In recent years, we have seen incredible shifts in the technology landscape – these shifts have opened up a new world of possibilities for what small teams can now accomplish.

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As operators that have worked closely in and around these new capabilities, we have published below a set of Operating Principles (OP) that we have developed internally that guide how we operate as a team every day.

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OP#1 – With automation, second order effects can have a transformational impact:

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When implementing automation, we must consider the second-order effects that result from automating a given workflow. Let’s look at a specific example.

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We receive “X” number of inquiries from prospective customers every day. It takes one person about 2-3 hours to respond to these. The impact of automating this is by no means limited to the time saved. Let’s review some of the second-order effects:

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• Speed and Quality – Relative to humans, AI is much better equipped to write detailed and thoughtful responses around common questions related to product, pricing or value proposition. AI is faster, responds in real-time, and brings forward responses that are more structured and detailed relative to those written by human counterparts.
  
• Conversion & Brand Development – As a result of the above second-order effects, we are able to improve conversion and brand development.

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In brief, companies and teams that are rapidly automating core workflows can build major advantages versus ones that do not. When evaluating automation, we need to look at second-order effects to prioritise which workflows to automate first.

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OP#2 – High-calibre operators need to re-think their roles as innovators:

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In the future, high-calibre operators will focus primarily on problem-solving, and not so much on repetitive tasks that need to happen every week. In the foreseeable future, the ability to imagine new and implement new solutions will continue to be a highly valuable skill set.

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For high-calibre operators, the core job has shifted to driving innovation – imagine new solutions, implement with urgency, measure the impact, and move into higher fidelity problems. This requires that we move our focus away from doing the daily grind, and toward developing agents that can do the same faster, cheaper and better. Let’s look at a few examples of this:

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• Example #1 – Let’s say you spend 30 minutes each week to send a report to keep your team abreast of how things are trending. In the age of AI, you need to spin up an agent that can do that for you with greater precision, consistency and structure. You can also automate the inflow of data into the agent to bring full end-to-end automation, reducing your role to simply reviewing and approving the update before it goes out.
  

• Example #2 – Let’s say you spend 2-3 hours each week to ask questions from LLMs for education on key aspects of building product features. In the age of AI, you need to train an agent that can ask all the obvious questions, and then summarise everything that you need to know about that topic.

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The impact of the above two examples is not limited to the time saved. Instead, it has a huge impact on effectiveness. In the first example, AI will uncover trends and patterns that humans may not catch. In the second example, AI can have a transformational impact on raising the thinking calibre on new product features. In due time, these things add up, and effectiveness translates into better execution.

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The core spirit of OP#2 is to rethink your role as one that focuses on developing the capabilities that are required for the Company to innovate. It is a fundamental shift in how you view your role.

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OP#3 – The “Founders Office” needs to lead by example:

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As a post PMF startup operating in the age of AI, we need to execute with great precision on a broad diversity of topics.

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How does our product fit into the broader ecosystem in which we operate? In what areas of our execution do we need to think more proactively? How do we manage the delegation of key priorities and the roll-up of execution on these fronts? At startups, historically, there is limited definition and rigor around some of these core questions and internal workflows.

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With LLMs, organisations need to be designed for adaptability, and the Founders Office needs to serve as the driving force behind these adaptations. Each major adaptation is a project in and of itself, and as with most projects, there needs to be a deadline and a clear DRI (Directly Responsible Individual). In large companies, the unit driving adaptations is sometimes referred to as the “AI Transformation” team. At Metal, we refer to this as the Founder’s Office.

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OP#4 – Agents needs to be treated as internal products:

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On a daily basis, teams across the board need to spin up agents that perform various types of routine work. Each agent is ultimately an internal product that requires architectural thinking. Do we design our agents to be deterministic in nature whereby they produce defined outputs from defined inputs? Or do we design agents for flexibility whereby they can harness the core intelligence of LLMs to self-improve?

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In building an agent, we are developing an internal product. And as with any high-quality product, we need to think through the architectural, design and workflow optimisations that are characteristic of great products.

As a first-time founder, I knew two things – I had to learn about fundraising and had to find a way to get to know people who in turn knew the people that invested in startups. I started my fundraise by figuring out the former part first.

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Learning the Pre-Seed Dynamic

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I started my approach by first digging into Metal’s product documentation and content sections. I first learned the perspective that the pre-seed landscape is unique (given the limited number of investors specialising at this stage). I learned about investor expectations at pre-seed and how to zoom in on the “most likely” investors.

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On pitching investors and developing the right collateral, I was lucky and fortunate to have access to terrific mentors within the Telora ecosystem. In that context, I found myself in a unique position whereby I had the guidance and recommendations of mentors that were invested in my success financially and that had traveled the same path before.

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Building the Right Pipeline

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With access to Metal, I found it particularly straightforward to build out my pipeline. I relied on data to first identify institutional investors that specialised in pre-seed and that were particularly active. Specifically, I used %_Investments_at_Pre-seed for the former, and 12mo_Deal_Count for the latter.

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After identifying investors, I spent some time qualifying them through a three-step process. I first evaluated their investments that were most similar to my Company to assess if it was a good fit. This allowed me to eliminate a lot of investors that were focused on deep tech or other niche sectors.

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Subsequently, I looked at the geographical breakdown of investors to eliminate ones that weren’t focused on North America. And finally, I looked for investors that had done a lot of work in the “B2B Software” sector.

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Through this process, I identified Afore VC that had done a lot of work in the B2B Software space, that was focused on North America, and that was super active in leading pre-seed rounds. I wouldn’t have identified Afore if it weren’t for Metal.

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Building Access to the Right Investors

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Before starting the raise, I knew I had to connect with people that knew the people that invested in startups. Using the relationship intelligence and intro pathways within Metal, I identified VC-backed founders that I knew that had raised pre-seed rounds.

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The Gmail and LinkedIn integrations within Metal were a true game-changer. Through these integrations, I found another Telora company that knew Haven, a startup whose founders were connected with many VCs, including a principal from Afore VC.

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In total, I had less than 30 conversations (of which only about 10-15 were calls with investors). By following a super focused approach, I was able to close our pre-seed round within the first four weeks of getting started.

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In hindsight, I believe our process and overall raise effort would have been particularly directionless if we weren’t using the right software to identify, qualify and access investors.

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Zephyr Technologies is a sports analytics startup that assists sports teams with using statistical insights to drive strategy. With software processes that can find any clip from the game and that can replay specific videos, Zephyr enhances coaching decisions and team performance.

For most sectors, there is a small set of investors that specialises within that space. Such investors are easy to identify by virtue of their historical investing pattern, which shows a large percentage of total investments in a given sector. In the below post, we distinguish between sector familiarity and specialisation (while also explaining how B2B Software is a unique mega sector).

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Familiarity vs. Specialisation

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Investors that are familiar with a given sector are ones that have made several investments in that space. As an example, investors that have made a minimum of "3" investments in Fintech are familiar with that sector. Such investors can be easily identified using the "Minimum # of Investments in Selected Sectors" filter.

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Investors that specialise within a given sector are ones that are concentrating their overall investments portfolio within that space. As an example, investors that have made 10%+ of their total portfolio investments in Fintech are specialising in that space. Such investors can be easily identified using the "Minimum % of Investments in Selected Sectors" filter.

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Our product documentation brings further clarity on how to identify and when to pursue investors that specialise in a given sector (versus ones that are familiar with it).

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Historically, founders have relied on word of mouth to identify sector specialists. As the venture landscape matures, founders are increasingly relying on empirical data to determine sector specialists. This is best achieved via two distinct methodologies.

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Pursuing Sector Specialists

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In our conversations with thousands of founders that are actively raising, we observe a pattern whereby founders report high-fidelity and high-context conversations with investors that specialise within their sector. This trend seems to be most prominent with founders that are building in niche sectors (I.e. robotics, productivity software, etc.). Below, we have shared examples of sector specialists that have deep expertise within their spaces:
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• Goodwater Capital: With more than 30% investments in Consumer, Goodwater is best known for deep expertise and specialisation within Consumer.
• QED: With over 40%+ investments in Fintech, QED brings deep expertise within Fintech (enabling them to lead 50%+ of all Series A rounds in which they invest).

In most scenarios, founders find it particularly useful to engage with sector specialists that really understand the space and can engage from a position of deep industry knowledge. In some cases, however, it may become critical to pursue sector specialists:

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Recommended Treatment for B2B Software

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Historical data on venture activity shows that about ~70% of all rounds over the past decade came from companies building in the B2B Software segment. In the realm of B2B SaaS, companies should be able to target a broad variety of investment firms (as most investors are open to investing in B2B SaaS).

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Within B2B SaaS, however, there may be specific sub-sectors that require or benefit from sector specialisation. Examples may include enterprise software or productivity software whereby there may be important attributes that are unique to that sub-sector. Therefore, B2B SaaS founders might be best off considering investors that specialise within a given sub-sector.