Applications such as ChatGPT and Claude have exploded in popularity across the digital landscape - and for good
reason. Being LLMs that can generate insightful recommendations and automate tasks in an intelligent manner, much
potential lies for organisations to embrace these and other AI-powered tools, such as LLMs and Natural Language
Processing (NLP).
Also classified as Generative Artificial Intelligence (GenAI), the likes of ChatGPT and Claude are accessible to
organisations across any industry or size. In fact, not embracing AI-powered technologies such as GenAI, LLMs or
NLP could entail the risk of businesses lagging behind - especially as competitors leverage the same to impress
customers.
Research by McKinsey estimates that Generative AI could
add $2.6 to $4.4 trillion in value across the global economy, especially in areas such as customer operations,
sales and marketing, and software engineering.
A few prime examples of how this value is created includes:
Enhancing the quality of, and context behind, customer interactions (both human and automated).
Optimising sales and marketing reports with forecasts that are accurate as they are intelligent.
Maximising productivity across the workforce, while reducing costs at the same time
With GenAI doing so much, it’s no wonder that many of the world’s top tech companies are investing heavily into GenAI research. Similar to how the internet completely transformed how we communicate and connect today, GenAI’s abilities to automate repetitive tasks, alleviate the possibility of human error, as well as deliver personalised content and recommendations has become a game-changer for the companies that have already adopted it.
We know that building, deploying and monitoring GenAI models isn’t infallible; complexities and challenges
abound in a space that incorporates vast datasets and advanced algorithms.
Our AI specialists understand this, which is why our teams work proactively to contain challenges surrounding
data, prompts and labelling strategies - while researching for newer, innovative ways to tackle persisting
problems too.
Additionally, maintaining a strict sense of responsibility is also top priority for our teams, so we can deliver
GenAI that leverages bottom line growth for your business, while being well within the boundaries of ethics and
compliance.
At EFutures, our AI teams help your business mitigate an array of GenAI challenges surrounding:
Hallucinations
Data transformation, which includes (but isn’t limited to) splitting and fine-tuning
Token limits
Raw and unfiltered datasets
Concerns around ethics, integrity and biases
Combined with our expertise in custom software and application development, our teams here at EFutures can deliver a holistic solution that’s power-packed with intelligent, AI-driven capabilities - while evolving to suit new technologies, as they appear.
Although publicly facing GenAI and LLM applications such as ChatGPT may be highly accessible for delivering
quick results, these can significantly increase vulnerabilities for your organisation. A lack of data security
is the biggest factor to consider here, as confidential information could be fed into publicly available LLM
models by employees (both knowingly and unknowingly).
Implementing private LLM models is a viable solution, as it mitigates the risk of data leakage, while
maintaining models that are custom-trained to exclusively address the unique needs of your organisation.
As a Microsoft and AWS partner, EFutures has the capabilities to build private LLM models at scale. Through
Microsoft’s partnership with OpenAI, and AWS offering Claude via its partnership with Anthropic, EFutures can
train, build and maintain private LLM and GenAI models to suit each client’s specific requirements.
Along with purpose-built customisation, our partnerships with Microsoft and AWS shall also deliver:
Advanced data security and comprehensive audit trails, to stringently meet a wide variety of compliance needs,
Comprehensive Identity and Access Management, via MFA and granular access controls,
Hosted infrastructure for LLM models that can scale up or down with near-zero downtime.
While our AI teams here at EFutures place great emphasis on working in close conjunction with the standards set by Microsoft and AWS, our experts also utilise other tools such as GitHub Copilot, to further strengthen the quality and accuracy of custom algorithms.
As AI proliferates across today’s digital landscape and automates numerous processes across organisations,
concerns are raised about deskilling and redundancies. However, the skills now required in job roles are
expected to evolve, in order to ensure working professionals can expertly function alongside AI-powered
systems - akin to getting familiarised with Microsoft Office tools at the height of the digitisation era.
At EFutures, our teams understand that building AI models goes beyond only meeting business bottom lines.
As a result, our AI expertise extends beyond technical areas, to also address social concerns surrounding
AI adoption.
Technical and otherwise, the entire scope of our AI services include:
Educating business teams on the potential and possibilities of AI
Strategically identifying use cases that are contextually relevant for AI adoption
Building MVPs to encourage a gradual adoption of intelligent technologies (especially for pilots)
Enterprise-wide implementation as well as post-implementation strategies
With AI beckoning more than just technological advancement, our team of AI experts will ensure that all your business concerns are addressed with an AI adoption roadmap that is tailored to exclusively meet the needs of your organisation - no matter how unique they may be.