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Vertical AI – Bringing human intelligence to software since 2018
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Enjoy more time to capitalise on your business by letting us handle the mundane.

World Map
Structured Products
Captured and validated across asset classes globally.
Margin Bookings
An automated process for collateral managers.
Transaction Reporting
Reconciled daily across global trade repositories.
Trade Allocations
Eliminate the need for monthly manual processing.
Since 2018, our in-house technology has empowered clients to seamlessly transition and focus on solving challenges with our customised, high-end products.
Fed up with wasting time on mundane, complex tasks and struggling to manage everything efficiently? Our AI-powered products automate routine processes, freeing up your time and boosting productivity. With advanced tools designed to streamline management and optimise workflows, you can focus on what truly matters—driving growth and making smarter decisions.
100% Compliant, 100% Accurate, >90% STP
Intellimation.ai is a deep-tech Vertical AI firm that has developed an industry-first, proprietary domain and contextual model for the Banking and Financial Services (BFS) industry. Our AI products are utilised in over 100 Corporate & Investment Banking (CIB) complex workflows, supporting Controls, Middle Office, and Front Office projects. Today, we serve 30+ customers, including several bulge bracket banks and their counterparts (mortgage providers, hedge funds, ETFs, direct lending funds, custodians, administrators, etc.) across the US, UK, Europe, Middle East, India, Singapore, and Japan.
Our Technology
 we are neither GenAI nor a wrapper on Gen AI
We are Vertical AI
Introducing our in-house built proprietary vertical AI engine called ‘ Vector AI ’, that achieves 100% accuracy and no hallucination in our processes. Our focus is on maintaining our benchmark while others are still striving for it
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WhatisVectorai?

Vector.ai is an in-house built proprietary domain and contextual model—an AI platform designed to address high-impact business challenges. It employs multi-agent systems with diverse AI 'experts' collaborating to solve complex problems. The platform can extract information and perform related actions without human intervention. It observes its environment, makes decisions based on its training and observations, and acts accordingly to achieve specific goals or tasks. Vector.ai can make high-level decisions, manage multi-faceted business functions, and execute tasks independently while continuously learning from its environment and interactions.

Our Innovative Products

Discover the power of AI with our advanced, easy-to-use solutions designed to transform the way you work. Our AI-powered tools streamline processes, enhance decision-making, and unlock new levels of efficiency across various industries. Select the filters below to narrow your search for industry designated solutions.
Connect with us to discuss bespoke AI applications tailored to your needs.
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Trained on data specifically focused on Banking and Financial Services:

The system is trained on synthetic data that replicates Banking and Financial Services (BFS) data.

Maturity:
In production AI for the past 4.5 years, trained on over 6,000 BFS counterparts. Our solution provides end-to-end lifecycle management for a structured products business valued at $100 million.
Performance:
Performance: 100% uptime, no SLAs breached to date, and quick deployment time of two to six weeks.
Accuracy:
Our technology ensures 100% accurate information harvesting across structured products, margin calls, legal documents, ISDA agreements, and IB/BBG chats. Built in-house and proprietary, it is not based on external large language models (LLMs), providing full control over outcomes and governance with no risk of availability issues.
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Use Cases

Automating the Pricing Workflow for Structured Credit Trading

Use Case #1

Objective Image Objectives
Pricing structured credit consistently presents significant challenges for traders and banks.
There is no defined source of information, as quotes can arrive via email, FTP, SFTP, API, etc.
The pricing team must quickly sift through this data and determine the final marks before a specified deadline.
This becomes especially challenging when conducted across multiple geographies.
Additionally, if a counterparty (e.g., fund) challenges the quotes, significant tracking is required.
Finally, the output needs to be integrated into multiple systems, including risk metrics, NAV, sensitivity analysis, and others.
Approach Image Approach
Automated collation of data from multiple sources, including both unstructured and structured data, using domain-specific and contextual NLP.
Sifting through the data and performing real-time comparisons of quotes with internal models and sources.
Extended core AI data through a unified and integrated platform for quote communication, aimed at reducing ad hoc and fragmented communication.
Integrated like a 'Spider' with multiple systems and stakeholders to ensure consistent data usage and circulation.
Impact Image Overall Impact
$XX in annual run-rate gross benefits.
Improved pricing results in a XX% increase in margins.
Faster turnaround times.
Reduction in manual errors.
Risk Impact:
• The risk of missing out on quotes or communication, both internally and externally, has been reduced by approximately 95%.
Impact on Alpha:
• Faster, more transparent, and efficient price discovery results in increased Alpha.
Volume Impact:
• Considerable scalability has been achieved with a reduction in manual and fragmented workflows.

Processing ISDA Master Agreements for Over-The-Counter Derivatives

Use Case #2

Objective Image Objectives
A large investment bank was processing thousands of ISDA agreements for over-the-counter derivatives.
The document spans multiple pages and contains clauses such as 'Events of Default' and 'Termination Events'.
At present, the entire process was manual, and each document could take up to two hours.
This led to manual errors, slower turnaround times, and was not scalable in high-volume scenarios, increasing risk and limiting capacity.
The bank sought an automated solution to read the ISDA agreements and extract data points in order to eliminate the risk of manual errors and achieve scalability.
Approach Image Approach
Choosing the right technology was important due to the unstructured and dynamic nature of the data.
A 'fix at source' approach or RPA was ruled out due to the variability of the data.
A natural language processing-based solution was chosen, particularly algorithms capable of processing based on context and domain, rather than a pattern- or frame-based approach.
The requirement was narrowed down to:
• Extracting the relevant data points.
• Comparing the data with the relevant downstream system.
• Highlight any anomalies found in the data.
• And provide the ability to produce confirmations.
Impact Image Overall Impact
Capability to handle tabular data along with traceable extraction to provide complete auditability.
A cost reduction of more than 70% in operations.
Enhanced customer experience
A significant reduction in manual errors, improving accuracy and overall process reliability.
Faster processing times, leading to improved operational efficiency and quicker turnaround for key tasks.
Risk Impact
• A reduction in manual errors by more than 95%, significantly improving data accuracy and process reliability.
FTE Impact
• The bank was required to follow a four-eyes policy, which was immediately halved following the implementation.
• Additionally, processing time was reduced, thereby decreasing the time required to address each anomaly.
Volume Impact
• Considerable scalability was achieved through the reduction of human dependency.

Automation of covenant monitoring process using natural language processing

Use Case #3

Objective Image Objectives
Investment banks originate and process thousands of loans.
The loan document consists of multiple covenants and policy exceptions.
After origination, the asset must be managed and risk monitored on a monthly, quarterly, or annual basis, depending on the terms.
The financial reports are submitted in PDF format and vary with each loan.
Tracking each of these was becoming inefficient and cumbersome, leading to oversight, increased risk of default, and impeding the bank's ability to act and rectify the situation.
Approach Image Approach
The loan origination process within the bank was carefully mapped and fully understood, ensuring clarity in each stage of the workflow.
The constraints within the entire process were identified as follows:
• Manual extraction of covenants and financial records for monitoring purposes.
• Analysis of financial records to identify covenant breaches or the inability to predict potential defaults.
• Risk monitoring with a predictive ‘air-traffic controller’ setup to alert for likely breaches and issue threshold warnings.
The above three processes were automated, impacting various upstream and downstream systems.
Natural language processing was used to extract information from loan documents as well as financial reports.
Machine learning was used to identify probabilities of default and to flag loans for the watchlist.
Impact Image Overall Impact
$XX in annual run-rate gross benefits.
A cost reduction of more than 60% in the monitoring division.
The solution resulted in an enhanced customer experience, with faster service, greater accuracy, and more proactive communication.
Reduction in manual errors during data entry.
Risk Impact
• A reduction in manual errors by more than 90%.
• Extended core AI data capabilities for real-time monitoring and analytics.
• Predictive analytics were leveraged to identify high-risk loans and minimise exposure, enhancing risk management and reducing potential losses.
FTE and Scalability Impact
• Scalability was achieved through automation, leading to a more robust and efficient monitoring process.
• Reduction in the number of full-time equivalents (FTE) required for monitoring.
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Case Studies

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