We build custom AI solutions that solve your toughest technical challenges—from fine-tuned language models to smart automation and decision engines. With responsible AI built in, our work goes beyond proof-of-concept: it becomes part of your long-term product DNA.
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Machine learning & LLM fine-tuning
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API & model integration
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Automation & decision systems
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Responsible AI frameworks
At Devin.no, Custom AI Development is where we turn advanced theory into production-ready systems. If you have a unique business process, internal data, or a vision for automation, this is the space where we engineer the custom model, pipeline, or integration that delivers real impact.
Here’s how we think about it:
Model readiness & fine-tuning: We don’t stop at picking a model. We adapt it to your data, your domain, optimize performance, ensure it behaves well.
Seamless integration: Whether it’s integrating via APIs, embedding into your backend, or deploying to cloud/edge, we plan for maintainability, latency, security.
Automation & Decision Systems: From workflows automation to real-time decision logic, we build systems that offload manual work, reduce errors, and scale.
Responsible AI practices: We don’t cut corners. Bias mitigation, transparency, auditability, observability — these are part of our process, not add-ons.
You’ll get custom AI infrastructure that isn’t just clever—it’s usable, safe, scalable. From picks & pipelines to integrations & governance, we handle the heavy lifting so you can focus on what matters: delivering value to users.
Model evaluation & comparison report: accuracy, latency, cost trade-offs, domain suitability
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Fine-tuned model(s) on your data (LLM / ML), plus baseline models for comparison
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API / service plug-ins for model endpoints (e.g. REST / GraphQL), or model embedding in existing systems
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Automation logic / decision-engine design: define decision rules, thresholds, fallback logic
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Deployment & scaling plan (cloud, edge, hybrid), including monitoring, latency & reliability constraints
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Responsible AI framework docs: bias & fairness audit, privacy & security assessment, explainability tools, logging & transparency
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Testing suite & evaluation: unit, integration, performance tests; real-world scenario validations
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Handoff package: model weights (if relevant), code, docs, usage examples, and developer guide
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