Model behavior
Prompt and context design, retrieval, dataset work, fine-tuning and task-specific evaluation.
AI ENGINEERING
Model adaptation, AI integrations, agentic workflows and realtime game AI backed by evaluation, backend services and cloud operations.
We connect the model layer to data, tools, permissions, application logic and observability so the result can be measured, operated and improved.
DISCIPLINE SCOPE
Our AI discipline spans product integration, retrieval, model training and fine-tuning, agent orchestration and realtime game AI. We choose between deterministic software, retrieval, model adaptation and generative behavior according to the task.
Backend & Cloud is part of the offer: authentication, data, queues, workers, APIs, persistence, observability and cost controls are what turn useful model behavior into a dependable workflow.
Prompt and context design, retrieval, dataset work, fine-tuning and task-specific evaluation.
Tools, explicit state, branching, validation, human approval and user-facing workflow.
APIs, data, queues, workers, permissions, deployment, tracing and usage controls.
Decision systems, navigation, tactics and simulation under frame-time and multiplayer constraints.
TECHNICAL PRIORITIES
Specify the task, baseline, acceptable failure, data rights and human responsibility.
Build representative tests and compare the simplest viable approaches.
Connect models to product data, tools, state, permissions and deterministic controls.
Trace outcomes, measure quality, latency and cost and run regression evaluation before changes.
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