What This Template Covers
The development agency vendor evaluation scorecard covers eight evaluation criteria, each with a defined scoring rubric and a weight that reflects its relative importance in the overall vendor decision. The AI and technical expertise criterion evaluates whether the agency has genuine depth in the specific technologies required for the project. For an AI SaaS product, this includes LLM integration, RAG architecture, prompt engineering, and AI evaluation practices. Broad web development experience is necessary but not sufficient. The pricing model criterion evaluates the commercial structure. Fixed-price, time-and-materials, and retainer models have different risk profiles for the buyer. Understanding the model and its implications is essential before comparing prices. The communication quality criterion evaluates how the agency communicates during the sales process, since this is a reliable predictor of how they will communicate during the delivery process. Agencies that respond slowly, give vague answers, or send generic proposals during sales typically do the same during delivery. The references criterion evaluates the quality and relevance of the agency's past client relationships. References from clients in similar sectors, at similar project complexity, are more valuable than references from very different contexts. The code quality and ownership criterion evaluates whether the agency produces code that meets professional standards and whether code ownership is transferred cleanly to the client. The security posture criterion evaluates whether the agency builds security into the development process rather than bolting it on after delivery. The delivery track record criterion evaluates whether the agency delivers on time and to specification based on verifiable evidence. The GDPR and compliance awareness criterion evaluates whether the agency understands relevant UK and EU regulatory requirements for the type of product being built.
How to Use This Template Step by Step
Step one: define your project requirements clearly before starting agency evaluation. The scorecard is only useful if you are comparing agencies against a specific project, not in the abstract. Write a one-page project brief that covers: what you are building, the technology requirements, the timeline, the budget range, and any specific compliance or regulatory requirements. Step two: send the brief to three to five agencies you are considering. Request a written proposal from each that addresses: their understanding of the project, their recommended technical approach, their team structure for the engagement, a timeline with milestones, a pricing breakdown, and examples of similar work they have delivered. Step three: complete the scorecard for each agency based on their proposal, a scoping call, and reference checks. Score each criterion on a scale of one to five using the rubric in the template. Apply the weight for each criterion to produce a weighted score. Sum the weighted scores for a total comparable rating. Step four: conduct reference checks before making a final decision. For each agency shortlisted, speak to two to three previous clients. Ask specifically: did the project deliver on time and on budget, how did the agency communicate when problems arose, what does the code look like (ask a technical person to assess this if you are non-technical), and would you hire them again? Step five: review the code from a previous project if possible. Ask the agency to share a GitHub repository from a comparable project. Have a technical person review the code quality, the documentation, the test coverage, and the security practices. This is the most reliable predictor of what your codebase will look like after the project. Step six: make the selection decision based on the scorecard result and your qualitative assessment of the relationship. The scorecard produces a comparable ranking, but the working relationship also matters. An agency with a slightly lower score but better chemistry and clearer communication during the sales process may be a better choice than one that scored marginally higher but communicated poorly.
Section-by-Section Walkthrough
The AI and technical expertise section should probe beyond surface-level claims. Any agency can say they do AI. The questions that reveal genuine expertise: Can they describe a specific RAG architecture they have built and the trade-offs they made? How do they handle prompt versioning and evaluation? What happens when the LLM returns an unexpected response? How do they manage AI API costs at scale? Agencies with genuine AI depth will answer these questions specifically. Agencies with limited AI experience will give vague or generic answers. The pricing model section should evaluate three dimensions: transparency (is the pricing model clearly explained and easy to understand?), risk allocation (who bears the risk if the project takes longer than expected?), and alignment (does the pricing model incentivise the agency to deliver a good outcome or just to bill hours?). Fixed-price models like SpeedMVPs' (from GBP 8,000) allocate delivery risk to the agency, which is the buyer-friendly structure. Time-and-materials models allocate the risk to the buyer. The communication quality rubric should be assessed based on interactions during the sales process. How quickly did they respond to the initial enquiry? Did their proposal address the specific requirements or was it a generic template? Did their scoping call questions demonstrate understanding of the project context? Were their answers to technical questions specific and confident, or vague and hedged? The references section should assess both the quality of the references (are they real, reachable clients in comparable contexts?) and the depth of the feedback. An agency that provides three references but all give identical, one-sentence testimonials is different from one where references give detailed, specific accounts of the project experience including how problems were handled. The code ownership section is particularly important. Some agencies build on proprietary platforms or retain licensing rights to components. Ensure the proposal and any contract clearly states that all code, all intellectual property created during the engagement, transfers to the client on delivery. SpeedMVPs transfers full code ownership at handover. This should be a minimum requirement for any agency on your shortlist.
Common Mistakes This Template Prevents
The most common agency selection mistake is selecting on price alone. The cheapest agency is almost never the cheapest outcome. A lower hourly rate combined with longer delivery time, more revision cycles, lower code quality, and poor communication produces a worse outcome at higher total cost than a higher-rate agency that delivers cleanly and on time. The scorecard's weighted criteria ensure that price is one factor among several rather than the deciding factor. The second mistake is not checking references. Agencies do not provide references who will give negative feedback. But a structured reference call with specific questions (not "would you recommend them?" but "tell me about a moment when the project was challenging and how they responded") extracts genuine insight that surface-level testimonials do not. The third mistake is not assessing code quality directly. Non-technical founders often evaluate agencies entirely on proposals and presentations, with no review of actual code output. A technical adviser, a freelance engineer, or a CTO candidate can review a sample codebase in one to two hours and give you a reliable assessment of the agency's quality standards. The fourth mistake is not addressing IP ownership before signing. Discovering after delivery that the agency retains rights to components or tooling they built during the project is a serious problem. All contracts should transfer full intellectual property in the work product to the client. Review this clause explicitly.
Customisation Tips for Different Project Types
For regulated sector projects (fintech, healthtech, legaltech), add a regulatory knowledge criterion to the scorecard. The agency needs to demonstrate that they understand the specific regulatory environment your product operates in: FCA requirements for fintech, NHS Digital data standards for healthtech, GDPR obligations for any UK product handling personal data. A technically excellent agency that does not understand the regulatory context will build a technically excellent product that fails its compliance review. For enterprise projects with complex integration requirements, add a systems integration criterion. The agency should demonstrate specific experience integrating with enterprise systems (Salesforce, SAP, Microsoft 365, legacy internal systems). Integration complexity is a significant delivery risk and the agency's track record on comparable integrations is a meaningful signal. For products that will need ongoing development after the initial build, evaluate the agency's handover process as a scored criterion. What documentation do they produce? How do they transfer knowledge to the internal team or to a second agency? What is the typical onboarding time for a new engineer to become productive with their codebase? Agencies that produce clean, well-documented handovers are a much better choice if you need continuity of development. For AI-specific agency evaluation, add a criterion for AI evaluation practices. Does the agency have a systematic approach to evaluating AI output quality? Do they build evaluation datasets as part of the project? How do they test prompt changes? Agencies with mature AI evaluation practices produce more reliable AI products than those that rely on manual spot-checking.