Build controlled enterprise AI systems, from models to agents
We provide enterprise model deployment, complete agent engineering, and custom model training so data, models, and business systems can become operational, verifiable, and continuously improvable capabilities within clear boundaries.
Choose on-prem, private-cloud, or hybrid deployment based on data, security, and infrastructure requirements.
Evaluate delivery against memory use, throughput, latency, task quality, and operational cost.
Preserve ownership and maintainability across models, runtimes, and agent workflows.
Enterprise Services
Each capability can stand alone or combine into an end-to-end path from industry models and enterprise inference runtimes to business agents.
Enterprise Model Deployment
Bring quantization and inference optimization into deployment so models run more efficiently, reliably, and controllably inside the enterprise boundary.
- Keep sensitive data, model assets, and inference logs under on-prem or private-cloud control.
- Use TyloQuant and HeadWiseKV to optimize quantization, memory, long context, and inference efficiency.
- Deliver service APIs, permissions, monitoring, benchmarks, upgrades, and operations paths.
Agent Engineering
Complete engineering from use-case design and tool integration to memory, permissions, evaluation, deployment, and operations.
- We have worked with multiple companies on agent projects from prototype validation to real business deployment.
- Build workflows, tools, knowledge, long-term memory, multi-agent coordination, and human approvals.
- Use permissions, evaluation, tracing, cost monitoring, and recovery to support continued operation.
Custom Model Training
Build a model asset that understands industry knowledge, private enterprise data, and specialized tasks.
- Define capabilities, data, and evaluation criteria for industry-specific or private enterprise models.
- Cover data governance, continued pretraining, SFT, preference alignment, distillation, and compression.
- Deliver training configs, checkpoints, evaluation reports, deployment plans, and iteration paths.
Delivery Model
Define business goals, data boundaries, hardware conditions, and success metrics.
Validate data, training, model deployment, and agent design on representative tasks.
Deploy the runtime, document operations and monitoring, and define the next iteration path.
Related Technology & Open Source
Our public research helps establish methods and boundaries. Enterprise delivery is re-evaluated for the actual model, hardware, and license.
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