AI / ML Engineering
Build smart. From LLM integration to training custom models, we handle your AI workflows from concept to deployment.
Why?
AI hype is everywhere. We turn it into models that solve real problems—and run in production.
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Custom models that fit
Fine-tuning and retraining make off-the-shelf LLMs speak your domain fluently.
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Pipelines stay fresh
Automated data flows keep features and predictions up-to-date without manual crunching.
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Responsible AI guardrails
Bias checks and monitoring dashboards keep outputs fair, safe, and compliant.
Our process
Refine Problem Statement
Prediction goals, constraints, and business value are defined with domain experts.
Prepare Data Pipelines
Data is collected, cleaned, labelled, and version-controlled to feed training reliably.
Select and Train Models
Algorithms are benchmarked and hyper-parameters tuned to achieve optimal accuracy and efficiency.
Validate Performance
Accuracy, bias, and drift are measured on hold-out sets and real-world samples.
Deploy and Monitor
Models are served behind APIs or embedded, with real-time performance metrics tracked.
Schedule Retraining
Automated retraining and model registry practices keep predictions relevant as data evolves.
Questions?
TensorFlow, PyTorch, and HuggingFace—plus custom ONNX for edge deployment.
Yes—data is encrypted, and fine-tuning happens in a VPC-isolated environment.
We log drift, latency, and quality metrics, triggering retraining pipelines as needed.
4-6 weeks: data prep, baseline model, evaluation, and business impact demo.
