Best AI code assistant for a 10-person startup on a tight budget

Hello everyone,

I've been lurking for a while, but this is my first post. I'm an accountant for a very small startup (we're exactly ten people, including founders), and I'm increasingly tasked with bridging gaps between our financial data and the operational tools we're building. We have a tight budget, which means we can't afford expensive enterprise licenses or dedicated developers for every little automation or integration. I've been researching AI code assistants to help with scripting, especially for Google Sheets, basic data pipelines, and connecting our accounting software (a mid-tier cloud ERP) to other platforms.

I've tested a few popular AI assistants over the last month, trying to get them to generate practical, working code for our specific, constrained environment. I've hit some consistent and reproducible failures that I think fit this forum's theme. My goal here is to share my detailed experience to see if others have found workarounds or if I'm simply expecting too much from the current tools.

Here’s a concrete failure case I encountered repeatedly:

**The Prompt (to multiple AI assistants):**
"Write a Google Apps Script function that takes a date string from a cell, checks if it's a valid date and within the current fiscal quarter (Q1: Jan-Mar, etc.), and returns TRUE or FALSE. Assume the date string format is 'MM/DD/YYYY'. Handle potential errors if the cell is empty or contains text."

**The Common, Flawed Output:**
The assistants would typically generate a function that used `new Date(dateString)` and then tried to calculate the quarter. However, the failure was in the date parsing. For example:
- They rarely accounted for the fact that `new Date('MM/DD/YYYY')` with that exact format can have inconsistent behavior across browsers/Apps Script's V8 runtime, sometimes returning an invalid date object.
- The quarter logic often used hard-coded month numbers without adjusting for a fiscal year starting in January, which was correct for my ask, but they'd fail to validate the date *first*.
- Most critically, the error handling for empty cells was often just a `if (!dateString) return false;`, which would also incorrectly return FALSE for a valid string like "01/01/2024" because it wasn't a truthy check for an empty *cell object* from `range.getValue()`.

**The Actual Correct Approach:**
After significant manual debugging and reading the Apps Script docs, a robust version needed:
1. Explicit parsing of the date string components using `Utilities.formatDate()` or splitting the string.
2. Validation using `Date.parse()` or checking if the constructed date object is `NaN`.
3. Then, and only then, calculating the current date's quarter and comparing.
4. Proper handling of the cell value being an actual Date object (which Apps Script can return) versus a string, and distinguishing an empty cell (`""`) from a cell with content.

This seems like a small thing, but it's representative. The AI would give me a 90% solution that would fail silently in edge cases—precisely the kind of thing that causes major reconciliation headaches for me later. The logic *looked* sound, but the practical implementation details for the specific environment (Apps Script's quirks with dates) were wrong.

My broader question, which relates to the thread title, is:
- For a budget-conscious team needing reliable, *environment-aware* code (not just generic algorithms), which assistant has proven most accurate for you?
- Are there specific techniques or prompting styles you use to force the AI to consider runtime environment and data validation more rigorously?

I'm particularly interested in experiences with:
* Automating finance-related spreadsheets (complex formulas, ARRAYFORMULA, Apps Script triggers).
* Generating scripts for data extraction from APIs (like QuickBooks, Stripe) where authentication and error handling are crucial.
* Basic compliance scripts (e.g., ensuring invoice records have all required fields before batch processing).

I’m methodical by nature, so I don't mind detailed responses! I want to choose a tool I can trust to get the specifics right, not just the broad concept. The cost of an error in our financial data automation is simply too high for us.