Production AI That Actually Works
You get AI systems meant to run in production, not to demo. From custom models to LLM-powered features using Claude and GPT, everything is engineered for production reliability — with the approach matched to your problem, whether that is a simple API integration or a custom-trained model.
The problem
Why ai projects stall
Most AI initiatives stall somewhere between a promising prototype and a production system that stakeholders can rely on. Teams build impressive demos that fall apart under real-world data, edge cases, and scale. Meanwhile, the explosion of AI capabilities has created a confusing landscape where companies struggle to distinguish genuine opportunities from hype and often either over-invest in custom models when an API call would suffice or under-invest in fine-tuning when generic models fall short.
The approach
How I do it differently
Your AI work crosses from research into production on a systematic path. It opens with a thorough assessment of your data and business objectives, settles the right approach — custom model, fine-tuned foundation model, or API integration — then engineers a complete pipeline from data ingestion through monitoring. The tooling stays technology-agnostic: the right tool for your job, whether that is a lightweight automation, a pre-trained model, or a fully custom solution, all deployed with the observability and guardrails production systems demand.
Process
How a ai engagement runs
- 01
Data Assessment
Audit existing data assets, identify gaps, and define success metrics. Determine whether a custom model, fine-tuned model, or API integration best fits the problem.
- 02
Model Selection
Evaluate candidate architectures and foundation models against your data and accuracy requirements. Build proof-of-concept benchmarks to validate the approach.
- 03
Training
Train, fine-tune, or configure the selected model with rigorous experiment tracking. Iterate on data augmentation, hyperparameters, and prompt engineering until targets are met.
- 04
Integration
Deploy the model behind a production API with proper error handling, rate limiting, and fallback strategies. Connect to your application with clean interfaces.
- 05
Monitoring
Implement drift detection, accuracy tracking, and automated alerting. Establish a retraining cadence and feedback loop to keep model performance stable over time.
What you get
Deliverables
Other services
SaaS MVP Development
Go from idea to production-ready SaaS platform in 8-12 weeks with a modern, scalable architecture.
Flutter Cross-Platform Apps
Ship beautiful, performant apps to iOS, Android, and web from a single codebase — without tripling your budget.
Cloud Architecture
Design and implement scalable, secure cloud infrastructure that grows with your product — built on the cloud tools your team can actually operate.
Ready to build your ai project?
30 minutes, free, for teams with a real project in motion. I confirm every request within 24 hours.
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