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Trusted technology partner to some of the world’s most successful private equity funds and their portfolio companies, through the investment cycle.
Trusted technology partner to
160+ private equity funds
and their portfolio companies, through the investment cycle.
160+ private equity funds
and their portfolio companies, through the investment cycle.
The rate of innovation in AI is unprecedented and will continue to accelerate due to the emergence of Large Language Model(LLM)-based systems.
“A new chat bot is a ‘code red’ for Google’s Search business.”
A new wave of chatbots like ChatGPT uses artificial intelligence that could reinvent or even replace the traditional internet search engine.
New York Times, Dec 2022.
In our experience, real-world AI-based solutions deliver ROIs of 20%-50% average to portfolio companies - helpful to know where to look before you buy or sell an asset.
Some organisations can also benefit from our AI Vision Workshops for C-Suite, Directors and Senior Managers to help them look into the future, explore other areas to apply AI and how to socialise the benefits of AI across your business.
Strategy Report
An executive summary, in both PDF and Word document formats.
AI Policies
AI policies including AI Usage Policy and AI Ethics & Governance Policy.
AI Roadmap
A full list of potential AI/ML use-cases, in priority order and organised into workstreams. Provided as an Excel sheet.
Detailed Recommendations
A list of recommendations and next steps to start the process of implementing the strategy.
AI Plan
An overlay with your existing IT delivery plans and in-flight projects to provide an integrated plan across all technical and business work. The plan will be provided as an Excel sheet.
Summary Presentation
We will provide a PowerPoint presentation that walks you through the key aspects of the AI Strategy.
A total of
87
use-cases required AI or Machine Learning with the rest more aligned to automation tasks.
ROI Range: 20%-50%+
A report containing a prioritized list of use cases was presented to the stakeholders in Q3 - the use cases are prioritized by ROI.
Effect of AI on online chatbot technology
- Generative AI improves chatbots by adding a layer of semantics on top of the chatbot to produce conversational responses and handle inquiries just as a human would. This helps businesses reduce their customer service costs while also improving the customer experience by providing immediate assistance.
- Generative AI chatbots can generate instant responses to customer queries, eliminating the need for customers to wait in lengthy phone queues or navigate complex Interactive Voice Response (IVR) systems.
- Generative AI-powered chatbots can also operate round the clock, providing instant support and information to customers at any time.
Legacy technologies
- A discussion of Chatbots must now include a discussion of legacy telephone-based customer interaction systems which the new Chatbot technologies are set to replace almost completely.
- Interactive Voice Recognition (IVR) based systems – required rigid conversation scripting and could only process limited response.
- Menu and button based chatbots - even more constrained than IVR, these required tone-based responses and rule-based interactions only.
- Existing Chatbots - rule-based models or predefined response templates.
Disruptive technologies
- Large language models (LLM), e.g., OpenAI GPT.
- Real time text-to-voice/voice-to-text.
- Real time sentiment and context analysis.
- Naturalistic human conversation.
** The notable exception here may be for interactive access for specialist
demographics such as severe accessibility issues (e.g., Deaf and Blind, etc.)
The current pipeline of work within this sector at D-LAB Real-World AI development studios around the world include:
(All are at a Proof of Concept / Early Release Level.)
- A financial assistance chatbot for a financial services client, analysing KPI results, aligning with objectives and providing suggestions.
- A voice-based chatbot for limousine reservations able to manage and respond to customer requirements.
- A zoom-call conversational bot which processes the ongoing call and offers suggestions and enhancements as a conversational assistant.
- A voice based conversational chatbot for an eCommerce fashion retailer that can do product search, make styling suggestions and offer alternative recommendations.
ROI Range: 20%-50%+
Effect of AI on online job recruitment technology
- Automated job candidate screening process
- Analysing resumes, cover letters and other application materials
- Reduce bias in the hiring process
- Removing identifying information
- Improve candidate engagement process
- Providing personalised feedback and communication throughout the recruitment process
- Predict candidate success
- Analysing data from previous hires and identifying patterns that indicate which candidates are most likely to succeed in a particular role
- Streamline recruitment process
- Automating repetitive tasks such as scheduling interviews and sending follow-up emails
Disruptive technologies consumed for this project
- Large language models (LLM), e.g., OpenAI GPT
- Vector databases
- AI decision engines
Project outcome: The typical recruitment process in CXTECH involves creating job descriptions for job sites and social media posts, analysing batches of resumes sent by candidates or stored in the internal resume database, matching those resumes, screening the candidates, reformatting and summarising feedback. RFAI automates this process to ensure fast and high-quality routine checks and analyses. Integrated OpenAI assistant checks grammar and spelling mistakes in resumes and job descriptions, helping to identify errors that may not be easily noticed with other grammar validator like Grammarly. It will do the analysis of resumes, summarisation of reports and feedback. It also connects to the internal database to match relevant resumes to the job requirements helping the recruiter to prioritize screenings with candidates.
ROI Range: 20%-50%+
The Challenge
- Legacy code is a huge challenge for many companies. Transforming legacy systems into a modern technology stack is a high-complexity endeavour, if done via traditional methods. Company X faced a challenge to convert 1+ million lines of PowerHouse (Cognos) code to WPF/Microsoft/C# and MSSQL SQL. The traditional manual approach was estimated at $6M, 6 years; the semi-automated alternative was estimated at $2M over 3.5 years.
The Solution
- Using proprietary technology, the project team was able to complete the modernization process for 33% of the cost and 58% of the time, estimated for the manual approach .
- Effect of AI on code modernization technology:
- High-quality, maintainable target code base that complies with best practice standards.
- Efficient interpretation of existing code-base and automatic outline of proposed system.
- AI serves as Co-Pilot for SW professionals significantly reducing the time and effort required for code analysis.
- Automatic test generation reduces significantly the effort in tests and code coverage.
- Gen-AI serves as a layer on top of systematic code conversion models allowing the combination of efficiency and robustness.
Disruptive technologies involved:
- Large language models.
- AI-driven Projective and Templates modelling framework.
Project Outcome
- A typical process would require detailed reverse engineering of all system components, creation of detailed specification and then tedious implementation process. The risks in such a process are huge and often such projects are avoided completely.
- AI-driven software reengineering technology can automate multiple stages of this process such that code is generated automatically in a robust way - human involvement is minimized.
- Test collateral such as automated regression tests can be generated from the models of the converted software.
