Find and safely cut unnecessary LLM spend
A deterministic decision engine reads your AI telemetry, reconstructs your system, and produces evidence-backed, reproducible recommendations — every one answering the only question that matters: why should I trust this?
What the Evidence Scan reads, what it costs, and how long it takes →
A real customer cut cost per call 70% — the measured case study →
The Thornbury demo — a fictional fixed-income asset manager — is public: see the full product before creating an account or uploading anything.
From the public Thornbury demo — a fictional firm, real engine output: derived from 124,800 logged calls over 24 days.
What Bayeto screens for
Dashboards report spend; they do not reduce it. Bayeto reads your telemetry for five ways money leaks — and prices the fix.
Paying frontier prices for routine work
Expensive models doing work a cheaper sibling completes to the same standard — including the migration you finished everywhere except one app.
Paying for the same answer twice
Repeated prompts and prefixes billed at full price because nothing in front of the model remembers the last answer.
Paying rush rates for patient work
Latency-tolerant jobs billed at synchronous rates, and calls billed at context tiers their prompts never reach.
Paying above the market you already use
The same model, cheaper from a provider or region already in your stack.
Paying for nothing at all
Spend on failed calls, and output far longer than the task ever needed.
Behind them run 14 deterministic screens; 3 ship with steps reviewed to the standard an agent can execute — the rest are advisory, each saying why.
Measured, not projected
−70%
cost per call, at a real customer
Measured in their own telemetry after the change shipped — the figure a dashboard can only promise.
Built for
Platform, FinOps and AI teams with material multi-model inference spend — and a bill someone has started asking about.
What it takes
One usage export, parsed in your browser — or go live over OpenTelemetry or your existing proxy. No application rewrite, nothing in your inference path.
When it is not worth it
If the likely saving does not clear the effort of acting on it, Bayeto says so — held moves are that sentence in product form, shown with their reasons.
A decision engine, not AI advice
Same telemetry in, same recommendations out — reproducible, auditable, and defensible in front of a CFO.
Deterministic
The same inputs always produce the same recommendation. Run it twice, diff the output — byte-identical.
No LLM in the decision path
Rules traverse a discovered graph of your system. Nothing is generated, nothing hallucinated — language models only phrase what the engine already decided.
Evidence-first
Every recommendation carries its observations, evidence, confidence, payback period — and the alternatives it rejected.
How it works
Start with one export — or go live
One CSV or JSON export, parsed in your browser — only the documented schema columns are transmitted, never prompt content. Or go live: OpenTelemetry from your own code, a POST from your own middleware, or a proxy like LiteLLM.
Bayeto reconstructs your system
Applications, models, providers, regions — discovered from telemetry alone, no configuration.
An honest, ranked briefing
Recommendations under your declared constraints. Risky moves on critical surfaces are held, not sold.
The measured loop
Keep the data flowing — live from your stack or a monthly export — and Bayeto measures what your changes actually saved, then grades its own prediction.
timestamp,model,input_tokens,output_tokens,... 2026-06-01T09:14:02Z,gpt-4o,1832,412,... 2026-06-01T09:14:07Z,claude-sonnet-5,2210,388,...
Connect live
- OpenTelemetry (OTLP)from your own code — no gateway
- LiteLLM Proxyone callback on your existing proxy
- OpenRoutertraces straight from your account
- Telemetry APIa POST from your own middleware
Through OTel GenAI instrumentation the OTLP route reaches OpenAI, Azure OpenAI, Google GenAI, Anthropic — with no application changes.
Or upload an export
LiteLLM, Helicone, Langfuse, OpenRouter exports are auto-detected — and any CSV or JSON carrying the documented columns parses in your browser.
The 13-column schema →Quality is the constraint, not the trade-off
Undeclared surfaces are treated as critical. Any move with an unmeasured quality risk on a critical surface is held and shown with its reason — never offered as a win, at any price.
Explore Thornbury Fixed Income — a fictional asset manager — before analyzing your own AI systems
The full product on a realistic dataset: discovery, briefing, evidence, the measured loop, the executive summary. Then upload your own export.