Model2System Studio
We are an AI engineering studio for companies that need practical systems, not just demos. We deliver computer vision, speech recognition, machine learning, optimization, private deployment, and GPU / Huawei Ascend adaptation for transportation, energy, public-sector, enterprise, and game AI scenarios.
We design the data pipeline, model route, evaluation method, inference service, business integration, private deployment, and continuous improvement needed to make AI run reliably in real workflows.
Object detection, multi-object tracking, counting, behavior recognition, temporal event analysis, and multi-camera video pipelines for transportation, industrial, campus, and safety scenarios.
Speech transcription, speaker diarization, keyword boosting, long-audio processing, information extraction, meeting summaries, and integration with business systems.
Forecasting, decision systems, reinforcement learning, game AI, routing, scheduling, and constraint optimization for domain-specific workflows.
Model serving, async task queues, APIs, monitoring, Docker deployment, private-network delivery, and GPU / Huawei Ascend adaptation.
These examples are anonymized where needed, and focus on our delivery across algorithm design, engineering systems, and applied research.
Transportation video analytics
We built an AI video analysis service for onboard bus cameras, covering passenger detection, door-area modeling, multi-object tracking, boarding and alighting event recognition, OD association, and domestic AI accelerator adaptation.
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Game AI / reinforcement learning
For a game AI startup, we extended research-grade Dou Dizhu AI to support Laizi rules, rebuilding action spaces, state encoding, legal hand generation, and policy evaluation.
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We do not just ship a feature list or a model demo. We connect business goals, model design, engineering systems, and deployment constraints into work that can be accepted, operated, and maintained.
AI engineering budgets depend on the business goal, data quality, model difficulty, deployment environment, and integration depth. These ranges help overseas clients quickly understand project scale.
Final pricing depends on data volume, model complexity, deployment constraints, timeline, and support requirements.
We use a staged process so clients can confirm data quality, model direction, and core effect before committing to full system development and deployment.
Each phase has clear outputs: proposal, PoC results, prototype, API docs, deployment docs, test reports, or production service.
Clarify the business objective, data type, operating scenario, deployment environment, budget range, and acceptance criteria.
Review anonymized sample data to estimate model performance, data quality, engineering difficulty, and project risk.
Define the technical route, project scope, deliverables, timeline, acceptance method, and budget range.
Validate the core algorithm and workflow on a focused dataset, producing demoable or testable results.
Build model services, APIs, async tasks, callbacks, monitoring, logs, private deployment, and integration tests.
Deliver deployment docs, API docs, operating instructions, and post-launch maintenance or optimization support.
We usually use staged delivery and staged payments to reduce risk on both sides.
For PoC or short projects, a 50% kickoff payment and 50% delivery payment is common.
For larger systems, payment milestones can be aligned with kickoff, initial acceptance, final acceptance, and maintenance.
A short brief is enough at the beginning. Sample data can be anonymized.
Send the business goal, sample data type, existing systems, deployment environment, and expected timeline. We can help decide whether a PoC, prototype, or production delivery is the right next step.