Approximate percentile cutoffs by total scaled score: 110+ for 99.9, 96 for 99.5, 85 for 99, 64 for 95, 53 for 90, 41 for 80.
Your raw score is not your scaled score. Depending on your slot, sectional scores get adjusted before percentile calculation.

Scaling direction by slot and section
VARC: Slot 2 scales up (hardest). Slot 3 scales down (easiest). Slot 1 roughly neutral.
DILR: Slot 1 scales up significantly (hardest). Slot 2 scales down (easiest). Slot 3 scales up with high variance.
QA: Slot 2 scales up slightly. Slots 1 and 3 roughly neutral.
DILR Slot 1 has a scaling factor around 1.13. DILR Slot 2 is around 0.93. This translates to 2-4 marks difference on scaled sectional scores.

The problem
CAT runs across 3 slots with different question papers. Difficulty varies. Comparing raw scores directly would disadvantage candidates who got a harder slot. IIM normalizes using equipercentile equating.

How CAT scaling works
The official scaling formula for each section is:
R̂ = (R - Gslot) × [(M⁰·¹ - G) / (Mslot⁰·¹ - Gslot)] + G
Where:
R is your raw score in that section.
R̂ is your scaled score.
G is the overall baseline: mean plus standard deviation across all candidates in that section.
Gslot is your slot's baseline: mean plus standard deviation for candidates who took your slot.
M⁰·¹ is the 99.9th percentile (top 0.1%) score across all candidates.
Mslot⁰·¹ is the 99.9th percentile score for your specific slot.

The formula does three things:
First, it measures how far your raw score R sits above your slot's baseline Gslot. This is the (R - Gslot) term.
Second, it scales that distance by comparing how the top 0.1% in your slot performed versus the top 0.1% overall. If your slot was harder, top performers in your slot scored lower than the overall top performers. This ratio amplifies your distance above baseline. If your slot was easier, this ratio compresses it.
Third, it anchors the result to the overall baseline G, making scores comparable across all slots.
This is applied independently for VARC, DILR, and QA. The three scaled sectional scores are summed to get your total scaled score, which maps to percentile.

Our approach
We worked with approximately 10,000 CAT score submissions based on actual response sheets and official answer keys. Submission is voluntary, which introduces selection bias toward higher scorers.
From this sample, we estimated section-wise and slot-wise parameters. We validated against known cutoffs and calibrated where needed. For percentile mapping, we used empirical CDF for the upper tail and extrapolated for lower percentiles to account for the full 2.5 lakh population.

Limitations
We are estimating population parameters from a biased sample. Our estimates are approximations. Your actual result depends on official normalization using complete candidate data.

Unsolicited advice
Estimated percentile means exactly that: it is an estimate. The modeling process has taught us the bias you can introduce into the process - no calculator will be on point, even if they claim to be, or have been for all the past years and have a LOT of data - things can still go wrong.

Chill, focus on XAT, and let sleeping dogs lie. 

Insights into normalisation and our estimates of scaling across slots