Technical Services for Production ML Systems
We provide structured engineering assistance across three core areas: establishing reliable ML operations, designing conversational interfaces, and optimizing system performance.
Back to HomeCollaborative Engineering Methodology
Our engagements follow a structured process designed to deliver working systems while transferring knowledge to your team. We start with assessment to understand current state and identify specific friction points. Then we move into collaborative implementation where our engineers work alongside yours, sharing practices through direct experience rather than documentation alone.
Each engagement concludes with comprehensive handoff including technical documentation, operational runbooks, and review sessions where we walk through architecture decisions and troubleshooting approaches. The goal is your team's capability to maintain and evolve the systems independently after our work together finishes.
Machine Learning Operations
This service helps organizations build and maintain the infrastructure needed to run machine learning models reliably in production. We assess your current ML pipeline, identify bottlenecks and reliability issues, and implement improvements to model versioning, experiment tracking, deployment automation, and monitoring systems.
What You Receive
- MLOps maturity assessment identifying current capabilities and gaps
- Pipeline architecture design documentation with diagrams
- CI/CD implementation for models including testing workflows
- Monitoring setup tracking model performance and data quality
- Six weeks of operational support during initial deployment
Typical Timeline
8-12 weeks depending on existing infrastructure
Conversational AI Design
A collaborative engagement focused on designing and building conversational interfaces including chatbots, virtual assistants, and voice-based systems. We begin with user journey mapping and dialogue flow design, then move to intent modeling, response generation, and iterative testing with real user scenarios.
What You Receive
- User journey maps documenting conversation flows
- Dialogue design including example conversations and edge cases
- Intent classification model configuration and training
- Response generation system implementation
- Integration support with existing systems
- Testing and refinement cycle with user feedback
Typical Timeline
10-14 weeks including testing iterations
AI Performance Tuning
A focused technical engagement for organizations whose deployed AI systems are underperforming expectations or consuming more resources than necessary. Our specialists diagnose bottlenecks in model inference, data preprocessing, and serving infrastructure, then implement targeted optimizations.
What You Receive
- Comprehensive performance audit identifying bottlenecks
- Optimization implementation across inference and preprocessing
- Latency and throughput benchmarks before and after
- Cost analysis showing resource utilization improvements
- Recommendations report for sustained performance management
Typical Timeline
4-6 weeks for most systems
Choosing the Right Service
Each service addresses different stages and challenges in AI system development and operation.
| Feature | MLOps | Conversational AI | Performance Tuning |
|---|---|---|---|
| Best for | Teams moving to production | Building chat/voice interfaces | Optimizing deployed systems |
| Infrastructure setup | |||
| Model training support | |||
| Deployment automation | |||
| Monitoring implementation | |||
| UX/conversation design | |||
| Performance optimization | |||
| Cost reduction focus | |||
| Typical duration | 8-12 weeks | 10-14 weeks | 4-6 weeks |
| Starting price | ฿16,000 | ฿38,000 | ฿60,000 |
Consistent Engineering Practices
These standards apply across all our service engagements to ensure quality and maintainability.
Code Quality
All code follows style guides, includes comments explaining non-obvious logic, and undergoes review before deployment. We write code for maintainability by others.
Testing Coverage
Unit tests for data processing, integration tests for pipelines, and validation procedures for model behavior. Tests are documented and runnable by your team.
Security Practices
Credentials stored securely, data encrypted in transit and at rest, access logging enabled, and security considerations documented in architecture reviews.
Performance Baselines
Establish metrics before work begins, track improvements throughout engagement, and provide final report with quantitative evidence of changes.
Discuss Your Requirements
Schedule a consultation to explore which service would be most appropriate for your current situation and timeline. We provide straightforward assessments based on your specific technical context.