Three Focused Engagements. One Standard of Work.
Prismata offers three distinct AI engineering services — each scoped around a specific organisational challenge and delivered to the same engineering standard.
Back to HomeHow Every Engagement Works
Regardless of which solution you choose, the engagement follows the same structured process — ensuring clarity at every stage and a handover your team can actually use.
Scoping
Problem definition, environment assessment, and scope agreement before any work begins.
Design
Architecture and technical design reviewed and approved before implementation starts.
Build & Review
Staged development with defined checkpoints for direction review and scope alignment.
Handover
Full documentation, configuration templates, and operational guidance for your team.
AI Data Pipeline Architecture
A complete design engagement for organisations that need a robust, scalable data pipeline optimised specifically for machine learning workflows. This service addresses the structural gap that makes most AI initiatives underperform: the data infrastructure is not built to support the models sitting on top of it.
We assess your current source systems, design a pipeline topology that handles your data volumes with appropriate quality checkpoints, and deliver architectural blueprints and configuration templates your engineering team can execute immediately.
What's Included
- Source system assessment and data quality audit
- Pipeline topology design optimised for ML workflows
- Data quality checkpoints and validation framework
- Versioning strategy for training and inference data
- Monitoring framework design and alerting specifications
- Architectural blueprints and configuration templates
- Implementation guidance documentation for your engineers
Engagement Process
Discovery (Days 1–5): Source system review, current state documentation, and requirements alignment with your data and engineering teams.
Architecture Design (Days 6–18): Pipeline topology design, component selection, and checkpoint framework development.
Documentation & Handover (Days 19–25): Blueprint finalisation, configuration templates, and walkthrough with your engineering team.
This Engagement is Well-Suited For
- Organisations preparing to train or deploy ML models for the first time at scale
- Teams experiencing data quality issues that affect model performance in production
- Data teams that have outgrown ad-hoc ETL approaches and need a structured pipeline design
- Companies that want a reviewed architecture before committing to implementation
Industries We've Worked With
- Asset management and wealth advisory
- Insurance and reinsurance operations
- Private banking and corporate finance
- Healthcare administration and clinical operations
AI-Assisted Compliance Monitoring
An intelligent monitoring system that helps compliance teams track regulatory adherence by analysing documents, communications, and transaction patterns. Built with a clear design principle: the system surfaces relevant information and flags concerns — final determinations always remain with your compliance officers.
The service is suited for financial services, healthcare, and other regulated industries where compliance teams are managing growing document volumes with limited capacity for manual review.
What's Included
- Regulatory requirement mapping for your specific obligations
- Monitoring model design and alert logic configuration
- Audit trail implementation meeting regulatory standards
- Integration design for existing compliance workflows
- Human review interface and escalation path design
- System testing and compliance team walkthrough
Full-Stack AI Solution Development
A comprehensive engagement covering the design, development, and deployment of a complete AI-powered application from concept to production. This is the appropriate service when your organisation has a clear AI use case that needs integrated, dedicated execution — not a piecemeal approach.
The engagement includes twelve weeks of post-launch support as a standard component, allowing for iterative improvement based on actual usage. Each phase includes review gates where priorities and direction can be adjusted based on emerging insights from earlier phases.
What's Included
- User research and operational readiness assessment
- Architecture design and technology selection
- Model development, training, and evaluation
- Frontend and backend engineering
- Testing, staging, and production deployment
- Operational handover with full documentation
- Twelve weeks post-launch support and improvement
Phase Overview
Research & Design: User research, architecture design, and technology selection (Weeks 1–3)
Model Development: Training data preparation, model development, and evaluation (Weeks 4–7)
Application Build: Frontend, backend, and integration development (Weeks 8–13)
Launch & Support: Deployment, handover, and twelve weeks iterative support (Weeks 14–28)
Choosing the Right Engagement
Use this matrix to identify which service aligns with your current challenge and organisational readiness.
| Feature | Pipeline SGD 510 |
Compliance SGD 1,850 |
Full-Stack SGD 2,700 |
|---|---|---|---|
| Architecture design | |||
| Implementation included | |||
| Frontend application | |||
| Post-launch support | 12 weeks | ||
| Compliance audit trail | Optional | ||
| Handover documentation |
Best for
Pipeline Architecture
When you need a solid data foundation before building models
Best for
Compliance Monitoring
When regulatory document volumes are overwhelming manual review
Best for
Full-Stack Development
When you have a clear AI use case and need end-to-end delivery
Consistent Across All Solutions
Data Security
All engagements operate under strict data handling protocols. NDA and client-specific data governance requirements are accommodated as standard.
Performance Metrics
Deliverables include defined metrics for evaluating system performance and detecting degradation in production.
Direct Support
During the engagement, your team communicates directly with the engineers doing the work — no intermediaries.
Documentation Standard
All deliverables are documented to a standard that allows your internal engineers to maintain and extend what was built.
PDPA Compliance
Singapore PDPA requirements are considered at the design stage across all data-handling solutions.
Version Control
All code and configuration is version-controlled and reproducible, with clear documentation of decisions made at each stage.
Not Sure Which Solution Fits?
A scoping conversation usually clarifies the right starting point. Tell us what you're working with and where the friction is, and we'll give you a direct perspective.