide what the thing actually does before anyone builds it. We map the workflow, draw the product boundary, and prototype the interface people will be using eight hours a day.
AI-native product developmentA standard product almost fits, and the gap is costing you in spreadsheets and email. We build the application around the roles, approvals, and integrations you actually have.
Custom business applicationsField and workforce apps meet conditions a demo never does: no signal, gloves, a cracked screen, a scanner that has to pair every time. We design for that before we design the screens.
Custom mobile applicationsThe interesting failures happen at the boundary. A timeout that was actually a success, an event processed twice, a retry that charged the customer again.
Distributed backends, APIs, and integrationsMost cloud trouble is not exotic. It is a system nobody can observe, a deploy nobody can reverse, and a bill nobody can explain.
Azure architecture and reliabilityLanguage systems are easy to demo and hard to keep honest. We wire in the permissions, the review step, the latency budget, and what happens when the model is confidently wrong.
Agentic workflow automationYour people already know the answer exists somewhere. The work is finding it, showing where it came from, and turning what a document says into something a system can act on.
Document intelligenceA camera sees what the lighting and the angle allow. We specify the capture first, build the detection second, and set the threshold against what a miss actually costs you.
Visual quality inspectionA forecast is only useful if somebody changes an order because of it. We work backwards from the decision, the constraint, and the price of being wrong.
Demand and inventory forecastingYou should be able to ship a model change on a Tuesday without holding your breath. That needs governed data, an evaluation you trust, and a rollback that has been tested.
Data and ML platform foundationsCurrent boundary
We favor pretrained models and selective small-model adaptation. We do not sell speculative foundation-model training, standalone cybersecurity, brochure websites, or founder-provided 24/7 support.
Most projects touch three or four of these at once. Describe the problem and we will tell you which parts actually apply.
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