Smarter AI just means it's better at lying to you.
A drop-in trust layer for any AI workload — a single model, a mix of models, or a swarm of agents.
Whether you're running one model, several different ones, or a swarm of agents — AI tends to sound confident even when it's just repeating itself or quietly lying an earlier answer. SACL catches that, gives you one clear answer you can defend in an audit, and cuts the AI bill by up to 40×.
One layer. Four wins.
Tells real consensus from fake
Counts only genuinely independent opinions. Copy-cats don't get a vote.
Same answer every time
When agents disagree, a simple rulebook — not another AI — picks the answer the same way every time, and shows its work.
Fully auditable
Every decision keeps a record of which sources actually contributed. Hand it to a regulator without flinching.
Up to 40× cheaper
Agents share a short shared notebook instead of re-reading each other's essays. That's where the savings come from.
Validated on public benchmarks, not ours.
Same model, official datasets and scoring, SACL-on vs SACL-off. Every number reproducible.
A 300-agent swarm naively read as high-confidence consensus — SACL graded it one real voice (independent_support = 1).
The short version: we tested SACL on three industry-standard AI accuracy tests. It got more answers right while using roughly 40–460× less compute. The full numbers are below for your engineers.
Same work, same accuracy — at roughly 1/40th the cost. A workload that would normally run hundreds of dollars finishes for a few.
SACL improved every model we tested.
HotpotQA · n=30
Exact-match accuracy (bars) plus F1 and token savings. SACL rows in amber.
HotpotQA, n=30, single run. The accuracy lift is consistent across model tiers. The cost savings are larger on the expensive model.
AI suggests. The rulebook decides.
Agents write into one shared notebook
Instead of sending each other long messages, every agent writes its findings into a single shared scratchpad.
A simple rulebook settles disagreements
When findings clash, fixed rules — not another AI — pick the answer. Every decision is logged with who said what.
The next agent reads a short summary
Not a wall of text. Same intelligence, far less to chew on — which is where the speed and cost savings come from.
It drops into a real agent runtime in small, reversible steps — flag off = byte-identical, with a built-in kill switch.
Built for teams whose AI has to be right.
If a wrong answer costs you money, customers, or a regulator's attention — this is for you.
Stop paying clean claims that aren't.
Dozens of agents read a claim and 'agree' it's fine. SACL flags when that agreement is really one agent's opinion echoed 50 times — before you pay out.
One independent answer, not twenty.
Stop paying for the same analysis run twenty different ways. Get one defensible answer with an audit trail of which sources contributed.
No more random escalations.
When your support agents disagree on what to tell a customer, SACL picks the answer the same way every time — so customers get consistent responses, not coin-flips.
A paper trail, not a black box.
Every decision comes with a record of which sources actually contributed. Regulators get something they can read — not a 'the AI said so'.
Doing something different? SACL works anywhere AI makes decisions you'd rather not have to second-guess — one model, many models, or hundreds of agents. Research, ops, underwriting, fraud review, content moderation, internal copilots, you name it.
Where it fits — and where it doesn't (yet).
- Long-running agents that accumulate state
- Many agents / high contention
- Auditability in regulated, high-stakes domains
- Cost at scale (hundreds → thousands of agents)
- Short, simple tasks
- Low-contention, single-shot work
- Free-form conversational memory — in progress
- Making a weak model smart
Accuracy is better on HotpotQA, tied on MuSiQue and RULER — we don't claim "more accurate everywhere."
All benchmarks are single-model, single-run at the stated n. Reproducibility is the credibility.
Run SACL on your workload.
Paid design-partner pilots — 4–8 weeks, $5k–$25k. We integrate SACL behind a flag into your agent stack and measure cost, reliability, and auditability on your own workload.