Waiga Arya
Sadaway Pvt. Ltd., formerly Indus Cosmeceuticals
I run growth for an organic beauty and personal care manufacturer in Faridabad, India. The business began in 1976 as a research and analytical laboratory. Fifty years later it makes and sells Indus Valley Organic Beauty, MINA IBROW and Bio Organic.
My job is to take a fifty year old Indian manufacturer into international markets. In practice that means the brands, the channels, the unit economics, new product development, and hiring the people who run it.
Background
I trained as a cosmetic scientist before moving to the commercial side of the industry, which is why I tend to argue about formulation and margin in the same meeting. I hold an MBA from the Yale School of Management.
Before this I worked in marketing and strategy at L'Oreal in Paris, at Supercilium in Amsterdam, and with Parfum Nicolas de Barry in France. Earlier still I competed as a ranked junior tennis player on the Indian national circuit, which is the other Waiga Arya you will find in a search. Same person.
Tools I have published
I direct the building of small tools that check what people already produce, from meeting transcripts to price lists. For each tool I decide which real files it is measured against, and that the results which do not flatter it are published beside the rest. AI agents write the code and the documentation.
- Follow Through finds the commitments people make out loud in a meeting transcript and tracks each one until somebody closes it. Reads English and Hinglish, because most meetings in India are not held in one language.
- Show Your Work finds the numbers in a spreadsheet that nobody can explain: a formula someone typed over, a total that stops one row short, a cell doing something different from its column.
- Before You Send reads a PDF and reports what is still inside it that you may not mean to send: text under a box that was never removed, an earlier draft, a name in the properties.
- Adds Up reads a price list and reports the arithmetic in it that contradicts itself: a volume tier where buying more costs more per unit, the same item priced two different ways, a stated discount that does not reconcile with the prices beside it. It never says whether the pricing is right.
- On The List reads a cosmetic ingredient list and reports what the EU's official annexes say about the ingredients on it: a name on the prohibited list, a colour printed out of position, the same ingredient entered twice. It reports a match as a match, never as a verdict on the product.
- Says On The Tin finds where a cosmetic label contradicts itself: a free-from claim on the front, and an ingredient on the back that breaks it. Paraben-free over a list containing methylparaben, vegan over carmine. It never says a product is compliant or clean.
- On Notice reads a cosmetic ingredient list and reports which ingredients on it are named in an EU rule that carries a date still ahead of it. The others check what is wrong today; reformulation takes the better part of a year, so the question that costs money is what becomes wrong while the stock is still on the shelf. It reports dates, never a verdict on whether a product may be sold.
- Kick The Tyres reports checkable evidence about a public code repository, and reports what it could not check as unknown rather than as a clean bill of health.
- Said Who is a memory store for AI agents that refuses to save a claim about what a person decided unless it can point at a real message that person typed and quote their words back. It supports Claude Code and nothing else, and says so in its first paragraph.
Writing
A fresh AI memory can still be false · October 2026. Why freshness cannot repair false provenance at creation, what Said Who refuses, what it still cannot prove about interpretation, and why its own public example had to be corrected before this story was published.
Every fixture in the suite used a classic xref table · September 2026. All eight tools above, set beside each other: each had a test suite that passed, each was wrong anyway once it met files nobody involved had chosen. Six of the eight can show that from the repository alone and two cannot, and the piece names which two and why. Also the five kinds of blindness the failures sort into, the one kind real files rarely catch because a silent tool looks the same either way, and the disclosure that these tools were built with an AI assistant, recorded on 146 of their 148 commits.
Two of my scanner's four verdicts were impossible to reach · September 2026. A repository scanner that was opening 8.3% of the bytes of the median repository and reporting nothing found, and why those two reports look identical to a reader. Also the enumeration of 3,359,232 input combinations that showed two of its four verdicts could never be printed by any command in it, and the README that was honest about one of those two and wrong about the other.
676 published documents kept their own history. Six kept different words · September 2026. 838 published PDFs from the US Federal Register, gov.uk, the WHO and arXiv, and how much of its own past each one still carries. 676 keep an earlier version of something. Only six keep different words. Also why every Federal Register document looks alarming and none of them is, and the two wrong numbers that had to die before the six could be trusted.
Lawful today, and non-compliant on 1 January 2027 · September 2026. A rule adopted in April 2026 starts applying on 1 January 2027, and 3,050 of 17,595 published cosmetic ingredient lists already name a substance it restricts. Also the three readings of the law that had to be corrected first, each of which had made the tool look better than it was.
Thirty correct findings, and only one looked like a mistake · September 2026. What a small price list checker found when it met 29,521 published tariff price lists and 200 hospital charge files: thirty hand checked findings all correct, only one of them looking like a real mistake, and the defects the real files exposed that its tests never did.
An ingredient list cannot tell you most of what you want to know · September 2026. What 16,635 real published cosmetic labels say when they are checked against the European Commission's own register, and what a hand audit of 30 repeated entry findings found, including 21 false positives and the limits of that sample.
Elsewhere
All nine install with pip: adds-up, before-you-send, follow-through, kick-the-tyres, on-notice, on-the-list, says-on-the-tin said-who and unexplained-cells, which is Show Your Work under a name of its own.