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- Upload users.csv 6 rows · 5 columns · streamed in chunks
- Profile columns snake_case headers · dtype detection
- Standardize text trim · missing tokens · email casing · AI aliases
- Coerce numbers $72,000 → 72000
- Impute missing numeric → median · text → most frequent
- Remove outliers IsolationForest, 150 trees
- Drop duplicates row hashing across chunks
- Score & export audit report + cleaned CSV
| Full Namestr | E-Mailstr | Age (yrs)int | Citystr | Salarynum |
|---|---|---|---|---|
| Aarav Shah | AARAV@MAIL.COM | 28 | mumbai␣ | $72,000 |
| Priya Verma | priya@mail.com | N/A | Delhi | 65000 |
| John Doe | john@mail.com | 214 | New York | 88,500 |
| Aarav Shah | AARAV@MAIL.COM | 28 | mumbai␣ | $72,000 |
| Sara Ali | sara@mail.com | 31 | - | 54,000 |
| Mei Chen | mei@mail.com | 29 | new york |
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Token refresh is handled in auth-service/src/refresh.ts (line 142). It uses a 7-day sliding window defined as AUTH_TTL in config/constants.ts.
4 downstream services depend on this: api-gateway, user-service, session-mgr, mobile-relay.
Lossless Payload Optimisation to Your Models
Ration Your Tokens. Same data, a fraction of the tokens. Lossless payload optimization preserves every bit of meaning. Lower costs. Faster inference. The wire format itself stays sealed.
✓ Registry engaged — the seed lives server-side. Every request ships diffs only.
✓ LLM-native engaged — the model reads SeedFmt directly. No decode step.
Fix your data before it breaks your model.
CAT scans every column for nulls, outliers, type errors, and inconsistencies. Repairs are staged — you approve before anything is committed.
Nothing ships without your sign-off.
Every fix is diffed and auditable. Approve, reject, or edit each repair individually or in bulk before committing.
The last line of defense before your data reaches the model.
Only clean, trustworthy data reaches your model. Nothing else.