Clients have achieved transformative results including complete sales infrastructure development from zero (DATALEAGUE), AI-powered automation driving net-new business (DiUS), successful market entry for SaaS platforms (Currious), certified B Corporation status with global reach (TOMbag), and strategic differentiation in competitive markets (Ex-Tech Solutions). Our 22 years of GTM experience has helped businesses achieve 10x growth.

We serve technology consulting, SaaS platforms, managed IT services, sustainability companies, media, real estate and B2B enterprises. Case studies demonstrate success across IT consultancies, collaboration software, consumer goods, and professional services throughout Australia and internationally.

Engagements range from focused 3-month strategies to 6-12 month ongoing partnerships. Specific GTM strategy projects can be completed in 4-12 weeks depending on scope and implementation requirements.

We leverage 22 years of foundational GTM experience combined with hand-on cutting-edge generative AI and prompt engineering techniques. Unlike traditional consultancies, we both design strategy and execute implementation, from building sales infrastructure to deploying AI workflow automation systems. We can also be apart of your embedded GTM team working alongside you and your team upskilling team members on the methodology and AI tooling as required.

We work with both. TOMbag demonstrates our co-founding and startup capability, while DATA LEAGUE and Currious show early-stage company support. DiUS and Ex-Tech Solutions represent established company engagements. Our experience spans startups, scale-ups, and enterprises.

AI workflow automation uses generative AI to track customer intent signals across channels, automatically identify buying opportunities, generate contextual insights, and trigger timely outreach. We leverage cutting edge AI platforms and AI models including Apollo, Clay, Replit, Claude Code, Gumloop, Google AI Studio, Perplexity, Perplexity Comet and more.

No. While based in Sydney, Australia, we serve clients internationally. Our 22 years of experience spans the APAC region, US, Europe and beyond, with expertise across diverse markets, cultures, and go-to-market approaches.

Success metrics vary by engagement but include: sales infrastructure establishment, pipeline generation, revenue growth, automation efficiency, market positioning improvement, Jobs-to-be-done AI agent automation, and client-reported business outcomes documented in testimonials from founders and executives.

A Prompt is a piece of text that provides context and direction for Language Models (LMs) and Large Language Models (LLMs) to generate text. It essentially serves as a starting point for the AI models to produce text that is relevant and accurate, while also guiding them towards a specific style or tone. By using prompts, LMs and LLMs can generate more focused and consistent text, which is particularly important for LLMs that have a vast number of parameters and can produce a wide range of outputs. Prompts are crucial for improving the accuracy, relevance, and efficiency of AI-generated text.

Prompt engineering (or AI Prompt Engineering) is the process of creating prompts that guide large language models (LLMs) to generate a desired output. It involves crafting, priming, refining, or probing a series of prompts within the bounded scope of a single conversation to communicate with and direct the behaviour of LLMs.

An AI prompt can take the form of any text, question, information, or coding that communicates to AI what response is desired. Prompts can include instructions, questions, or any other type of input, depending on the intended use of the model. Prompt engineering is an emergent ability of LLMs, allowing them to learn from a user's interventions and adapt to them.

Prompt engineering is an essential AI engineering technique for refining LLMs with specific prompts and recommended outputs, and it is also the process of refining input to various generative AI services to generate text or images.

Prompt Design is the process of crafting a well-structured and effective prompt for a Language Model (LM) or Large Language Model (LLM). A good prompt can provide context, direction, and focus for generating text, which can lead to more accurate and relevant outputs. The key to effective prompt design is to understand the specific needs of the user and to craft prompts that are tailored to those needs. This involves considering factors such as the intended audience, the desired output, and the style and tone of the text.

A generative model is a type of artificial intelligence (AI) model that can generate new data based on patterns it has learned from existing data. This type of model is often used in natural language processing and image generation applications, where it can produce realistic and diverse outputs. Generative models are a powerful tool for content creation and can help businesses improve their productivity and efficiency.

By leveraging the capabilities of generative models, businesses can create high-quality content at scale, leading to improved customer engagement and revenue. Contact us to learn more about how generative models can benefit your business.

Large Language Models (LLMs) are AI models that use machine learning to process and understand language. They can handle large amounts of text data and learn from it to generate new language. LLMs perform various natural language processing tasks, such as translation, summarization, and generation. Examples include OpenAI's GPT-3 and GPT-4. LLMs revolutionize interactions with technology by improving the efficiency and accuracy of language tasks, creating more engaging user experiences. Businesses can use LLMs for chatbots, customer support, and content generation, enhancing efficiency, reducing costs, and improving customer engagement.

Large Language Models (LLMs) are a type of artificial intelligence (AI) that can generate text based on input prompts. These models are trained on vast amounts of text data, which enables them to learn the patterns and structures of language. When given a prompt, an LLM uses this knowledge to generate text that is contextually relevant and coherent.

LLMs work by breaking down text into smaller components, such as words and phrases, and then analysing the relationships between them. This process, known as natural language processing, allows the model to understand the meaning and context of the text it is processing. Once the model has analysed the prompt, it uses its knowledge of language structure to generate text that is relevant and accurate.

One of the key advantages of LLMs is their ability to generate text that is consistent in style and tone. This is because LLMs learn from the input data they are trained on, including the style and tone of the text. By using this knowledge, LLMs can generate text that is appropriate for the intended audience and consistent with the style and tone of the input prompt.

Overall, LLMs are powerful tools for generating text that is both accurate and relevant. By using the vast amounts of data they are trained on, these models can generate text that is contextually aware and tailored to the specific needs of the user. As such, they have a wide range of applications, from automated customer service to content creation.

Large Language Models (LLMs) are trained on vast amounts of text data using a process known as supervised or semi-supervised learning. This involves feeding the model a large corpus of text and providing it with a prompt, which it then uses to generate text. The generated text is compared to the original text, and the model is adjusted based on the difference between the two. This process is repeated thousands or even millions of times, allowing the model to learn the patterns and structures of language.

The training process for LLMs can take weeks or even months, as it requires a vast amount of computing power and storage. However, the end result is a highly accurate and contextually aware model that can generate text that is relevant and coherent.

One of the key challenges in training LLMs is ensuring that the data used is diverse enough to capture the full range of language patterns and structures. This requires a large and varied corpus of text data, which is often sourced from online sources such as websites, social media, and news articles.

Chat GPT-4 was trained using both public data and data licensed from third-party providers and was then fine-tuned with reinforcement learning from human and AI feedback for human alignment and policy compliance.

Overall, the training of LLMs is a complex and resource-intensive process, but the end result is a powerful tool for generating accurate and relevant text. As such, LLMs have a wide range of applications, from automated customer service to content creation.

Prompts play a vital role in both Language Models (LMs) and Large Language Models (LLMs) by providing a starting point for generating text. Essentially, a prompt is a piece of text that provides context and direction for the model to generate text. By using prompts, LMs and LLMs can produce more accurate and relevant text, as they have a better understanding of the context and topic they are generating text for. Additionally, prompts can be used to guide the model towards a specific style or tone, making the generated text more consistent and appropriate for the intended audience.

Not only do prompts improve the accuracy and relevance of the text generated by LMs and LLMs, but they can also help to improve their efficiency. This is because prompts can be used to narrow down the range of possible text outputs, which is particularly important for LLMs that have a vast number of parameters and can generate a wide range of outputs. By providing a prompt, the model can focus on generating text that is relevant and useful, rather than wasting resources on generating irrelevant or inaccurate text.

In summary, prompts are crucial for LMs and LLMs as they provide context, direction, and focus for generating text. By using prompts, LMs and LLMs can produce more accurate and relevant text, as well as improve their efficiency in generating text.

There are several types of prompts that can be used for Large Language Models (LLMs), each with its own strengths and weaknesses. Some of the most common types of prompts include:

1. Open-ended prompts: These prompts provide no specific direction or topic for the LLM to generate text on. Instead, they allow the model to generate text based on its own knowledge and understanding of language.

2. Closed-ended prompts: These prompts provide a specific topic or direction for the LLM to generate text on. They can be useful for generating text that is focused and relevant to a particular topic.

3. Conditional prompts: These prompts provide a condition or constraint that the LLM must take into account when generating text. For example, a conditional prompt might require the LLM to generate text that is both informative and entertaining.

4. Interactive prompts: These prompts involve a back-and-forth dialogue between the user and the LLM. They can be useful for generating text that is tailored to the specific needs and preferences of the user.

5. Zero-shot prompts: These prompts enable the LLM to generate text on a topic it has not been trained on. This is achieved by providing the LLM with a few keywords or phrases related to the topic.

6. Few-shot prompts: These prompts involve the LLM being trained on a small amount of data related to a particular topic or task. This enables the LLM to generate text on that topic with greater accuracy and relevance.

Incorporating different prompt types, including zero-shot and few-shot prompts, can further enhance the capabilities of LLMs and generate text that is more diverse and accurate. By leveraging the advantages of each prompt type, businesses can improve the efficiency and effectiveness of their LLM-generated text. This can have a significant impact on various industries, from content creation to customer service, and lead to improved productivity and revenue.

Fine-tuning an LLM involves taking a pre-trained Language Model (LM) or Large Language Model (LLM) and training it on a specific task or domain. This process allows the model to adapt to the nuances and complexities of the specific task or domain, resulting in improved performance and accuracy.

During the fine-tuning process, the model is re-trained on a smaller dataset that is relevant to the specific task or domain. This enables the model to learn the patterns and structures of language that are specific to that task or domain, leading to more accurate and contextually relevant outputs.

Fine-tuning is particularly useful for tasks such as sentiment analysis, text classification, and question-answering, where the model needs to be trained on a specific set of data to achieve optimal performance. By fine-tuning a pre-trained LLM, businesses can improve the efficiency and effectiveness of their AI-generated text, leading to increased productivity and revenue.

It's important to note that fine-tuning an LLM requires a significant amount of computing power and storage, as well as a large and varied corpus of text data. Additionally, the quality of the fine-tuning process can be affected by a range of factors, including the quality and relevance of the training data, the model architecture, and the fine-tuning parameters.

Fine-tuning an LLM is a powerful tool for improving the accuracy and relevance of AI-generated text. By understanding the nuances and complexities of a specific task or domain, businesses can generate text that is tailored to the specific needs of their users, leading to improved engagement and conversions.

Fine-tuning and Parameter Efficient Fine-tuning (PEFT) are two techniques used to optimise the performance of pre-trained Language Models (LMs) and Large Language Models (LLMs).

Fine-tuning involves taking a pre-trained LM or LLM and training it on a specific task or domain. This process involves re-training the model on a smaller dataset relevant to the specific task or domain, which enables the model to adapt to the nuances and complexities of that domain. Fine-tuning is particularly useful for tasks such as sentiment analysis, text classification, and question-answering, where the model needs to be trained on a specific set of data to achieve optimal performance.

PEFT, on the other hand, is a more efficient and cost-effective way to fine-tune pre-trained LMs and LLMs. This technique involves training only a subset of the model's parameters, rather than the entire model. This can significantly reduce the time and resources required to fine-tune the model, while still achieving comparable or even better results than traditional fine-tuning.

The key advantage of PEFT is its ability to optimise the performance of pre-trained LMs and LLMs in a more efficient and cost-effective way. By training only a subset of the model's parameters, PEFT can significantly reduce the time and resources required to fine-tune the model, while still achieving comparable or even better results than traditional fine-tuning.

Both fine-tuning and PEFT are techniques used to optimise the performance of pre-trained LMs and LLMs. Fine-tuning involves re-training the entire model on a specific task or domain, while PEFT involves training only a subset of the model's parameters. While both techniques have their advantages and disadvantages, PEFT is a more efficient and cost-effective way to fine-tune pre-trained LMs and LLMs. By understanding the differences between these techniques, businesses can select the one that best fits their needs and achieve optimal results from their LMs and LLMs.

A prompt engineer is a professional who specialises in crafting effective prompts for Language Models (LMs) and Large Language Models (LLMs). By understanding the specific needs of the user and tailoring prompts to those needs, a prompt engineer can improve the accuracy and relevance of the text generated by these models.

Prompt engineering is a crucial aspect of AI-powered content creation and can help businesses improve the visibility of their content and rank higher in organic search results. By incorporating different prompt types, including open-ended, closed-ended, and interactive prompts, prompt engineers can further enhance the capabilities of LLMs and generate text that is more diverse and accurate.

AI Prompt Engineering is important because it helps to create user interfaces that are more engaging, intuitive, and effective. By designing and testing prompts, designers can create interfaces that prompt users to take specific actions, ultimately leading to a positive user experience.

Prompt Engineering can improve user engagement and retention by prompting users to take specific actions that are beneficial to the business, such as creating engaging relevant content, targeted messaging for B2B sales, making a purchase or signing up for a newsletter. It can also help to reduce user frustration and confusion by providing clear and concise instructions.

Moreover, AI Prompt Engineering can help businesses to achieve their goals by increasing conversions, reducing bounce rates, and improving user satisfaction. It can also help to improve the efficiency and accuracy of natural language processing tasks, such as chatbots and customer service interactions.

AI Agents are designed to perform specific tasks or functions by interacting with their environment and making data-driven decisions. They can be used in various applications, from automated customer service to content creation. Leveraging AI Agents can enhance business efficiency and productivity, leading to increased revenue and growth. Contact us to learn more about incorporating AI Agents into your business strategy.

A Generative Pre-trained Transformer (GPT) is a type of large language model (LLM) developed by OpenAI. GPTs are designed to process and understand language, and are capable of generating human-like text. The "transformer" aspect of the name refers to the transformer architecture used in the model, which is a type of neural network that is particularly well-suited to processing sequential data.

GPTs are pre-trained on large amounts of text data, which allows them to learn the patterns and structures of language. Once the model is trained, it can be fine-tuned for specific natural language processing tasks, such as language translation, text summarization, and language generation. GPTs are particularly well-suited to language generation tasks, as they are capable of generating coherent and natural-sounding text that is similar in style to human writing.

GPTs are important because they have the potential to revolutionise the way that we interact with technology. By enabling machines to generate human-like text, GPTs can be used for a wide variety of tasks, such as chatbots, content creation, and language translation. As GPTs continue to improve, they have the potential to create more engaging and natural user experiences.

Reinforcement Learning from Human Feedback (RLHF) is a type of machine learning that involves humans providing feedback to improve the learning process of an AI model. Unlike traditional reinforcement learning, which relies on an agent receiving feedback from its environment, RLHF involves humans providing feedback to the agent in the form of rewards or penalties.

The goal of RLHF is to improve the performance of AI models by incorporating human expertise and intuition. By providing feedback, humans can help to guide the learning process towards more effective and efficient outcomes. This can be particularly useful in scenarios where the environment is complex or where the agent has limited access to data.

One of the key advantages of RLHF is its ability to leverage the unique skills and knowledge of human experts. By incorporating human feedback, RLHF can help to improve the accuracy and relevance of AI models, leading to better performance and results. Additionally, RLHF can help to reduce the amount of time and resources required to train AI models, making the process more efficient and cost-effective.

RLHF is an exciting and promising area of machine learning that has the potential to revolutionise the way we train and develop AI models such as ChatGPT-4, Bard, Claude and other LLMs.

AI Prompt Engineering can be used by enterprises in a variety of ways to improve user engagement and achieve their goals. By designing and testing prompts, businesses can create user interfaces that prompt users to take specific actions, such as making a purchase or signing up for a newsletter. This can increase conversions, reduce bounce rates, and improve user satisfaction.

Moreover, AI Prompt Engineering can help businesses to improve the efficiency and accuracy of natural language processing tasks, such as chatbots and customer service interactions. By enabling machines to understand and generate language, businesses can reduce response times and improve the overall quality of their customer interactions.

AI Prompt Engineering can also be used to create sales and marketing content, such as email campaigns and social media posts. By generating content that is tailored to the needs and interests of users, businesses can increase engagement and drive more conversions.

When it comes to guiding large language models (LLMs), there are several different prompting techniques that can be used. These techniques can help businesses to better communicate with and direct the behaviour of LLMs, resulting in improved efficiency and accuracy of natural language processing tasks.

One prompting technique is zero-shot prompting, which involves using simple questions or requests that do not provide extra context or guidance to the AI. This technique can be useful in situations where the AI already has a good understanding of the topic at hand.

Another technique is one-shot prompting, which involves providing a single example or piece of information to the AI to help it generate a desired output. This technique can be useful when working with limited data or when trying to generate novel responses.

Role prompting involves encouraging the AI to "get in character" by providing a specific persona or role for it to emulate. This technique can be useful in situations where the AI needs to generate responses in a particular style or tone.

Introducing a critical agent can also be a useful prompting technique. This involves providing a second AI that can critically evaluate the responses of the first AI and provide feedback. This can help to improve the quality and accuracy of the AI's responses.

Finally, chain of thought prompts involve providing a series of related prompts to the AI to help it generate a coherent and logical response. This can be useful when trying to generate longer pieces of text or when trying to connect different ideas together.

By using these prompting techniques, businesses can improve the performance and accuracy of their LLMs, leading to more effective natural language processing tasks and ultimately, a better user experience.

AI Prompt Engineering can be used to create a wide variety of marketing content, including email campaigns, social media posts, and website copy. By generating content that is tailored to the needs and interests of users, businesses can increase engagement and drive more conversions.

With AI Prompt Engineering, marketers can create content that is more engaging and personalised. For example, AI Prompt Engineering can be used to generate personalised email campaigns that are tailored to the needs and preferences of individual users. This can help to increase open and click-through rates, and can ultimately lead to more conversions and higher profitability.

AI Prompt Engineering can also be used to generate social media posts that are more engaging and shareable. By analysing user data and generating content that is relevant and interesting to users, businesses can increase their social media presence and drive more traffic to their website.

Finally, AI Prompt Engineering can be used to generate website copy that is more engaging and informative. By analysing user behaviour and generating content that is tailored to the needs and preferences of individual users, businesses can improve the user experience and drive more conversions.

AI Prompt Engineering can significantly enhance business efficiency. The extent of improvement depends on the specific implementation, use case, and industry. According to a McKinsey study, by 2030, activities accounting for up to 30% of hours currently worked across the US economy could be automated, a trend accelerated by generative AI.

Generative AI has the potential to increase sales productivity by 3-5% of current global sales expenditures. It's important to note that this analysis may not fully capture the additional revenue that generative AI can bring to sales functions. For instance, generative AI can identify leads, follow-up capabilities, and help facilitate more effective outreach, resulting in additional revenue. Additionally, generative AI can help save time for sales representatives which can be utilised in higher-quality customer interactions, leading to increased sales success.

In some cases, generative AI can work in partnership with workers to enhance their productivity, acting as a virtual collaborator. Its ability to quickly process large amounts of data and draw conclusions from it helps the technology offer insights and options that can significantly improve knowledge work. This can speed up product development and allow employees to focus on higher-impact tasks. Contact us to explore how we can help your sales teams.

Generative AI can boost marketing productivity by 5 to 15 percent of total marketing spending. It provides better data insights, leading to innovative marketing campaigns and more effective targeting of customer segments. This allows marketing teams to allocate more resources to creating high-quality content for owned channels, potentially reducing the need for external channels and agencies. Contact us to explore how we can help your sales teams.

AI Prompt Engineering can improve business efficiency in several ways. It enhances user engagement and retention by prompting users to take specific actions, such as making a purchase or signing up for a newsletter, leading to increased conversions and reduced bounce rates. It also reduces user frustration by providing clear instructions, creating more intuitive interfaces. Additionally, AI Prompt Engineering helps businesses achieve their goals by increasing conversions, reducing response times, and improving customer interactions. By enabling machines to understand and generate language, businesses can handle customer queries more efficiently.

An AI copilot is a type of generative AI that can assist humans in performing complex tasks enables workers to create higher quality output in the same amount of time. Enterprises can benefit from AI copilots by improving their efficiency and productivity. By leveraging the power of AI copilots, businesses can automate repetitive or time-consuming tasks, freeing up employees to focus on higher-level tasks that require human expertise.

Additionally, AI copilots can help to reduce errors and improve the accuracy of tasks, leading to improved outcomes and customer satisfaction. Research shows that Generative AI coding tools reduce task times by 56% and writing tools decrease writing time by 37%, with improved quality. On average, across the economy, Generative AI can automate 22% of task-hours and augment an equal share. To learn more about how AI copilots can benefit your enterprise, contact us for our expert Generative AI strategy advisory services.

After telling us how we can help your business, we will schedule an discovery call to learn more about your business and scope the areas you would like to implement AI Prompt Engineering systems to improve business operations and your GTM strategy.

After that, we'll put together a Proposal or Statement of Work based on your business and requirements. In some cases, if required, we can start with a Proof of Concept where we dedicate a short period of time to showing you and your team the value of AI Prompt Engineering in one of your areas you want business improvement before agreeing to a full engagement.

We are located in Sydney, Australia and operate in AEST business hours however service customers all over the world.

We are flexible in our way of working. We can work on both an hourly business advisory basis and on a daily-rate depending in the scope of work, time needed and resources required to complete to engagement or project. Contact us today to see how we can work together.

Yes, we can help train your teams and management in how to leverage GenAI to achieve business goals. Training works best coupled with Generative AI advisory services so that our training is specific the your enterprises specific needs. Contact us to learn more.