AI Solutions

Three Approaches to
AI Challenges

Whether you need long-term research collaboration, ethical emotion recognition, or infrastructure optimization, we have a solution designed for your specific needs.

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Our Methodology

Every engagement begins with a thorough discovery phase where we work to understand your actual challenges, not just your stated requirements. We ask questions about your existing systems, your team's capabilities, your constraints, and your long-term goals. This initial consultation is always complimentary because we need to ensure there's a genuine fit before committing to work together.

Once we've established that we can help, we create a detailed project plan with clear milestones, deliverables, and success criteria. For research partnerships, this includes defined checkpoint meetings and publication timelines. For emotion recognition systems, it covers data collection, model training phases, ethics review, and deployment architecture. For infrastructure audits, it specifies profiling periods, analysis deliverables, and implementation support.

Throughout the engagement, we maintain open communication through shared documentation systems, regular review sessions, and asynchronous updates. You always know what we're working on, why we're making specific decisions, and how the project is progressing. We believe surprises belong in birthday parties, not professional services engagements.

Project completion includes comprehensive handoff — detailed documentation, code repositories with clear README files, deployment guides, usage instructions, and training sessions for your team. We also provide defined support windows (30-90 days depending on the service) where we remain available for questions and troubleshooting as you move toward independent operation.

Detailed Service Descriptions

AI Research Partnership

AI Research Partnership

RM 8,600

Long-term collaborative research engagements where the Inferix team works alongside your researchers to tackle open-ended problems. This includes novel model architectures, cross-domain transfer learning, few-shot adaptation, or domain-specific benchmark development.

Timeline: Structured in 3-month increments with defined checkpoints

Key Benefits

  • Dedicated research hours from experienced AI specialists
  • Access to shared experiment infrastructure and tools
  • Regular review sessions with joint decision-making
  • Co-authored publications and shared intellectual property

Research Process

  1. 01. Initial problem definition and literature review
  2. 02. Hypothesis formation and experiment design
  3. 03. Implementation, testing, and iteration cycles
  4. 04. Results analysis and publication preparation
  5. 05. Checkpoint review and next phase planning

Ideal For

Organizations with ambitious technical questions and the patience to explore them properly. This works well for research institutions, AI teams at technology companies, or product groups investigating new capabilities before full development.

Discuss Your Research
Emotion Recognition Systems

Emotion Recognition Systems

RM 5,500

Development of multimodal emotion detection systems that analyze facial expressions, voice tone, and text sentiment — individually or in combination. Includes culturally-aware training for Malaysian audiences and comprehensive ethics guidelines.

Timeline: 6-12 weeks depending on complexity and data requirements

Key Benefits

  • Culturally-aware model training for Malaysian contexts
  • Privacy-preserving deployment architecture
  • Comprehensive ethics review and usage policies
  • Multimodal analysis (facial, vocal, textual)

Development Process

  1. 01. Use case analysis and ethics review
  2. 02. Data collection protocol design
  3. 03. Model training with bias testing
  4. 04. Privacy-preserving architecture design
  5. 05. Deployment with usage guidelines

Ideal For

Customer experience research teams, accessibility application developers, interactive media creators, or educational technology companies seeking to build emotion-aware systems responsibly and ethically.

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AI Cost Optimization Audit

AI Cost Optimization Audit

RM 1,800

A focused review of existing AI infrastructure spend covering cloud compute costs, model serving efficiency, storage optimization, and resource utilization patterns. Includes detailed recommendations with estimated savings.

Timeline: 2-3 weeks to actionable insights

Key Benefits

  • Comprehensive infrastructure spend profiling
  • Model serving efficiency analysis
  • Detailed recommendations with estimated ROI
  • Fast turnaround with actionable insights

Audit Process

  1. 01. Infrastructure access and baseline profiling
  2. 02. Workload analysis and cost benchmarking
  3. 03. Opportunity identification and prioritization
  4. 04. Detailed recommendations report
  5. 05. Implementation guidance and support

Ideal For

Organizations with growing AI workloads wanting to ensure their infrastructure investments are well-calibrated. Typically identifies 15-30% cost savings within first quarter of implementation.

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Service Comparison

Feature Research Partnership Emotion Recognition Cost Optimization
Timeline 3+ months 6-12 weeks 2-3 weeks
Dedicated Team
Cultural Adaptation —
Ethics Review —
Publications Optional —
Infrastructure Access —
Post-Project Support Ongoing 90 days 30 days
Investment RM 8,600 RM 5,500 RM 1,800

Not sure which service fits your needs best?

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Technical Standards

Version Control

All code in Git repositories with clear commit histories, branching strategies, and comprehensive README documentation for handoff.

Testing Protocols

Automated testing suites, performance benchmarks, bias testing for ML models, and edge case validation across deployment scenarios.

Documentation

Architecture diagrams, API documentation, deployment guides, troubleshooting references, and knowledge transfer materials.

Security Practices

Encrypted data storage, secure API endpoints, access controls, audit logging, and compliance with Malaysian PDPA requirements.

Performance

Latency monitoring, throughput optimization, resource efficiency tracking, and scaling considerations for production deployment.

Continuous Improvement

Model retraining strategies, performance monitoring, feedback loops, and evolution plans for ongoing system refinement.

Ready to Get Started?

Let's have a conversation about your AI challenges and explore which solution might be the right fit.

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