Three areas where AI delivers specific, measurable value
Each offering is a defined engagement with clear scope, known duration, and fixed pricing. You know what you are getting before you commit.
Back to HomeHow every engagement is structured
Regardless of which service is engaged, the process follows the same structure — because the quality of the outcome depends on the quality of the foundation.
Data assessment
We evaluate the data you have — its structure, completeness, and whether it supports the intended AI capability. This stage prevents investment in work that the data cannot support.
Scoping and agreement
The deliverables, timeline, and technical approach are agreed in writing. Scope boundaries are stated clearly so there are no ambiguous areas that could expand later.
Build with iteration
The AI component is built in stages, with structured review points. Findings from early stages can inform adjustments to later ones — within the agreed scope.
Handover and transfer
All deliverables, documentation, and knowledge sessions are completed before the engagement closes. Your team leaves able to operate and maintain what was built.
Time Series Forecasting Consultation
An advisory service for organisations looking to apply AI-driven forecasting to temporal data — whether financial metrics, supply chain volumes, energy consumption, or user activity patterns.
The engagement includes an assessment of your historical data quality, selection of appropriate modelling approaches, pilot model development, and interpretation guidance. You receive a working forecast model along with documentation on its assumptions and limitations.
Designed for teams that want an honest view of what prediction can and cannot achieve for their specific data.
- Historical data quality assessment and gap identification
- Selection of modelling approach with written justification
- Pilot model development and performance evaluation
- Interpretation guide — how to read and use the forecast outputs
- Full documentation of assumptions and known limitations
Conversational AI Design and Build
Full design and development of a conversational assistant tailored to your specific domain and user needs. The service covers persona definition, dialogue flow architecture, intent and entity modelling, integration with your backend systems, and thorough user testing.
Built on modern language model infrastructure with careful attention to tone, accuracy boundaries, and graceful failure handling. The assistant is trained on your domain knowledge and comes with a management interface for ongoing content updates.
Six to ten-week engagement with post-launch tuning support.
- Persona definition and tone design appropriate to your users
- Dialogue flow architecture and failure handling design
- Intent and entity modelling from your domain knowledge
- Backend system integration and user acceptance testing
- Management interface and post-launch tuning period
Enterprise Knowledge Graph Construction
Design and population of a knowledge graph that maps the relationships between concepts, entities, and processes within your organisation. The service includes ontology design, entity extraction from your existing documents and databases, relationship mapping, and query interface development.
The resulting graph becomes a foundation for AI applications such as intelligent search, contextual recommendations, and automated reasoning. Particularly suited for organisations with complex, interconnected information landscapes.
Delivered over ten to fourteen weeks with iterative validation sessions.
- Ontology design tailored to your organisational knowledge structure
- Entity extraction from existing documents and databases
- Relationship mapping with iterative validation by domain experts
- Query interface for exploring the graph from your applications
- Integration guidance for search, recommendations, and reasoning use cases
Which service fits your situation
The three offerings address different data challenges and require different starting conditions. This comparison may help you identify which is the most relevant starting point.
| Aspect | Forecasting SGD 560 |
Conv. AI SGD 1,740 |
Knowledge Graph SGD 2,680 |
|---|---|---|---|
| Best for | Teams with historical time-series data wanting AI-based forecasting | Organisations needing domain-specific conversational interfaces | Organisations with complex, interconnected knowledge assets |
| Duration | 3–4 weeks | 6–10 weeks | 10–14 weeks |
| Data requirement | Historical numerical records | Domain knowledge, user scenarios | Existing documents, databases |
| Primary output | Working forecast model + documentation | Deployed assistant + management interface | Populated graph + query interface |
| Technical complexity | Moderate | High | High |
Technical protocols shared by every engagement
Data privacy and PDPA compliance
Mutual NDA signed before any data access. Processing procedures follow Singapore PDPA requirements throughout.
Version control and reproducibility
All code and pipeline configurations are version-controlled. You receive the full repository history, not just the final state.
Structured testing before handover
Deliverables are tested against agreed criteria before the engagement closes. Test results are included in delivery documentation.
Limitation documentation
All deliverables include explicit documentation of known failure modes, out-of-distribution conditions, and appropriate use boundaries.
Knowledge transfer sessions
Dedicated sessions with your technical team before sign-off ensure the system can be operated and maintained independently.
Full intellectual property transfer
Complete ownership of all deliverables, code, and models transfers to you at engagement close. No ongoing licensing or access dependencies.
Which service matches your current data challenge?
We are glad to talk through your situation and help you identify whether one of these engagements is a sensible fit — or whether the timing is not quite right yet.
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