AI infrastructure
BatchIn
Inference API, batch jobs, and dedicated GPU capacity for teams that need stable throughput.
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BatchIn / AI Solutions / AI4Edu
From GPU clusters to production AI to classroom systems, we build what teams and schools actually need.

BatchIn, AI Solutions, and AI4Edu share one operating standard without blurring what each one does.
AI infrastructure
Inference API, batch jobs, and dedicated GPU capacity for teams that need stable throughput.
Applied AI
Production AI systems for internal tools, service operations, vision programs, and edge workflows.
Education tools
AI education systems and student learning twins for schools that need earlier intervention.
You should know where the product starts, where delivery begins, and where human accountability stays.
BatchIn keeps us close to live traffic, hard latency budgets, and the economics behind every deployment.
We look for the queue, review step, or decision that is slowing a team down, then design the system around that constraint.
The model can draft, route, or score, but the accountable person still owns the decision that matters.
We start with one workflow, one owner, and one success metric before scale enters the room.
01
Map the workflow, system boundaries, and success metric before building.
02
Use the first working pilot to validate integrations, rules, human review points, and metrics.
03
Once in daily use, watch logs, feedback, and metrics, then decide what scales.
01
Map the owner, systems, data boundary, and success metric before anything ships.
02
Deliver a working prototype in 6 weeks with the integrations, review points, and reporting the team actually needs.
03
Move into daily use, harden the weak spots, and expand only after the first workflow proves itself.
The brand relationship, delivery posture, and contact path in one place.
Direct contact
Tell us what is blocked. We will tell you whether to begin with BatchIn, a scoped system build, or AI4Edu.