Engineering Intelligence

From Chaos
To Intelligence

We don't sell magic boxes. We sell engineered systems. Building the data infrastructure and custom models to turn AI into a reliable business asset.

Data Engineering
Predictive Models
Custom Integration

Engineers, Not Wrappers.

The market is flooded with "API Wrappers" that just send prompts to ChatGPT. We build context-aware systems. We own the dirty work: ingestion, cleaning, and training.

The 80% Rule

AI is 20% model and 80% data engineering. Before we build the brain, we build the pipeline that feeds it. Garbage in, garbage out.

The XY Problem

Clients often ask for X (a chatbot) when they have problem Y (poor search). We diagnose the root cause before writing a single line of code.

Production Ready

We don't deliver Jupyter Notebooks. We deliver Docker containers, API endpoints, and dashboards capable of handling 10,000 requests/minute.

Engineers, Not Wrappers.

The market is flooded with "API Wrappers" that just send prompts to ChatGPT. We build context-aware systems. We own the dirty work: ingestion, cleaning, and training.

The 80% Rule

AI is 20% model and 80% data engineering. Before we build the brain, we build the pipeline that feeds it. Garbage in, garbage out.

The XY Problem

Clients often ask for X (a chatbot) when they have problem Y (poor search). We diagnose the root cause before writing a single line of code.

Production Ready

We don't deliver Jupyter Notebooks. We deliver Docker containers, API endpoints, and dashboards capable of handling 10,000 requests/minute.

Engineers, Not Wrappers.

The market is flooded with "API Wrappers" that just send prompts to ChatGPT. We build context-aware systems. We own the dirty work: ingestion, cleaning, and training.

The 80% Rule

AI is 20% model and 80% data engineering. Before we build the brain, we build the pipeline that feeds it. Garbage in, garbage out.

The XY Problem

Clients often ask for X (a chatbot) when they have problem Y (poor search). We diagnose the root cause before writing a single line of code.

Production Ready

We don't deliver Jupyter Notebooks. We deliver Docker containers, API endpoints, and dashboards capable of handling 10,000 requests/minute.

Targeted Solutions

We don't use a hammer for every screw. Select your industry.

Common Pain Points

  • "Machine downtime"
  • "Human error in quality checks"
PredictionIoT

Predictive Maintenance AI

Predict breakdown 72 hours in advance using sensor data. Fix it during lunch, not during a rush.

VisionAutomation

Computer Vision Quality Control

Automate visual inspection to catch 99.9% of defects without slowing the line.

Enterprise Sustainability Intelligence

From Passive Reporting
To Active Decarbonization.

Modern ESG isn't about scrubbing spreadsheets for a PDF report. It's about data engineering. We implement an AI-driven intelligence layer that sits on top of your ERP and IoT feeds to automate the math and find carbon leaks in real-time.

The 'Glass Box' Principle

We strictly separate AI reasoning from math. Our Deterministic Calculation Engine handles the numbers using audit-grade emission factors (eGRID, DEFRA), while AI handles the context.

The 'Watchdog' Engine

Stop reporting on emissions 6 months after they happen. Our anomaly detection engine spots 'Carbon Leaks' (e.g., HVAC spikes) in real-time, preventing compliance failures.

Strategic Scenario Planning

Don't just track history. Simulate the future. Ask the Platform: 'How does switching our APAC logistics fleet to EV impact our 2030 Net Zero trajectory?'

Current State (The Pain)
Reactive Reporting

Reporting on emissions 3–6 months after.

Fragile Spreadsheets

Manual data entry prone to broken formulas.

Opaque "Black Box"

"Trust us, this is the number" with no audit trail.

Upstatiq Future State
VALUE DRIVER
Proactive Intelligence

Real-time anomaly detection alerts.

Robust Pipelines

Automated ingestion with immutable audit trails.

Transparent Evidence

"Here is the number, and here is the invoice."

80% Reduction in Reporting Lead Time
Enterprise Sustainability Intelligence

From Passive Reporting
To Active Decarbonization.

Modern ESG isn't about scrubbing spreadsheets for a PDF report. It's about data engineering. We implement an AI-driven intelligence layer that sits on top of your ERP and IoT feeds to automate the math and find carbon leaks in real-time.

The 'Glass Box' Principle

We strictly separate AI reasoning from math. Our Deterministic Calculation Engine handles the numbers using audit-grade emission factors (eGRID, DEFRA), while AI handles the context.

The 'Watchdog' Engine

Stop reporting on emissions 6 months after they happen. Our anomaly detection engine spots 'Carbon Leaks' (e.g., HVAC spikes) in real-time, preventing compliance failures.

Strategic Scenario Planning

Don't just track history. Simulate the future. Ask the Platform: 'How does switching our APAC logistics fleet to EV impact our 2030 Net Zero trajectory?'

Current State (The Pain)
Reactive Reporting

Reporting on emissions 3–6 months after.

Fragile Spreadsheets

Manual data entry prone to broken formulas.

Opaque "Black Box"

"Trust us, this is the number" with no audit trail.

Upstatiq Future State
VALUE DRIVER
Proactive Intelligence

Real-time anomaly detection alerts.

Robust Pipelines

Automated ingestion with immutable audit trails.

Transparent Evidence

"Here is the number, and here is the invoice."

80% Reduction in Reporting Lead Time
Enterprise Sustainability Intelligence

From Passive Reporting
To Active Decarbonization.

Modern ESG isn't about scrubbing spreadsheets for a PDF report. It's about data engineering. We implement an AI-driven intelligence layer that sits on top of your ERP and IoT feeds to automate the math and find carbon leaks in real-time.

The 'Glass Box' Principle

We strictly separate AI reasoning from math. Our Deterministic Calculation Engine handles the numbers using audit-grade emission factors (eGRID, DEFRA), while AI handles the context.

The 'Watchdog' Engine

Stop reporting on emissions 6 months after they happen. Our anomaly detection engine spots 'Carbon Leaks' (e.g., HVAC spikes) in real-time, preventing compliance failures.

Strategic Scenario Planning

Don't just track history. Simulate the future. Ask the Platform: 'How does switching our APAC logistics fleet to EV impact our 2030 Net Zero trajectory?'

Current State (The Pain)
Reactive Reporting

Reporting on emissions 3–6 months after.

Fragile Spreadsheets

Manual data entry prone to broken formulas.

Opaque "Black Box"

"Trust us, this is the number" with no audit trail.

Upstatiq Future State
VALUE DRIVER
Proactive Intelligence

Real-time anomaly detection alerts.

Robust Pipelines

Automated ingestion with immutable audit trails.

Transparent Evidence

"Here is the number, and here is the invoice."

80% Reduction in Reporting Lead Time

The Upstatiq Lifecycle

We eliminate the "Black Box" fear with a transparent, pilot-first approach. We transition you from Hype AI to Production AI.

01

The Blueprint

1-2 Weeks

Discovery & Spec. We define Inputs, Outputs, and Success Metrics (Accuracy/Latency). No ambiguity.

02

The Pilot (MVP)

4-6 Weeks

We take a data slice and build a functional prototype to validate results with your experts.

03

Production Engineering

2-4 Months

Containerization, API Wrappers, Queue Management, and Security. The "Iceberg" beneath the model.

04

Scale & Maintenance

Ongoing

Active monitoring for Model Drift and automated retraining pipelines.

The Upstatiq Lifecycle

We eliminate the "Black Box" fear with a transparent, pilot-first approach. We transition you from Hype AI to Production AI.

01

The Blueprint

1-2 Weeks

Discovery & Spec. We define Inputs, Outputs, and Success Metrics (Accuracy/Latency). No ambiguity.

02

The Pilot (MVP)

4-6 Weeks

We take a data slice and build a functional prototype to validate results with your experts.

03

Production Engineering

2-4 Months

Containerization, API Wrappers, Queue Management, and Security. The "Iceberg" beneath the model.

04

Scale & Maintenance

Ongoing

Active monitoring for Model Drift and automated retraining pipelines.

The Upstatiq Lifecycle

We eliminate the "Black Box" fear with a transparent, pilot-first approach. We transition you from Hype AI to Production AI.

01

The Blueprint

1-2 Weeks

Discovery & Spec. We define Inputs, Outputs, and Success Metrics (Accuracy/Latency). No ambiguity.

02

The Pilot (MVP)

4-6 Weeks

We take a data slice and build a functional prototype to validate results with your experts.

03

Production Engineering

2-4 Months

Containerization, API Wrappers, Queue Management, and Security. The "Iceberg" beneath the model.

04

Scale & Maintenance

Ongoing

Active monitoring for Model Drift and automated retraining pipelines.

Pilot Program Open

Engineer Your
Competitive Edge.

Stop analyzing. Start building. Book a technical discovery session to map your data infrastructure to real-world AI capabilities.

Data Audit

We assess your data readiness before suggesting a model.

Model Selection

We choose the right architecture for your specific KPI.

Initialize Request

ID: #REQ-1602

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