Three services, three distinct problems
Each Dataveil service addresses a different layer of the challenge of putting AI to work responsibly and effectively in Singapore.
Back to HomeHow Dataveil structures its work
Each engagement begins with a scoping call to confirm that the service is well matched to the organisation's current state and needs. Work begins only after scope, timeline, and deliverables are agreed in writing. This prevents misaligned expectations and keeps the engagement on course.
The three services can be taken independently or sequenced. An organisation might start with the privacy consultation to establish design boundaries, proceed to inference optimisation once models are in production, and later deploy the BI suite to make the outputs of those models accessible across the business.
Scoping Call
Understand your environment and confirm fit before proposing work
Written Scope
Deliverables, timeline, and price confirmed in writing before any work begins
Active Engagement
Work conducted with weekly check-ins and direct access to the lead practitioner
Structured Handover
Documentation, configuration files, and knowledge transfer session at close
Privacy-Preserving AI Consultation
An advisory engagement focused on designing AI solutions that respect data privacy constraints from the outset. The consultation covers privacy-by-design principles, differential privacy techniques, secure computation approaches, and compliance alignment with PDPA and GDPR. Particularly relevant for organisations handling personal, medical, or financial data.
The engagement delivers a technical guideline document tailored to your data environment and use cases, along with vendor evaluation criteria for privacy-preserving technologies. The document is written to be actionable — specific enough for your technical team to implement and clear enough for your compliance function to reference.
Key benefits
- Privacy risks identified and addressed at the architecture stage, not after deployment
- PDPA and GDPR compliance considerations documented in one structured reference
- Vendor evaluation criteria to guide technology selection with privacy as a criterion
- Differential privacy and secure computation techniques assessed for your use cases
Engagement process
Data environment review
Document review, architecture diagrams, and data flow mapping for your AI use cases
Regulatory exposure assessment
Identify PDPA obligations, GDPR applicability, and sector-specific requirements
Technical guideline document
Deliver tailored recommendations, technique selection rationale, and implementation guidance
Review session and Q&A
Walkthrough of the document with your technical and compliance teams
This engagement suits organisations that
- Handle personal, health, or financial data in AI workflows
- Are building new AI systems and want privacy considered at the design stage
- Need to assess existing AI systems against PDPA or GDPR requirements
- Are evaluating privacy-preserving technology vendors and need evaluation criteria
Real-Time AI Inference Optimisation
A technical service focused on improving the speed, cost-efficiency, and reliability of your production AI models. This engagement is designed for teams with working models that need to perform better under real production loads. Results are delivered as optimised model artifacts, updated deployment configurations, and a benchmarked performance comparison report.
The work covers model profiling, latency analysis, quantisation, pruning, batching strategy, caching architecture, and infrastructure right-sizing. Every change is measured before and after to establish a clear picture of what improved and by how much.
Key benefits
- Lower latency under production load conditions, measured against agreed benchmarks
- Reduced infrastructure cost through right-sizing and batching strategy improvements
- Optimised model artifacts delivered alongside updated deployment configuration files
- Before-and-after benchmarking report suitable for internal reporting or board review
Engagement process
Baseline benchmarking
Profile current model performance under representative production conditions
Bottleneck identification
Latency analysis, infrastructure assessment, and opportunity prioritisation
Optimisation implementation
Quantisation, pruning, batching, caching, and configuration changes applied and tested
Post-optimisation benchmarking and handover
Final measurement, report delivery, and knowledge transfer session with your team
This engagement suits organisations that
- Have AI models in production or approaching production readiness
- Are experiencing latency, throughput, or cost issues under real traffic
- Want to reduce cloud infrastructure spend without degrading model quality
- Need documented performance evidence for internal or regulatory reporting
AI-Integrated Business Intelligence Suite
Design and deployment of an analytics environment that augments traditional business intelligence with AI-driven features — including natural language querying, anomaly detection, automated insight generation, and predictive dashboard widgets. The service is built to make your existing data more accessible to a wider range of stakeholders.
The engagement covers data source integration, BI platform configuration, AI model embedding, user interface customisation, and role-based access setup. Includes training sessions for analysts and business users so the environment is understood and maintained after delivery.
Key benefits
- Business users can query data in plain language without SQL or technical training
- Anomaly detection reduces the time spent identifying issues in operational data
- Predictive dashboard widgets bring forward-looking context to existing reporting
- Role-based access ensures the right information reaches the right people
- Training sessions leave your team fully equipped to maintain and extend the environment
Engagement process
Data and platform assessment
Review data sources, existing BI platform, and stakeholder requirements
Architecture and integration design
Design AI feature layer and integration plan agreed with your technical team
Build and configuration
Data integration, AI model embedding, dashboard configuration, and access controls
User acceptance and training
Testing sessions, stakeholder training, and final documentation handover
This engagement suits organisations that
- Have significant data assets that are underused because access is too technical
- Want to extend an existing BI platform with AI capabilities rather than replace it
- Need business users and analysts to work from the same intelligence environment
- Are looking to reduce the time from data event to informed decision
Choosing the right engagement
A feature comparison to help you identify where to start based on your organisation's current situation.
| Feature | Privacy Consultation | Inference Optimisation | BI Suite |
|---|---|---|---|
| PDPA & GDPR alignment | — | — | |
| Model performance improvements | — | — | |
| Infrastructure cost reduction | — | — | |
| Natural language data access | — | — | |
| Performance benchmarking | — | — | |
| AI-augmented dashboards | — | — | |
| Technical guideline document | |||
| Team training sessions | — | — | |
| Price (SGD) | 420 | 1,180 | 2,280 |
All prices in SGD. Fixed engagement pricing — no variable additions.
Technical and professional standards across all engagements
Data confidentiality
NDAs standard. Client data never shared across engagements.
Regulatory alignment
PDPA, GDPR, and MAS TRM guidance reviewed for each engagement.
Measured claims only
All performance assertions supported by benchmarking data.
Scope integrity
What was scoped is what is delivered. Changes discussed openly.
Documented outputs
All deliverables written for long-term reference, not single-use reports.
Direct practitioner access
Senior engagement lead accessible throughout — not passed to a support team.
Know which engagement fits your situation?
Or not quite sure yet — that's what the scoping call is for. Share a few details and we'll work through it together.
Request a Consultation