⚡ Unpopular Opinion

You Don't Need to Score
100% of Calls

Your competitor scores 100% of calls and brags about it. You sample 40% with statistical rigor and get more accurate data at a fraction of the cost. Who's actually winning?

The Real Comparison

Big Tech QA
100%
Calls Scored
~80%
Accuracy
$$$$$
Monthly Cost
OttoQA
40%
Statistical Sample
96%
Accuracy (±3%)
$
Monthly Cost

Why Big Tech Sells "Score Every Call"

It's not because you need it. It's because their seat license model requires it.

Seat Licenses Need Volume

Big Tech charges per seat. To justify that cost to 5,000-seat centers, they have to promise "100% coverage." You're a 75-agent center paying for that same waste.

Cheap Models for Volume

To score 100% of calls affordably, they use cheaper, faster, less accurate AI models. More calls scored ≠ better data when accuracy drops.

No Statistical Improvement

After a certain sample size, scoring more calls doesn't improve your data quality. It's just waste—calls that don't move your QA metrics.

Let's Do the Numbers

A 75-agent center taking 10,000 calls/month. Who gets better data?

📊
10,000 Monthly Calls • 75 Agents
Big Tech Approach
10,000
Calls scored
~80%
Accuracy (cheap models)
~2,000
Potentially wrong scores
OttoQA Approach
4,000
Statistical sample (40%)
96%
Accuracy (premium LLMs)
±3%
Confidence interval

Higher statistical validity. 60% less cost. Higher accuracy on every call scored.

Premium AI.
Statistical Rigor.

We use the highest-quality, most expensive LLMs available. We can afford to because we're not trying to score 10,000 calls for pennies each. The result? 96% accuracy that Big Tech can't match.

Premium LLMs Only

We use the most advanced AI models available. No cutting corners to hit volume targets.

96% Accuracy (±3%)

Statistically validated scoring that you can actually trust for coaching decisions.

Statistical Sampling

Score the right percentage to achieve validity—not every call just because you can.

Pay for What Matters

Usage-based pricing means you're not funding wasted processing on calls that don't improve your metrics.

AI Model Quality Comparison

Accuracy vs. volume tradeoff

Big Tech
~80% Accuracy
Cheap models for volume
OttoQA
96% Accuracy (±3%)
Premium LLMs for quality

Better Data. Lower Cost.

Statistical sampling with premium AI delivers higher validity than brute-force scoring with cheap models.

96% Accuracy

Every call we score is scored right. No garbage-in-garbage-out from cheap models trying to hit volume.

70-80% Cost Savings

Pay for evaluations that matter, not volume for volume's sake. Usage-based means no waste.

Higher Statistical Validity

96% accuracy with ±3% confidence beats 80% accuracy at 100% coverage. Better data, not just more data.

Faster Insights

Premium models process faster and more accurately. No waiting for 10,000 calls to grind through cheap AI.

Built for Your Size

75 agents? 150? You don't need enterprise volume. You need accurate data at a price that makes sense.

Your QA Forms

Use your existing evaluation criteria. We adapt to your process, not the other way around.

"Enterprise vendors sell '100% call coverage' to 5,000-seat centers who can afford the waste. You're a 75-agent center. Why pay to score calls that don't move your QA metrics?"

See How the Math Works for You

Stop Paying for Waste

Get more accurate QA data at a fraction of the cost. Statistical sampling + premium AI beats brute-force volume every time.