What This Template Covers
The MVP pitch deck outline covers twelve slides that represent the standard structure expected by UK pre-seed investors, with specific guidance for AI product positioning. The cover slide establishes the company name, a one-line value proposition, and the founder's contact details. It is the first impression and should be clean, not cluttered. The problem slide defines the specific problem you are solving, who has it, and how significant it is. The best problem slides make investors nod because they recognise the problem from their own experience or from their portfolio companies. The solution slide introduces your product and how it solves the problem. For AI products, this slide must be specific about what the AI does, not just that it uses AI. The market size slide quantifies the opportunity. TAM (total addressable market), SAM (serviceable addressable market), and SOM (serviceable obtainable market) are the standard framework. The product slide shows the product. Screenshots, a live demo, or a short video. Investors fund products they can visualise. The traction slide presents evidence that the hypothesis is correct. At pre-seed, traction can be user interviews, letters of intent, waitlist sign-ups, pilot agreements, or early revenue. The business model slide explains how the company makes money. The go-to-market slide explains how you will reach the first paying customers. The competition slide shows you understand the competitive landscape and have a credible position within it. The team slide presents the founding team and why they are the right people to execute this. The financials slide covers the use of funds and the financial projections for the raise period. The ask slide states clearly how much you are raising, at what terms (if known), and what you will achieve with it.
How to Use This Template Step by Step
Step one: start with the problem slide, not the cover. The problem definition is the foundation of everything else in the deck. Before you write any other slide, write three sentences describing the problem: who has it, what form it takes, and what the cost of the problem is. If you cannot do this clearly, the rest of the deck will be weak. Return to this slide last and make sure every other slide connects back to it. Step two: write the solution slide next. Describe what your product does in plain English. For AI products, describe the specific AI-enabled action: not "uses AI to improve efficiency" but "analyses incoming support tickets, categorises them automatically, and surfaces the relevant knowledge base articles for the agent's review." The more specific, the more credible. Step three: validate your market size numbers. Do not use top-down TAM figures from expensive analyst reports without grounding them in bottom-up logic. Show the calculation: "There are approximately 45,000 independent financial advisers in the UK. The average IFA firm spends roughly GBP 15,000 per year on compliance reporting tools. That is a GBP 675 million UK market." Investors will question your market size figures. Have the calculation ready. Step four: prepare your traction slide carefully. Pre-seed traction is rarely revenue. More often it is: user interviews with specific quotes, a waitlist of named potential customers, a letter of intent from a named pilot customer, early beta users with engagement data, or a pilot agreement with a recognisable brand. Present what you have honestly, with specific numbers. Ten engaged beta users who have given detailed feedback is more credible than 500 waitlist sign-ups who have never used the product. Step five: write the team slide with the right framing. Investors at pre-seed are funding people as much as ideas. The team slide should answer: why are you the right people to solve this problem? This means relevant domain expertise, technical depth (or a credible plan to fill the technical gap), and evidence of the ability to execute. Step six: prepare the financials slide with realistic projections. Pre-seed investors do not expect precise financial projections. They expect logical projections that reveal the unit economics and the assumptions driving the model. Show your assumptions explicitly. What is the average contract value? What is the expected sales cycle? What is the gross margin? What does the team cost? When do you run out of the raised money if growth is slower than projected? Step seven: draft the ask slide last. State the amount (GBP X), the use of funds broken into categories (product development, sales and marketing, team), and the milestones you will hit with this funding that justify the next raise. Pre-seed investors are funding you to reach Series A metrics. Your ask should be calibrated to give you 18 to 24 months of runway.
Section-by-Section Walkthrough
The problem slide should not include the solution. The moment you introduce your product on the problem slide, you lose the narrative tension that makes investors lean forward. Spend the full slide on the problem. Use concrete examples, data points, and if possible a quote from a real potential customer describing their pain. The solution slide for an AI product should address the scepticism around AI claims directly. Investors in 2025 have seen hundreds of decks claiming AI-powered everything. What makes your AI claim credible: is it proprietary data, a specific fine-tuned model, a novel prompt architecture, or a workflow design that the AI enables more effectively than an alternative approach? Name the differentiation specifically. The market size slide should use the bottom-up calculation approach for the SOM figure even if you use top-down for TAM. Show that you have thought about which specific customers you will target first and how many of them there are. A realistic SOM of GBP 5 million with a credible path to capture is more compelling than a TAM of GBP 50 billion with no discussion of how you get there. The traction slide should quantify everything that can be quantified. "Strong user interest" is a weak signal. "We have conducted 40 user interviews with target customers, of whom 28 said they would pay GBP 200 per month for a product that does X, and 6 have signed letters of intent" is a strong signal. At pre-seed, qualitative evidence (quotes, conversations, relationships with relevant people in the sector) supplements the quantitative evidence and is expected. The competition slide should not position competitors as "old" or "failing." Investors often know the competitors better than you do. Instead, show the landscape honestly and articulate the specific wedge you are entering with: the customer segment your competitors underserve, the capability they lack, or the price point they do not address. The team slide at pre-seed is often one or two founders. Be honest about gaps. If the technical expertise is being covered by a development agency for the MVP build (for example, SpeedMVPs building the initial product), say so and explain the plan to bring technical ownership in-house. Investors respect founders who know what they do not know.
Common Mistakes This Template Prevents
The most common pitch deck mistake is building a 25-slide deck because you want to include everything. Pre-seed pitch decks should be 10 to 14 slides. Investors are reviewing hundreds of decks. A concise deck that covers the essentials clearly is much more effective than a comprehensive deck that buries the key points in detail. This template's 12-slide structure is the right ceiling. The second mistake is leading with technology rather than problem. Decks that open with the AI architecture, the LLM integration, or the technical roadmap before establishing what problem is being solved give investors no emotional hook. The problem must come first. The third mistake is presenting generic market size figures from analyst reports without any grounding in specific customer numbers. "The global X market is GBP 450 billion" is nearly useless without a credible path to any fraction of it. The bottom-up market calculation is more credible and more useful. The fourth mistake is including a competitive slide that dismisses all competitors. Every market that is large enough to be interesting has good competitors. A competitive slide that shows all competitors as having no strengths signals either naivety or dishonesty. Show the competitive landscape honestly and articulate a specific, defensible position. The fifth mistake is asking for an amount without specifying the use of funds. "We are raising GBP 500,000" prompts the question "for what?" The ask slide should always include the use of funds broken into categories and the milestones that the funding will achieve.
Customisation Tips for Different Project Types
For B2B SaaS products targeting enterprise, add a sales cycle and expansion revenue section to the business model slide. Enterprise investors are familiar with long sales cycles and want to see that you have thought about how to shorten them (product-led growth, a champion-driven sales motion, a pilot-to-contract process). Show the ACV (annual contract value) and the NRR (net revenue retention) target that demonstrates the expansion opportunity. For consumer products, the traction slide takes on extra importance because consumer metrics (DAU, WAU, retention cohorts) are the primary signal of product-market fit in the absence of revenue. Show retention curves, not just top-line acquisition numbers. Investors in consumer products are experienced at spotting leaky buckets. For regulated sector products (fintech, healthtech, legaltech), add a regulatory pathway slide. Investors in these sectors know that regulatory approval, FCA authorisation, or NHS procurement processes add time and cost to the commercial path. Show that you understand the regulatory requirements, what approvals are needed, the timeline for obtaining them, and the cost. A clear regulatory roadmap is a competitive advantage signal in regulated markets. For AI products with proprietary data as the core moat, make this explicit on the solution or moat slide. Proprietary data that creates a better model over time (the data flywheel) is one of the most defensible AI business models. If your product generates unique training data through user interactions, show how this creates compounding advantage that is difficult for a competitor to replicate even with significant investment.