CODASOL Investor Introduction Portal
CODASOL converts 15+ years of industrial data knowledge into scalable AI-MDM intelligence for asset-intensive enterprises.
The Challenge We Solve
Poor industrial master data creates procurement, maintenance, inventory, compliance, operational, and AI-readiness issues. Enterprises need domain-specific data intelligence before automation and AI can deliver reliable outcomes.
CODASOL Substance
15+
Years of industrial data experience
100+
Completed projects
100M+
Material master records exposure
20M+
Asset records exposure
10M+
Supplier, Bill of Materials, service, and operational records exposure
Metrics reflect exposure to and processed industrial records only.
CODA-AI: Today and the Vision
Today, CODASOL combines industrial data experience, MDM delivery knowledge, and domain understanding from asset-intensive operating environments. The vision for CODA-AI is to convert that foundation into a scalable vertical AI-MDM intelligence layer for classification, enrichment, governance, deduplication, asset data quality, and MDM decision support.
CODA-AI is designed to sit above existing enterprise systems and support them; it does not replace SAP, Oracle, IBM, Microsoft systems, or legacy platforms.
Vertical AI-MDM
Purpose-built for complex industrial materials, assets, suppliers, service data, and operational master data.
Repeatable Intelligence
Codified methods can support scalable delivery, recurring revenue potential, and faster customer value creation.
Data Foundation
Cleaner industrial data supports ERP modernization, procurement optimization, maintenance planning, and future AI readiness.
Investment Overview
USD 3,900,000
Orderly shareholder payout for selected early shareholders whose investment horizon has been reached.
USD 3,900,000
Growth and transformation funding, including working capital and CODA-AI acceleration.
This is teaser-level information only and is not an offer, commitment, or recommendation.
Group Structure
The investor pathway is presented through CTS and its indirect relationship to CODASOL Group. Final structure, ownership, allocation, documentation, and legal terms remain subject to diligence and final documentation.
Indicative ROI & Ownership Calculator
Indicative only | Non-binding | Subject to final documentation
Scenario inputs
Build the investment ticket
Debt allocation is automatically derived as 50% of the ticket before any equity cap adjustment.
Enter a total investment amount to calculate the scenario.
Maximum total raise assumption is USD 7,800,000.
Maximum equity allocation is USD 3,900,000 under the current raise structure.
Debt allocation exceeds the current USD 3,900,000 debt raise assumption.
Future valuation is below the 2025 reference valuation assumption.
Total Projected Return
USD 9,873,252
Enter a total investment amount to calculate the scenario.
Ownership is calculated only on the equity portion of the investment. The debt portion is modelled separately and does not create ownership.
Future equity value responds to the future CODASOL Group valuation input, while the debt return responds to the selected simple-interest rate and period.
This calculator is for indicative scenario modelling only. Ownership is calculated only on the equity portion of the investment. The debt portion does not create ownership and is modelled using simple interest. Final ownership, valuation, interest rate, repayment terms, investor rights, allocation, and legal structure are subject to final documentation and approval.
Future valuation inputs are illustrative and do not represent a guarantee, forecast, or commitment.
Stage 1 investor teaser
Meet the Team
CODASOL combines industrial data expertise, governance experience, operational delivery capability, and strategic investor readiness.
Azmat Taufique
Chairman of the Board of Directors, Coda Group
Global investment and infrastructure leader with 30+ years of experience across private equity, fund management, infrastructure, and cross-border investments.
View Bio
Azmat Taufique brings senior-level investment, governance, and cross-border transaction experience to CODA.
Naleem Bukari
Co-Founder and CEO
Technical and business leader with deep expertise in asset management, material management, ISO 55001, inventory optimization, and master data management.
View Bio
Naleem Bukari is one of the driving forces behind CODA’s industrial data and asset management foundation.
Michael J. Schlunegger
Executive Director Strategy / BD&Marketing
Swiss business leader with 30+ years of experience in business development, sales, and technology products across multiple regions.
View Bio
Michael J. Schlunegger brings international business development, sales leadership, technology commercialization, and strategic growth experience to CODA.
Rizwan Nawab
Co-Founder, Chief Operating Officer (COO), and Member of the Board of Directors
Operational leader with strong experience in SAP materials management, procurement, vendor development, supply chain management, and project implementation.
View Bio
Rizwan Nawab supports CODA through operational, procurement, supply chain, and project implementation experience.
Murali Venkataraman
Executive Director Projects
Global supply chain operations leader with 35+ years of oil and gas experience, including senior management, major capital projects, and international delivery.
View Bio
Murali Venkataraman brings 35+ years of oil and gas supply chain and operations experience across frontline, senior management, and consulting roles. He has led strategic transformation initiatives and supported major capital projects across Asia, Europe, the Middle East, and the Americas.
Marwa Haddar
Strategic Advisory Board
Corporate finance and restructuring advisor with experience across Asia, the Middle East, Africa, and infrastructure-related mandates.
View Bio
Marwa Haddar supports CODA with corporate finance, restructuring, transaction, and strategic advisory experience.
James P. Bond
Executive Advisory Council
Global finance and infrastructure strategy expert with World Bank Group leadership experience and advisory roles across emerging markets.
View Bio
James P. Bond brings global finance, infrastructure strategy, and emerging-market advisory experience.
Investor Q&A
CODA-AI – Deep Dive Investor Q&A
Why is CODA-AI not just another chatbot?
Answer
CODA-AI uses a chatbot interface, but the real product is the industrial decision layer behind it. A normal chatbot mainly searches and summarizes information. CODA-AI is designed to support governed industrial decisions using operational logic, maintenance context, supplier intelligence, duplicate rules, and risk awareness.
Easy Example
A chatbot may say: “These two materials look similar.” CODA-AI may say: “Do not merge them yet because one material is linked to critical pumps, approved supplier contracts, and six years of maintenance history. Wrong merging could create operational downtime risk.”
The key difference is that the chatbot is only the communication interface. The real value is the industrial brain behind it.
Why can’t companies simply connect ChatGPT to SAP?
Answer
Connecting a chatbot to SAP is technically easy today. The difficult part is understanding what is operationally safe, financially correct, maintenance-approved, and engineering-compliant.
Easy Example
A generic chatbot may say: “These valves are similar.” CODA-AI may say: “These valves should not be standardized because one is approved for corrosive offshore environments and the other is not.”
The challenge is not reading ERP data. The challenge is understanding operational consequences across maintenance, procurement, engineering, and safety.
Why is CODA difficult to copy?
Answer
The software itself is not the moat. The moat is the industrial experience behind the software. CODA has accumulated operational learning over more than 15 years and 100+ industrial projects.
Easy Example
Imagine two chefs. One has internet recipes. The other has cooked professionally for 15 years and already knows what works, what fails, and why customers return. Both can cook, but only one has operational experience.
CODA has already seen duplicate inventory, failed standardization projects, supplier inconsistencies, ERP chaos, and maintenance issues in real industrial environments.
What is industrial intelligence?
Answer
Industrial intelligence means understanding operational consequences, not only technical descriptions. Generic AI understands words. Industrial intelligence understands what happens operationally if a decision is made.
Easy Example
Google knows: “This is a bearing.” CODA-AI should know: “This bearing is used in a critical pump and the wrong replacement could stop production.”
The value is not recognizing a part number. The value is understanding maintenance impact, asset criticality, procurement implications, and operational risk.
Why is verticalization important?
Answer
Every industry has different operational logic, maintenance behavior, suppliers, terminology, and engineering rules. A generic AI model cannot deeply understand every industry equally well.
Easy Example
A hospital AI cannot suddenly run a refinery safely because the workflows, safety logic, and operational risks are completely different.
CODA-AI aims to build industry-specific intelligence for sectors such as oil & gas, LNG, utilities, mining, manufacturing, and ports.
Why is CODA better than a generic AI startup?
Answer
Most AI startups start with technology and then search for problems to solve. CODA started with real industrial operational problems long before AI became mainstream.
Easy Example
CODA solved industrial master data problems for years before turning that experience into AI.
This creates a stronger foundation because the company already understands customer pain points, ERP environments, maintenance workflows, and procurement complexity.
What is the CODA-AI industrial layer?
Answer
It is a smart operational layer sitting above ERP and operational systems such as SAP, Oracle, IBM, Snowflake, and Databricks.
Easy Example
Instead of only storing data, the layer helps answer: “Can this material be safely standardized?” or “Which duplicate creates operational risk?”
The layer connects ERP data, supplier logic, inventory, asset structures, maintenance history, and governance workflows into one intelligence layer.
Why does AI-MDM matter now?
Answer
AI is only as good as the data underneath it. Poor industrial data creates unreliable AI decisions.
Easy Example
If a refinery has five names for the same valve, AI-driven procurement and maintenance recommendations become unreliable.
CODA-AI improves the industrial data foundation first before higher-level automation and AI-driven operational decisions are deployed.
Why can CODA scale beyond services?
Answer
CODA is converting repeated industrial work into reusable software and AI logic. That allows the company to move from manpower-based execution toward scalable recurring revenue.
Easy Example
Today engineers manually solve duplicate problems repeatedly. CODA-AI learns those patterns once and reuses them.
The same duplicate logic, classification logic, supplier normalization, and governance workflows can then be deployed across many customers.
Why should investors care now?
Answer
The market is moving from AI chat toward AI operational decision-making. Industrial companies now want AI that improves operations, not only productivity.
Easy Example
The first AI wave focused on chat and productivity. The next wave focuses on maintenance, procurement, operations, and industrial intelligence.
CODA-AI is positioning itself inside that next wave.
How does CODA-AI create value beyond simple duplicate detection?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
How does CODA-AI support safer industrial master data decisions?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
Why is operational context important in industrial AI?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
How can CODA-AI help reduce procurement and inventory risk?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
Why does CODA-AI matter for maintenance and asset reliability?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
How does CODA-AI connect data quality with operational performance?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
Why is governed AI important in asset-heavy industries?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
How can CODA-AI support scalable recurring revenue?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
Why is CODA-AI different from standalone AI tools?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
What is the long-term vision for CODA-AI?
Answer
CODA-AI combines industrial domain knowledge, ERP integration logic, operational context, and AI-driven governance into one industrial intelligence layer. The goal is not only better data, but safer and smarter industrial operations.
Easy Example
Example: Instead of simply identifying duplicate materials, CODA-AI evaluates maintenance impact, supplier approval, inventory implications, and operational risk before recommending actions.
This creates operational value that generic AI tools or standalone chatbots usually cannot provide.
Contact / Request NDA Deck
Qualified investors may request the NDA deck and next-step materials after introductory review.
Disclaimer
This portal is Stage 1 non-NDA investor teaser content only. It is provided for introductory discussion purposes and does not constitute an offer to sell securities, a solicitation, investment advice, or a binding commitment. All figures, ownership outputs, transaction structures, and forward-looking statements are indicative only, non-binding, and subject to diligence, final legal documentation, and investor qualification.
CODASOL
Investor Assistant
Ask Stage 1, non-NDA questions about the CODASOL investor opportunity.
