About Altimeda
Combining years of digital transformation and sectoral industrial experience
ALTIMEDA: AI Lakehouse Technology for Intelligent Modern Enterprise Data Automation
Altimeda was founded by Carlos, Iqbal, and Samuel, bringing together combined expertise in digital transformation and sector-specific operations. This diverse background shaped Altimeda's practical, outcome-focused approach to enterprise transformation, especially for industrial clients.
Our unique blend of consulting and deep technical engineering, combined with extensive domain knowledge in mining, manufacturing, and property management, allows us to understand both C-suite imperatives and field realities.
Data Lakehouse Engineering
Unifying fragmented data estates for comprehensive insights.
Enterprise System Development
Replacing inefficient systems with robust, scalable platforms.
GenAI Operational Assistants
Augmenting human expertise with intelligent AI solutions.
8
Years of Experience
Leading AI & digital transformation
20+
Successful Projects
Across diverse industrial sectors
2
Operating Regions
Indonesia & Australia
We're building a future where industrial enterprises leverage cutting-edge AI and data capabilities, adapted for their unique complexities
Article 2
Activating Dark Data in Manufacturing
Unlocking Hidden Value from Your Factory Operations
Manufacturing dark data is the vast volume of operational information collected but never fully utilized across machines, systems, and records. Activating this data transforms factory operations, leading to improved OEE, reduced downtime, better quality yield, and proactive decision-making. This article targets manufacturing executives and operational leaders seeking to extract value from existing factory data without replacing core systems.
What Is Manufacturing “Dark Data”?
In manufacturing, dark data refers to information that is generated and stored but rarely analyzed, connected, or acted upon. It represents a significant untapped resource.
Common sources include:
- Machine and PLC logs, SCADA, and HMI event streams
- Historian time-series data
- Maintenance work orders, technician notes, and quality inspection results
- Production schedules, shift reports, energy usage, and operator comments
Why Dark Data Exists in Manufacturing
OT and IT Silos
Systems are designed for different purposes, leading to disconnected data models and ownership.
Context-Light Data
Millions of data points lack crucial context (product, batch, shift) to be truly meaningful.
Variable Data Quality
Manual logs, spreadsheets, and unstructured notes create inconsistencies across sources.
Lagging Indicators
Reporting often focuses on post-incident analysis rather than real-time or predictive insights.
Why Activating Dark Data Matters
Manufacturers that activate dark data consistently see improvements across four critical dimensions, driving competitive advantage.
Operational Efficiency
- Increased throughput
- Reduced micro-stoppages
- Improved changeover performance
Asset Reliability
- Earlier failure detection
- Better maintenance planning
- Reduced unplanned downtime
Quality & Yield
- Process drift identification
- Reduced scrap and rework
- Faster root-cause analysis
Energy & Cost Control
- Visibility into energy-intensive processes
- Identification of abnormal consumption
- Support for sustainability reporting
Core Principles for Activating Dark Data
Context Before Complexity
Raw sensor data needs context (machine, product, batch, shift) to become actionable insight.
Do Not Disrupt Production Systems
Implementations must read from OT systems without interfering with control loops or uptime.
Centralize Data, Not Systems
Focus on consolidating data into a common analytical model, not replacing existing OT systems.
Standardize Operational Definitions
Agree on consistent definitions for downtime, OEE components, and defects to ensure reliable analytics.
Progress from Visibility to Intelligence
Dark data activation is a journey from understanding 'what' is happening to predicting 'what will' and prescribing 'what should' be done.
Reference Architecture for Dark Data Activation
Our reference architecture outlines a structured approach to transform raw operational data into actionable intelligence and automated insights.
Phased Delivery Model
Phase 1: Visibility & Trust
Focus on connecting key data sources, establishing context, and delivering credible dashboards for immediate visibility. Success is when "Operations trusts the data."
Phase 2: Diagnosis & Optimization
Emphasis on root-cause analysis, cross-domain correlation, and reducing chronic losses. Success is when "Decisions are data-led, not opinion-led."
Phase 3: Prediction & Automation
Implementation of early warning systems, reduced manual monitoring, and embedded intelligence. Success is when "Issues are addressed before they become incidents."
How to Start (First 60 Days)
Kickstart your dark data activation journey with these practical steps:
Step 1: Identify High-Value Use Cases
Focus on areas like chronic downtime lines, high scrap products, maintenance-heavy assets, or energy-intensive processes for maximum initial impact.
Step 2: Map Data Sources
Document existing data, its location, ownership, and quality to understand your current landscape.
Step 3: Define the Context Model
Establish standard definitions and calendars for machines, lines, products, and shifts to ensure data consistency.
Step 4: Build a Minimum Viable Data Platform
Ingest, contextualize, and visualize key data points. Prioritize trust and clarity over perfection in this initial phase.
Final Word:Manufacturers are sitting on years of untapped operational intelligence. Dark data is not a liability—it is latent value. By systematically connecting, contextualizing, and activating this data, manufacturers can move from reactive firefighting to proactive control, turn experience into institutional knowledge, and lay the foundation for automation and GenAI. It's about making operations visible, explainable, and ultimately optimizable.
Article 3
AI Assistants for Field Operations
Deploying GenAI Where Work Actually Happens
AI Assistants for Field Operations use GenAI to support supervisors, technicians, and crews at the point of work. These assistants sit on top of existing platforms to provide real-time guidance, answers, summaries, and decision support—using natural language, mobile-friendly interfaces, and governed operational data. They reduce administrative burden, improve compliance, speed up issue resolution, and capture site knowledge.
This article is for operations leaders, site supervisors, maintenance managers, safety teams, and digital transformation leaders who want to practically deploy GenAI for field-based workers—without disrupting critical operations or overwhelming users.
What Are AI Assistants for Field Operations?
AI Assistants are task-focused GenAI capabilities embedded into day-to-day workflows for mobile and site-based workers. They are not generic chatbots, but context-aware operational assistants that:
- Understand the job, site, asset, or task
- Use approved documents and data as sources of truth
- Provide fast, relevant answers and guidance
- Reduce the need to search systems, manuals, or emails
- Capture structured and unstructured knowledge from the field
Typical interaction examples:
- “What are the prestart requirements for this site?”
- “Summarise today’s daily diary before I submit it.”
- “Why was this job delayed yesterday?”
- “What does the SOP say about isolating this asset?”
- “Draft a variation note based on today’s events.”
Why Field Operations Are Ideal for GenAI
Field operations are information-heavy but time-poor. This creates common challenges where GenAI can provide significant value.
Common challenges in the field:
- Workers operate across multiple systems and documents
- SOPs and manuals are long and rarely read on-site
- Compliance steps are skipped under time pressure
- Knowledge lives in people’s heads, not systems
- Reporting is often delayed or incomplete
Why GenAI fits:
- Interpreting unstructured information
- Summarising and explaining content
- Responding in natural language
- Adapting to different user skill levels
- Operating through simple chat-style interfaces
For field teams, this means less searching, less typing, and fewer mistakes.
What Problems AI Assistants Solve in Field Operations
AI Assistants directly address critical pain points in field operations, leading to more efficient, compliant, and knowledgeable teams.
Reducing administrative burden
Field workers spend a disproportionate amount of time on reports, explanations, forms, and procedure searches. AI assistants can pre-fill daily diaries, draft site notes, and convert voice notes into structured entries, saving valuable time.
Improving compliance and safety
Compliance failures often stem from lack of clarity or access to the right documents. AI assistants instantly answer SOP and safety questions, guide users through checks, and highlight missing items before submission, reinforcing correct procedures.
Capturing operational knowledge
Much field knowledge is tacit and temporary. AI assistants capture explanations, lessons learned, and site notes, structuring unstructured inputs to make past knowledge searchable and reusable for future teams.
Speeding up issue resolution
When issues arise, workers often rely on memory or calls, leading to delayed root cause identification. AI assistants summarise site activity, surface similar past issues, and explain likely causes and next steps based on approved knowledge.
Where AI Assistants Fit in the Field Operations Value Chain
Pre-Start & Mobilisation
- Use cases: “What inductions are required for this site?”, “Are all tickets and permits valid today?”, “Summarise today’s planned activities and risks.”
- Impact: Faster mobilisation, fewer missed compliance steps, clearer daily alignment.
Execution & Daily Work
- Use cases: “What’s the correct procedure for this task?”, “Explain the quality checklist in simple steps.”, “Log today’s activities based on these notes.”
- Impact: Consistent execution, reduced errors and rework, less manual data entry.
Reporting & Daily Diaries
- Use cases: “Summarise today’s diary.”, “Rewrite this diary entry to be clearer for the client.”, “Highlight any risks or delays in today’s work.”
- Impact: Higher-quality reporting, faster submission, stronger evidence for claims and disputes.
Variations, Issues & Claims Support
- Use cases: “Draft a variation note from today’s events.”, “What evidence do we have for this delay?”, “Summarise relevant contract clauses.”
- Impact: Earlier identification of commercial impacts, better documentation, reduced leakage and disputes.
Maintenance & Asset Operations
- Use cases: “What does the SOP say about isolating this equipment?”, “Summarise recent maintenance history for this asset.”, “What are common failure causes for this fault?”
- Impact: Faster troubleshooting, improved asset reliability, safer interventions.
Design Principles for Field AI Assistants
Successful AI assistant deployments adhere to core design principles that ensure trust, usability, and effectiveness in the field.
Ground the AI in trusted data
Assistants must only reference approved SOPs, controlled documents, verified operational data, and project-specific context. This prevents hallucination and builds user trust.
Context is everything
The assistant should understand the user's project, site, asset, task, and permissions. Context transforms generic AI into a precise operational tool, providing highly relevant guidance.
Mobile-first interaction
Field AI assistants must work seamlessly on phones and tablets, using short, clear responses, supporting voice-to-text, and minimizing typing and navigation for ease of use on the go.
Assist, don’t automate prematurely
Early-stage assistants should suggest, summarise, explain, and draft. Only later, with proven reliability, should they trigger workflows or automate actions, always with human approval.
Be explainable and auditable
Every AI-generated output should reference its source documents or data, be reviewable before submission, and be logged for audit and traceability, ensuring transparency and accountability.
Reference Architecture for Field AI Assistants
This architecture outlines the interconnected layers required to deploy robust and effective AI assistants in field operations.
Phased Deployment Model (What Actually Works)
A phased approach minimizes risk and builds trust, starting with low-impact capabilities and progressively adding more advanced functions.
Phase 1: Q&A and Summaries
Capabilities: Ask questions about SOPs, summarise diaries/notes, explain tasks in plain language. Success metric: “Field teams actually use it.”
Phase 2: Assisted Reporting & Guidance
Capabilities: Draft daily diary entries, highlight missing information, guide compliance steps. Success metric: “Reporting quality improves with less effort.”
Phase 3: Proactive Insights & Agentic Support
Capabilities: Flag anomalies/risks, suggest next actions, automate routine tasks with approval. Success metric: “Problems are prevented, not just reported.”
Governance, Safety & Adoption
Governance essentials:
- Clear boundaries on what AI can and cannot do
- Approved source lists and regular review of prompts/outputs
- Audit logs for all AI interactions
Adoption best practices:
- Train supervisors first
- Embed AI into existing workflows (not a separate app)
- Start with clear, practical use cases
- Measure time saved and error reduction
Tangible Outcomes Seen in Field Deployments
Organizations deploying field AI assistants consistently achieve measurable improvements across key operational areas:
- Reduced administrative time for supervisors and field staff
- Faster and clearer daily reporting, improving data quality
- Improved compliance adherence with safety and operational procedures
- Better quality site documentation, supporting accurate record-keeping
- Faster issue resolution, minimizing downtime and delays
- Stronger organizational knowledge capture, reducing reliance on individual memory
How to Start (First 30–60 Days)
Kickstart your field AI assistant journey with these practical, impact-focused steps:
1
Identify high-friction field tasks
Focus on daily diaries, prestart checks, SOP access, and incident notes for maximum initial impact and user adoption.
2
Curate trusted knowledge sources
Clean and approve SOPs, identify authoritative documents, and define clear data boundaries to ensure AI reliability.
3
Pilot with real users
Deploy to a small group of supervisors or technicians, observe usage, and refine prompts, prioritizing usability over initial sophistication.
4
Measure and expand
Track time saved and quality improvements, expanding to additional workflows once trust and value are established.
Final Thought:AI Assistants for Field Operations are not about replacing workers—they are about reducing friction, improving clarity, and supporting better decisions where work actually happens. When grounded in trusted data, designed for mobile use, and rolled out in phases, GenAI becomes a safety partner, a reporting assistant, a knowledge companion, and a force multiplier for field teams. The organizations that succeed will be those that deploy AI quietly, practically, and purposefully—making field work easier, safer, and more consistent every day.