TrueML Talks #28 - GenAI and LLMs for Sales Outreach @ OneShot

We are back with another episode of True ML Talks. In this, we again dive deep into GenAI and LLMs for Sales Outreach at OneShot and we are speaking with Peda Venki Pola

Venki is the founder and CTO at OneShot and before this, he worked at Salesforce as a Software Architect. OneShot helps B2B SaaS companies to generate the top of the funnel for their business.

Watch the full episode below:

AI-Powered Sales Outreach

OneShot's AI-Powerered Sales Outreach starts with your Ideal Customer Profile (ICP). They don't just take your word for it; they analyze your existing customer data to identify patterns and suggest the perfect target audience.

Once the ICP is defined, OneShot's AI goes into overdrive. They leverage their models and open-source tools like Hugging Face's Hub to scour the web, scraping data from LinkedIn profiles, company websites, and even financial reports. This treasure trove of information is then summarized and analyzed by powerful LLMs like GPT-4 and Claude, revealing insights about companies and individuals that traditional sales tools simply can't match.

But data is just the foundation. OneShot uses its understanding of your business and the insights gleaned from prospect research to generate personalized outreach messages that resonate. No more generic greetings and impersonal pitches – each message is tailored to the specific needs and interests of the recipient.

OneShot doesn't stop at email. They can automate outreach across multiple channels, including LinkedIn messages and even call scripts, ensuring your message reaches your prospects wherever they are.

OneShot's AI is constantly learning and evolving. They track the performance of their outreach campaigns, using reinforcement learning to identify what works best and adapt their strategies accordingly. This ensures that your outreach efforts are always optimized for maximum impact.

Two Sides of the Coin

AI-powered outreach has two dimensions:

• Sender: Businesses using AI to send messages face concerns about transparency and approval. OneShot offers options like co-pilot and autopilot modes, allowing users to review and personalize AI-generated content before sending.
• Receiver: While some may find AI-generated messages impersonal, many appreciate the research and personalization they offer. It's a shift from generic greetings to targeted content that sparks genuine interest.

Humanizing the Bots

Generic AI solutions won't work long-term. Businesses need to:

• Customize Language: Adapt the AI's tone and style to your brand voice and audience. Imagine tailoring messages for CTOs versus individual contributors.
• Leverage Business Data: Train AI models on your specific data to generate messages that resonate with your ideal customers.

A Future of Collaborative AI

AI isn't replacing humans, it's augmenting them. Imagine:

• AI handling the legwork: Researching prospects, crafting initial messages, and automating repetitive tasks.
• Humans adding the personal touch: Reviewing and refining AI output, building relationships, and closing deals

How to Transform 3 Common Intent Signals Into Opportunities with AI

Revealing intent is one thing. Actioning it is another. In this post we’ll explain how to use take three common intent signals and use them to create hyper relevant and personalized outreach.

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Unique pricing model @ OneShot

People, Not Sales: A Different Approach to Pricing

Unlike traditional sales software, OneShot doesn't charge based on closed deals. They focus on what they can directly control: the number of prospects you reach. Why this approach?

• Focus on the Journey: Reaching the right people is crucial for sales success, even if not every interaction leads to a deal.
• Transparency and Fairness: Businesses only pay for outreach efforts, not for unpredictable outcomes like closing rates.

Beyond Just Numbers: Tailoring Value

It's not just about quantity; OneShot considers the quality of outreach as well. Their pricing reflects:

• Research Depth: Do you need basic info or an in-depth analysis of each prospect?
• AI-Powered Messages: How many personalized messages will you send per prospect?
• Multi-Channel Reach: Email, LinkedIn, calls – choose the channels that fit your strategy.

A Model for Mutual Success

This pricing structure benefits both OneShot and its customers:

• OneShot: They're incentivized to deliver high-quality leads and engagement.
• Customers: They only pay for the outreach they need and see the value in each potential connection.

Tech Stack @ OneShot

From Langchain to Building Their Own

OneShot started with Langchain, a popular AI/ML toolkit. While it offered a good foundation, it lacked the flexibility needed for OneShot's specific needs. So, they're transitioning back to a custom-built solution that allows them to create things like a "gateway" for connecting to multiple AI models.

The Quest for Perfect Embeddings

OneShot searches through a massive database of 40 million companies to find the perfect fit for each business. To achieve this, they use various tools:

• GPT Embeddings: These capture the meaning of text and help find relevant companies based on keywords and descriptions.
• Pinecone & Other Vector Databases: These store the embeddings efficiently and enable fast searches.
• ChatGPT Models: These analyze the user's query and identify the most relevant knowledge articles.
• Switching LLMs on the Fly: Depending on the task, OneShot can use different models like GPT-4 or Claude.

Leveraging Open Source

OneShot doesn't go it alone. They leverage open-source tools like yours for fine-tuning models and hosting solutions, ensuring efficiency and access to the latest advancements.

Challenges and Lessons Learned

Building an AI platform isn't without its hurdles. OneShot faces challenges like:

• Balancing Flexibility with User-Friendliness: Catering to both tech-savvy and non-technical users requires careful design choices.
• Keeping Up with the Evolving LLM Landscape: New models and tools emerge constantly, requiring adaptation and exploration.
• Monitoring and Maintaining the AI Engine: Ensuring smooth performance and reliability is crucial for user trust.

The Future of OneShot:

OneShot currently relies on its API to connect to AI models, but they're exploring new horizons. As they scale, they're considering integrating with platforms like Azure OpenAI to access a wider range of models.

However, OneShot is constantly innovating. They're exploring possibilities like:

• Bringing Your Own LLM: Allowing users to choose their preferred models for even more customization.
• MLOps Integration: Leveraging platforms for robust monitoring and management of their AI infrastructure.

By embracing flexibility, tackling challenges, and staying ahead of the curve, OneShot is building a powerful and accessible AI platform that empowers businesses to achieve success.

How OneShot Manages LLMs with Prompts

Think of prompts as instructions or questions that help the LLM understand what information you're looking for.

OneShot doesn't use a one-size-fits-all approach. They provide different prompts for different uses:

• Lead Qualification: Is this the right person to contact? Prompts help the AI analyze data and give you a clear yes or no answer.
• Information Extraction: Summarize key points about a prospect's business. Prompts guide the AI to find relevant information and present it concisely.
• Sales Content Creation: Craft personalized emails, LinkedIn messages, and call scripts. Prompts help the AI tailor the content to your specific prospect and desired tone.

OneShot empowers its users by giving them control over prompts. You can choose the model to run the prompts on and adjust settings like "creative" or "deterministic" to fine-tune the results.

Full-Stack Goes AI: How LLMs are Redefining Development

Remember the days when "full-stack developer" meant juggling front-end and back-end? Well, move over, because AI is now part of the equation! LLMs are completely changing the development landscape:

1. AI is the new "full-stack": It's not just about code anymore. Developers now need to understand AI functions, embeddings, and MLOps platforms. The entire pipeline is AI-infused!

2. AI is commoditized: Building and using AI models is easier than ever thanks to user-friendly platforms. Developers can now focus on fine-tuning and customization.

3. Chatbots are the new co-pilots: Gone are the days of endless Google searches. Engineers can now leverage AI assistants like ChatGPT to write better code and boost their productivity.

How AI SDRs Will Impact Sales Development in 2024

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Futureproof Your Business: Leveraging LLMs for Success

The world of AI is evolving rapidly, and large language models (LLMs) are at the forefront of this change. But with so much innovation happening, it can be hard to know where to start and how to prepare your business for the future.

LLMs are no longer a futuristic concept. They're here, they're accessible, and they're offering real value across various business departments. Whether it's boosting employee productivity in sales and marketing, or creating personalized experiences for customers, integrating LLMs into your workflow can lead to significant competitive advantages.

However, simply implementing generic LLM solutions. To truly unlock their potential, you need to customize them to your specific business processes. This involves fine-tuning models, incorporating human expertise, and utilizing efficient MLOps platforms for continuous improvement. Remember, LLMs are tools, and your expertise in applying them is what sets you apart.

Practical Advice for readers:

• Start small and experiment: Don't try to boil the ocean. Choose a specific business challenge where LLMs can provide clear value, and begin experimenting with different models and applications.
• Focus on customization: Remember, one-size-fits-all solutions rarely work. Invest time in understanding your unique needs and tailoring LLM solutions to your specific context.
• Build a culture of continuous learning: The LLM landscape is constantly evolving. Stay informed, explore new developments, and encourage your team to embrace lifelong learning in this dynamic field.

Read our previous blogs in the True ML Talks series:

GenAI and LLMOps for Customer Success @ Level AI

Learn how Level AI leverage GenAI and LLMOps to solve Customer Success and Quality Assurance from Abhimanyu, Staff AI Research Engineer @ Level AI

TrueFoundry BlogTruefoundry

Keep watching the TrueML youtube series and reading the TrueML blog series.

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