Guide
Rapid AI prototyping for teams without in-house engineering capacity
You have an idea for an AI tool. Something that could actually help your business or your users.
But you don't have a technical co-founder. You can't afford to hire a development team. And you need proof it works before committing serious budget.
Sound familiar?
This guide shows you how to go from idea to working prototype in 3-7 days, using AI-assisted development tools and a simple, testable approach.
Who this is for
Teams in creative, cultural, or research-driven organisations who need a credible AI pilot fast, but do not have in-house engineering capacity.
Examples: Music labels needing catalogue tools, research teams testing AI workflows, cultural organisations exploring visitor engagement, and agencies testing client-facing prototypes.
Common obstacles
- Ideas stall because requirements are vague or too broad.
- Experiments happen in notebooks, not in front of users.
- Costs and risks escalate before you see evidence of value.
- No shared criteria for "this is worth funding further".
Scoping and success criteria
- Define one user, one job-to-be-done, and one primary success metric (time saved, accuracy, quality).
- Limit data sources and formats; avoid brittle scraping when a CSV upload works.
- Decide early what "good enough" looks like for a first cohort of testers.
A lean prototyping approach
- Frame the user problem and success criteria in a one-page brief.
- Design a thin workflow with guardrails (data handling, failure modes, human review).
- Build a narrow slice with real inputs and outputs, not a slide deck.
- Test with a small cohort; measure time saved, quality, and satisfaction.
Managing risk and quality
- Handle sensitive data carefully: prefer redacted/sample data; document what is sent to vendors.
- Design failure modes: what happens when the model is wrong or cannot answer?
- Add human review for any external-facing output; track overrides to improve prompts.
Example prototype patterns
- Research and summarisation assistant with citation trails.
- Content generation with brand guardrails and approval steps.
- Data-to-brief converters (e.g., survey inputs → creative briefs).
- Evaluation tools that score drafts against rubrics you define.
Build approach and stack
- Use a simple web front end plus a small API layer for prompts and routing.
- Keep storage minimal (a lightweight database or spreadsheet-backed store) until you see traction.
- Instrument logs: prompts, responses, user actions, and errors so you can iterate quickly.
- Prefer hosted models and APIs initially; self-host only if privacy, compliance, or cost demands it later.
Validation with real users
- Recruit 5–10 target users; watch them use the tool on real tasks.
- Measure completion time, satisfaction, and frequency of human overrides.
- Capture where the assistant hesitates or hallucinates and patch prompts/workflow.
- Decide go/no go based on evidence, not enthusiasm.
What we deliver
- A validated, narrow prototype built on your real workflow.
- Documentation: architecture, prompt libraries, failure modes, and handover notes.
- A go/no go decision framework for next investment steps.
- Optional support to harden the prototype or hand off to your team.
Next steps
Start with a short, paid Discovery & Governance Sprint so we scope one narrow, high-signal pilot and keep costs contained while you get real feedback from users.