Turn trusted SP data into approved action with governed AI
See how we would take one SP workflow from evidence to production, with proposed use cases, a reference architecture, and a clear delivery plan. Enter your business email to access the briefing.
Prepared for SP Digital and SP Group. Not for wider distribution.
Briefing for SP Digital
Turn trusted SP data into approved action with governed AI
Our AI-native engineers work alongside SP teams to take one high-value workflow into production. Human approval stays built in, and fees are tied to measurable results.
Shortfall flaggedDelivery below schedule in the 14:00 to 14:30 window. Raised by the VPP platform.
Evidence retrievedDispatch logSCADA historian, read‑onlyMaintenance historyOnly sources you may read
Draft investigationTwo sites under-delivered. Historian data shows one inverter offline. Recommend an inspection work order.
Approval needed from the asset engineer before a work order is raised.
Approved
Work order WO-5531 createdEvidence pack saved to the case.
Message Mekong OS
What We Read About SP
Why enterprise AI is hard to move into production at SP
Data must stay within SP's environment
DAP runs on‑premises with Cloudera and Talend. Connected systems must comply with the CII cybersecurity code, while SP's data leaders continue to prioritise AI sovereignty.
Promising agents still need a path to production
SP already builds LLM, RAG and agent workflows. However, we found no public standard for runtime controls, approvals or workflow orchestration. Without these foundations, pilots can stall before production.
Five business areas share one platform
Asset management, customer service, sustainability, corporate productivity, and engineering delivery all depend on DAP. Each use case brings its own connectors, business definitions, and model operations.
Sandbox rules require a clear evidence trail
SP's 15 MW VPP pilot operates within EMA's two-year sandbox. Delivery shortfalls, penalties, and compliance evidence must be investigated and followed through. Much of that work appears to remain manual.
Who We Are
AI-native engineering for systems that cannot afford guesswork
AI-native engineering
AI agents build against clear specifications. Senior engineers define the acceptance criteria, review every change, and approve each release.
30-50%Faster time to market
40-60%Less rework
90%+Of requirements covered by tests
Enterprise engineering
We connect the systems you already run while meeting their security, operating, and compliance requirements.
Energy and infrastructure experience
Our team brings more than 12 years of experience with Siemens Mobility and Yunex Traffic, alongside work in energy trading and industrial AI.
Forward-deployed AI pod
Engineers work alongside SP's process owners in Singapore, supported by AI, data, security, integration, and QA specialists in Hanoi. One delivery lead remains accountable throughout.
Outcome-based delivery
A fixed fee covers delivery. Once the agreed KPI reaches its target, an outcome bonus applies for that measurement period.
Offerings
From one SP workflow to a measurable result
Two services run on one supporting platform. DAP remains SP's programme. We strengthen and extend it without replacing it.
Private and sovereign AI
Search, drafting, and approved workflows run within SP's environment. Models, data, and retrieval remain on‑premises, in a private cloud, or air‑gapped.
Clear accountability. Every approval has a named business owner.
Controlled system updates. Agents update business systems only after the required approval.
Measured value. Results are reported against the baseline agreed during discovery.
Mekong OS
Mekong OS connects SP's source systems to approved actions through seven governed layers. The architecture shows what SP could retain, where we could integrate, and what discovery still needs to confirm.
Existing systems stay in place. Mekong OS works with SP's current platform and source systems.
Metrics keep one definition. Reusable data products give each KPI a consistent meaning.
Delivery follows a clear sequence. SP receives a scoped and costed path from discovery to production.
Eastgate's Own Solution
Mekong OS turns business knowledge into approved action
The Mekong carries water across six countries before reaching Vietnam's delta. Mekong OS follows the same idea: knowledge flows through one governed system and emerges as work that people can review, approve, and use.
What flows in
Documents
Business systems
Company knowledge
Business context
Sources join a shared organisational context while retaining their own permissions and access rules.
Agent workflows
Agents and people share each step of the workflow, including tasks, approvals, and system updates.
Workflow controls
Policies, approval rules, and evaluation criteria govern the workflow. Every step leaves an audit record.
Approved actions
Once approved, decisions become permitted system updates. The outcome is recorded and measured.
What flows out
Decisions
System updates
Business outcomes
Mekong OS Reference Architecture
How Mekong OS could fit SP's data and AI platform
Explore the architecture one layer at a time, from SP's existing source systems to the applications people use. Each layer shows what SP could provide, where Eastgate could integrate, and what discovery must confirm.
Supported programme areas
Asset managementCustomer serviceSustainability solutionsCorporate productivityEngineering and delivery
What is ready now, and what we would build with SP
Engagement Model
Start with one workflow. Expand when the results support it.
Discovery
Choose one workflow, identify its users, map its systems and permissions, and measure the current baseline.
Output a workflow map, baseline, and focused pilot scope
Focused pilot
Build the agreed scope and test it against the acceptance criteria defined during discovery.
Output a working workflow with measured results
Production delivery
Agree on ownership, security requirements, operational support, rollout, and ongoing measurement.
Output a production rollout plan and support model
One delivery team across Singapore, Hanoi, and SP
Onsite in SingaporeA delivery lead and forward-deployed engineers work directly with SP's process owners.Engineering hub in HanoiAI, integration, data, security, and QA engineers build against the agreed specification.Your team at SPThe workflow owner confirms acceptance. SP's platform team approves the design and operates the production service.
Choose the commercial model that fits the work
Architecture and sizing
Define the deployment architecture, GPU and storage requirements, security controls, and costed delivery plan.
Fixed price
Forward-deployed engineers
Embed engineers with SP's team to take one selected use case into production.
Fixed fee, then outcome-based
Project delivery
Deliver a defined workflow against agreed milestones and acceptance criteria.
Fixed price per milestone
Dedicated AI engineering team
Build and operate additional workflows with a stable, long-term engineering team.
Time and materials or retainer
How outcome-based fees work
Before deployment, we agree on the baseline, target, and measurement data. For example, if a maintenance incident takes 30 hours to close today, the target could be 20 hours. The fixed fee covers delivery; an outcome bonus applies only in periods when the KPI meets or exceeds its target.
Target 20 h
30Baseline
28Q1
23Q2
19Q3
17Q4
Fixed fee onlyFixed fee plus outcome bonus
Tailored Use Cases
Four energy workflows worth exploring with SP
These are discussion hypotheses based on SP's publicly described DAP programme areas. They are not confirmed requirements or delivered SP results.
From employee request to approved system update
Retrieve only the information an employee may access. Recommend an action, route it for approval, and update the business system with a record of every step.
Ask using the employee's permissions
Retrieve authorised information
Prepare a recommendation with cited sources
Route for human approval
Update the business system
Controls
Permission-aware retrieval. The employee's permissions remain in force from search through action.
Resumable workflows. Work continues after approval or returns safely after an interruption.
Complete records. Every action and supporting item of evidence is logged.
Systems
Identity and access management
Document and knowledge stores
SAP and Oracle business systems
Proposed measures
Number of manual handoffs
Approval turnaround time
Total completion time
Audit-record completeness
Turn an asset issue into an approved work order
Bring the relevant asset records together, prepare a recommended action, route it for engineering review, and track the resulting work order to closure.
Raise an issue against an asset
Gather historian and maintenance records
Draft the recommended action
Route for engineering review
Create and track the approved work order
Controls
Named engineering review. Each asset class has a responsible reviewer.
Source-backed recommendations. Every recommendation includes the records that support it.
Approval before action. No work order is created without the required approval.
Systems
Enterprise asset and work management
Read-only SCADA and ADMS historians
GIS and network models
Proposed measures
Investigation time
Time spent gathering information
Work-order preparation time
Closure‑tracking completeness
SCADAADMSEAMOPC UADNP3GIS
Investigate VPP shortfalls with a complete evidence trail
When the VPP platform reports a delivery shortfall, gather the authorised evidence, draft the investigation, and track the follow‑up. Bidding, real-time dispatch, and settlement remain within the specialist systems.
Detect the shortfall in the VPP platform
Retrieve authorised evidence
Draft the investigation
Route the findings for approval
Create a work order
Track the issue to resolution
Controls
Read-only operational access. The workflow reads VPP data without controlling dispatch.
Approval outside response windows. Follow-up approval never sits inside 9‑second or 60‑second operational response windows.
Compliance evidence. Each investigation produces an evidence pack for sandbox review.
Systems
VPP platform
DAP data
Work-management system
Proposed measures
Evidence-assembly time
Investigation turnaround
Follow-up completion
End-to-end traceability
VPPDERBESSEV chargersEMA sandboxMeasurement and verification
A DAP-to-VPP interface and the delivery boundary with Univers have not been described publicly. Both require confirmation.
Put forecasts and failure predictions in front of planners
Models on DAP identify possible asset failures or changes in load and solar output. The workflow gathers the evidence behind each signal and routes it to a planner, who decides the next action.
Flag a risk or deviation
Retrieve telemetry and maintenance history
Explain the contributing factors with sources
Route the evidence for planner review
Plan a change or inspection order
Controls
Evaluated models. Models are versioned and assessed before use.
Human planning decisions. People approve planning changes. Models do not control real-time dispatch.
Read-only operational access. OT data is accessed through historians.
Univers memorandum of understanding for the VPP platform
The announced scope covers design, integration, testing, and scaling. It does not publicly define responsibility for forecasting, bidding, or settlement.
SP's data science leadership joined a panel on AI sovereignty in the Asia-Pacific region.
Singapore International Energy Week
SP is a sponsor, and its Group CEO is among the announced speakers.
Future of the Grid at Sands Expo
SP speakers are scheduled to discuss AI-driven asset management and distributed energy resource integration.
SP programmes that may shape the opportunity
FUSION.AI. This group-wide workforce AI project reportedly redesigned more than 400 roles and saved over 300,000 person-hours, according to SP's 2026 annual report.
CoRe+. The programme covers corporate digitisation and AI, including HR Buddy and Compliance AI.
Smart metering. SP operates more than 1.72 million smart meters through an Itron head-end.
Questions to answer with SP
Which DAP workflow already has a clear owner, defined users, and a measurable baseline?
Which agent framework and workflow engine are SP's standards?
Is an interface between DAP and the VPP platform planned?
Where does the delivery boundary sit between SP, Univers, and the distributed energy resource management system?
Start with one workflow.
Two hours with your workflow owner and our architect. You leave with a pilot you can measure.