Most LLM reviews don't need a human
Your team checks every output. Only a fraction truly need it. The rest create delays, costs, and fatigue.
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Your team checks every output. Only a fraction truly need it. The rest create delays, costs, and fatigue.
As usage grows, the pain compounds:
Review backlogs pile up
QA expenses grow with usage
Decisions take longer
Same checks, over and over
Feedback disappears into noise
Depends on who reviewed it
"We know most outputs are fine — we just can't risk skipping checks."
Sound familiar?
We help teams lighten review load without sacrificing accuracy.
Auto-validate routine outputs
Flag only ambiguous cases
Learn from your reviewers
Spot patterns early
Keep outputs trustworthy
Our goal: your reviewers spend time where it matters most.
You might be a fit if:
You check every LLM output before it reaches a customer
Human review is your primary safety layer
QA volume and cost are growing fast
You can't move faster because review is the bottleneck
Sound familiar? Let's talk.
We've spent years building and deploying LLM systems in environments where accuracy matters. Across industries - finance, healthcare, insurance, logistics - we kept seeing the same pattern: human review carries the load, and it quickly becomes the bottleneck.
Most teams don't want to remove humans. They just want them focused on the cases that actually need judgment. But today, everything gets checked anyway.
We started Hilig to help teams take the repetitive, low-risk review work off people's plates. And let software handle what software can. The goal isn't to replace reviewers, but to support them and lighten the load as LLM usage grows.
We're working closely with teams to understand their workflows and shape a solution that fits the real world.
Facing growing review load, rising costs, or slow turnaround? Share your setup.