Each approach has its strengths—but also critical limitations that can derail your project.
Best for <10 items
High $/item, Low fixed cost
Best for fixed processes
High fixed cost, Medium $/item
Best for exploration
Low costs, Low trust
Best for developing AND scaling a trusted process
Low fixed cost, Low $/item, High trust
A detailed breakdown of what each approach can and can't do.
| Feature | Manual Review | BPO/Automation | Black Box LLMs | Helioscope |
|---|---|---|---|---|
| Time to first result | Hours | Weeks | Minutes | Minutes |
| Upfront cost / minimum contract | $0 | $10k-$40k | $0 | $0 |
| Cost per 1,000 items | $1,000+ | $100-$500 | $10-$50 | $10-$50 |
| Handles uncertainty / edge cases | Yes | No | No | Yes |
| Easy to iterate and refine | Yes | No | Yes | Yes |
| Scales to large datasets | No | Yes | Yes | Yes |
| Trust in results | High | High | Low | High |
| Requires technical expertise | No | Yes | No | No |
Every approach to data analysis at scale has a painful cost. Here's the reality:
Custom ML models and rules engines require $10k-$40k upfront—and you need to know exactly what you want before you start. No room for iteration or discovery.
Manual review costs $1+ per item. BPO is cheaper but still doesn't scale. At 10,000 rows, you've spent a small fortune—and you still can't iterate quickly.
Off-the-shelf AI is cheap but unreliable. How do you know what's accurate and what's hallucinated? Most tools are either overconfident (hiding mistakes) or report uncertainty on everything (making them useless).
Helioscope gives you the best of all worlds:
Low fixed costs, low marginal costs, and trusted results through rapid iteration.
Don't just take our word for it. See the cost and performance data.