Three Services.
Clear Scope.
Each service addresses a distinct need. Pick the one that fits your current stage, or start with a PoC to find out if a larger engagement makes sense.
← Back to HomeHow We Approach Every Engagement
Regardless of which service you select, every Cortexium engagement follows the same foundational process. This consistency is what allows us to make reliable commitments about timeline and outcome.
The process is designed to surface unknowns early — before they become problems — and to ensure you have everything you need to continue the work independently after handover.
Scoping
Define the problem, data availability, and success criteria in writing.
Data Assessment
Evaluate what data exists, its quality, and what access is needed.
Development
Build the solution with weekly progress updates and open communication.
Handover
Code repository, documentation, and a closing session with your team.
Conversational Analytics
We build an analytics layer on top of your existing chatbot, voice assistant, or messaging platform. The service surfaces insights about user intent, conversation quality, and resolution patterns — information that's often sitting in your logs but never structured for decision-making.
Suitable for customer service, sales enablement, and product teams that rely on conversational interfaces and want to understand how well those interfaces are actually working.
What's Included
- Conversation tagging and categorisation framework
- Intent clustering and frequency analysis
- Funnel and resolution rate analysis
- Reporting dashboard with key metrics
- Technical documentation and handover session
Delivery Steps
- 1. Platform integration and data export assessment
- 2. Tagging schema design and conversation labelling
- 3. Intent model training and cluster validation
- 4. Dashboard build and metric configuration
- 5. Client review, adjustments, and handover
- Organisations running live chatbots or voice assistants
- Customer service teams with high conversation volume
- Product teams measuring conversational interface quality
- Healthcare networks with patient data across facilities
- Financial consortia with inter-organisational data collaboration
- Multi-branch enterprises with distributed operational data
Federated Learning Implementation
A service for organisations that need to train AI models across distributed data sources without centralising sensitive information. We design the federation architecture, handle secure aggregation protocols, and manage the training coordination across nodes.
This is the appropriate choice when data cannot leave its source — due to regulatory requirements, contractual obligations, or organisational policy — but you still want a globally trained model.
What's Included
- Federation architecture design and documentation
- Secure aggregation protocol implementation
- Node configuration and training coordination setup
- Model evaluation across distributed data
- Deployment guide and full technical handover
Delivery Steps
- 1. Node inventory and data schema assessment
- 2. Federation architecture design and client sign-off
- 3. Secure aggregation protocol implementation
- 4. Training coordination and model convergence validation
- 5. Model evaluation, optimisation, and documentation handover
AI Proof-of-Concept Sprint
A compressed engagement designed to validate a specific AI hypothesis. We scope a tightly defined problem, build a functional prototype using your existing data, and present findings with clear go/no-go recommendations. Ideal for decision-makers who want evidence before committing to full-scale projects.
The Sprint is structured to be standalone. You're not obligated to continue into a larger engagement — the output stands on its own as a technical decision-support document.
What's Included
- Problem scoping and feasibility assessment
- Functional prototype using existing data
- Evaluation against defined success criteria
- Final presentation with go/no-go recommendation
- Technical documentation for the prototype
Delivery Steps
- 1. Hypothesis definition and scope agreement
- 2. Data availability and quality check
- 3. Prototype development and internal testing
- 4. Results evaluation against agreed criteria
- 5. Presentation and written recommendation delivery
- Decision-makers with a specific AI hypothesis to test
- Teams uncertain whether their data is sufficient
- Organisations wanting evidence before budgeting larger projects
Which Service is Right for You?
| Feature | PoC Sprint MYR 1,800 |
Conv. Analytics MYR 4,200 |
Federated Learning MYR 8,700 |
|---|---|---|---|
| Duration | 2–3 weeks | 5–8 weeks | 10–16 weeks |
| Working prototype/deliverable | |||
| Analytics dashboard | |||
| Privacy-preserving model training | |||
| Go/no-go recommendation | Context-dependent | Context-dependent | |
| Full technical documentation |
Fixed-Price Services in MYR
- Problem scoping
- Functional prototype
- Go/no-go recommendation
- Technical documentation
- Final presentation
- Intent clustering and tagging
- Funnel analysis
- Reporting dashboard
- Technical documentation
- Handover session
- Federation architecture design
- Secure aggregation protocols
- Node training coordination
- Model evaluation
- Full technical handover
Not Sure Which Fits?
Tell us what you're working with and we'll suggest a starting point. There's no obligation — just a straightforward technical conversation.
Get in Touch