Everyone got LLMs.
Bills went up.
Productivity didn’t.
Companies rolled out AI to every team and got duplicated work, reinvented wheels, and no shared learning — rising spend with flat results.
We're building the intelligent layer in between: it routes, gates, and combines models — learns from your company's own usage — and flags the answers you shouldn't trust.
Cost & Scale
The Cost Problem
Two structural forces are compounding at once.
Neither one is fixed by more compute.
Everyone got LLMs.
Bigger models stopped
being the answer.
Orchestrating several models now beats the single frontier model.
The gains moved to the layer on top.
Building
More than a router.
The layer that sits between your users and every model you pay for.
Gate
Queries are filtered and reformulated before anything is sent — smaller, cheaper models can handle more than you think.
Route
We're building a router that sends each query to the model — or combination of models — most likely to answer correctly and cheaply.
Combine
When one model isn't enough, several are merged into one better answer.
Learn
The router trains on your company's own usage — automatically, from how your teams query and respond — so it gets better every week you use it.
Know when your model is wrong.
Turn it into training data.
The Problem
Reliability is themissing layer.
Today's language models can sound convincing even when they're wrong. Production AI needs more than good answers — it needs to know when to trust them.
High Risk
Fluent answers can still be confidently wrong.
Auto Re-question
Low-confidence answers are automatically challenged and re-asked for a better one.
Closed Loop
Every correction improves the next answer.
The Workflow
The Reliability Loop
Every interaction becomes a learning opportunity. Detect likely failures, verify difficult cases, and turn every correction into data that continuously improves future responses.
01
Gate & Route02
Answer Checked04
Learn03
Experts VerifyCost goes down at the gate.
Reliability goes up in the loop.
One Engine
One engine, two uses.
The same engine, pointed two ways.
Deploy it
Run it as your LLM layer: lower cost, higher reliability, personalized to how your teams actually work.
Point it at your model
The same engine predicts, per query, which models will struggle — before running them.
Aimed at your own model, that becomes benchmarking and evaluation targeted at exactly the weak spots, and training data curated to fix them.
For enterprises, a router. For model builders, an evaluation engine.
Reliability
Why Thoth is different
Not another AI gateway. A reliability engine for production AI.
Most platforms stop at routing or evaluation. Thoth connects detection, intelligent routing, expert verification, and continuous learning into one closed loop, so every correction makes your model more reliable over time.
- Failure Detection
- Intelligent Routing
- Expert Verification
Failure Detection
We're building detection that flags responses likely to be wrong — before they reach users.
Intelligent Routing
We're building routing that sends each query to the best-suited model and refines the prompt to raise answer quality.
Expert Verification
High-risk responses escalate to domain experts in real time — human expertise inside the reliability loop.
The End Goal
The End Goal
“Every correction strengthens the next response. We’re building a reliability layer where production failures become training data, enabling models that improve continuously with every interaction.”
Pedro Alves
(CTO, Thoth AI)
Get started
Tell us where your model breaks.
We'll build the loop that fixes it.
Talk to our team
A 30-minute call with the team who runs LLM reliability engineering. No sales deck.
Thoth AI — LLM Reliability
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