All case studies
Approach

AI that pulls its weight, not a chatbot bolted on for show.

Most "AI features" are decoration — a chat window nobody asked for, generic tips that could apply to anyone. We only build AI in where it replaces real busywork or surfaces something specific a person would've otherwise missed.

LLM Integration Real-Time Data Custom Prompting Human-in-the-Loop
AI INSIGHTS You're spending 22% above your dining average Reallocate $200/mo → ~$14k over 10 years Graph questions are costing you the most marks Repeated mistakes cluster in data-analysis questions Built from a user's own data — not generic advice SOURCE Finance app GAMSAT platform Reviewed by the user before acting

The problem with most "AI" features

Most AI integrations are gimmicks: a chatbot bolted onto the sidebar, a "summarize" button nobody asked for, generic advice that could apply to anyone. It's AI for the sake of having AI, not because it makes the product better.

Our approach

We only add AI where it replaces something tedious or surfaces something specific a person would've otherwise missed — not generic advice, but insight built from their own data. Two examples from builds we've shipped:

Personal finance & net worth planner

Instead of generic budgeting tips, the AI reviews a user's own spending and income history and flags exactly where money's leaking or where there's room to invest more.

  • "You're spending 22% above your 3-month dining average" — not "spend less"
  • Reallocating $200/month into an existing investment compounds to roughly $14,000 over 10 years — a specific number tied to their actual finances
  • Cross-references real-time market and news events against the user's actual holdings, so what surfaces is relevant to their portfolio, not a generic news feed sitting next to it

Read the full case study →

GAMSAT prep platform

Instead of a generic "your weakest section is Reasoning," the AI looks at time spent per question, patterns in repeated mistakes, and specific question types.

  • Flags that graph-interpretation questions are the actual problem — not the section as a whole
  • Surfaces repeated mistake patterns across practice sessions, not just a final mark
  • Turns a raw score breakdown into a specific, actionable study plan instead of a generic mark-based report card

Read the full case study →

The bar for shipping an AI feature: would a person actually miss it if we removed it? If the honest answer is no, it doesn't ship.

The technical foundation

IntegrationLLM-Powered Insights
DataReal-Time Data Feeds
ValidationStructured Output
AccessPer-User Data Scope
ReviewHuman-in-the-Loop
HostingVercel

AI features are only as good as the data feeding them and the constraints around them. Every insight is generated from the user's own data, validated with structured output instead of free-form text, and framed as a suggestion — the person using the app still makes the call.

The outcome

AI that quietly does its job — flags what's actually worth flagging, tells you something you didn't already know, and gets out of the way the rest of the time.

Want something built like this?

Book a free 30-minute call — no pitch deck required.