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Custom AI Development
Custom models and systems for requirements that standard approaches, hosted models and packaged software do not meet.
About Custom AI Development
A general-purpose model or an available product does not serve every requirement: unusual data modalities, strict latency or cost envelopes, on-premises constraints or proprietary domains. Custom AI development addresses these cases with purpose-built models, pipelines and systems, combining techniques from more than one discipline where the requirement calls for it. We record constraints on data residency, hardware and latency as requirements before selecting any model.
A custom system has no vendor to maintain it, so we design it for the organization's own engineers to maintain. The design therefore provides reproducible training and inference pipelines, documented data dependencies and a defined retraining process. Training, where required, runs on pipelines those engineers can rerun after handover. We version each pipeline with the data and configuration behind a given model, so you can rebuild any model.
What you receive
- Requirement specification and constraint analysis
- Purpose-built model and training pipeline
- Evaluation set specific to the requirement
- Deployed system with operational documentation
When to engage
Engage once you have tested the requirement against available models and products and none of them meets it.
Custom AI Development case studies
Common questions about Custom AI Development
When does an organization need custom AI development?
Custom development is warranted once available models and products have been tested against the requirement and none of them meets it. Common reasons are unusual data types, strict latency or cost limits, on-premises constraints and proprietary domains.
Who maintains a custom AI system after handover?
Your own engineers. The system is designed for them from the start, and handover includes the documentation and runbooks they need to retrain, redeploy and change it without ByteForge.
Can a custom AI model run on-premises?
Yes. On-premises operation is treated as a requirement from the start, alongside the hardware and latency limits it brings. Models and pipelines are chosen to run within those limits on infrastructure you operate.
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Discuss this service on a call.
Describe the objective and the systems it affects, and we will outline how an engagement would proceed.