AI · LLM · Cost Governance

Stop LLM spend
from leaking.

Goveris is AI spend governance and audit infrastructure. It treats every model call as an auditable economic decision — logging the requested model, selected route, estimated cost, risk, confidence, budget state, and decision reason.

How it works ↓ Talk to me

tokens budgets within cap

01 — THE PROBLEM

AI spend is invisible until the invoice arrives.

Teams wire LLM calls into every product, but no one can say which model, which team, or which feature is burning the budget — or whether an expensive model was even the right choice for a given request. When the bill triples, there's no audit trail to explain why, and no control layer to have prevented it. AI cost without governance is a leak you only notice after the money's gone.

02 — HOW IT WORKS

Every call, an economic decision.

01 · YAKALA

Capture the call

Every model request passes through Goveris with its model, cost estimate, risk, and budget context.

02 · YÖNET

Route by budget

Backed by Calybris Core, it allows, downgrades, retries, or blocks based on budget and expected value.

03 · DENETLE

Log the reason

Model, route, cost, and decision reason are written to an audit trail you can report on.

03 — WHAT YOU GET

Spend you can see, cap, and defend.

Full record

Every call logged with model, route, cost, risk, and decision reason.

Budget control

Per-tenant caps that downgrade or block before you overspend, not after.

Cost report

Who spent what, on which model — a report finance can actually use.

Losing track of your AI bill?

Let's put a governance and audit layer between your product and its model spend.

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04 / OPERATING DOSSIER GOVERIS · CALYBRIS CORE 0.5.7

Make the invoice explain itself.Fatura kendisini açıklasın.

Goveris records model choice as an economic decision. Request metadata, catalog price, tenant budget, risk, confidence, selected route and the reason are bound before the call becomes an untraceable line on an invoice.Goveris model seçimini ekonomik bir karar olarak kaydeder. İstek metadata'sı, katalog fiyatı, tenant bütçesi, risk, güven, seçilen rota ve gerekçe; çağrı faturada izlenemeyen bir satıra dönüşmeden önce birbirine bağlanır.

PUBLIC ARTIFACT · 3 PAGES · SYNTHETIC SAMPLE

The report is part of the product.Rapor ürünün bir parçası.

The published sample replays 500,000 metadata-only calls. It exposes attribution, policy decisions, what-if ranges, review queues, evidence hashes and the exact boundary between a planning estimate and a production savings claim.Yayınlanan örnek, yalnızca metadata içeren 500.000 çağrıyı replay eder. Attribution, politika kararları, what-if aralıkları, inceleme kuyrukları, kanıt hash'leri ve planlama tahminiyle üretim tasarrufu iddiası arasındaki kesin sınırı gösterir.

Open full PDFPDF'i aç ↗ REPORT METADATA ↗ SAMPLE WAL ↗

Goveris AI spend governance audit report, page one — Page 1 · executive snapshot and policy simulationSayfa 1 · yönetici özeti ve politika simülasyonu

Goveris audit report attribution page — 02 / ATTRIBUTION

Goveris shadow pilot protocol page — 03 / SHADOW PILOT

01 / OBSERVE

Mirror metadata.Metadata'yı aynala.

Model, tokens, tenant, workflow, latency, risk and confidence enter; prompts and responses stay out.Model, token, tenant, workflow, latency, risk ve güven girer; prompt ve yanıtlar dışarıda kalır.

02 / PRICE

Resolve the catalog.Kataloğu çöz.

Requested and eligible routes receive one comparable cost basis.İstenen ve uygun rotalar tek karşılaştırılabilir maliyet temeli alır.

03 / DECIDE

Run Core 0.5.7.Core 0.5.7'yi çalıştır.

Budget, risk, confidence and tenant constraints produce allow, downgrade or block.Bütçe, risk, güven ve tenant sınırları allow, downgrade veya block üretir.

04 / RECORD

Write the reason.Gerekçeyi yaz.

Input, policy, decision and report identities stay replayable in the evidence ledger.Girdi, politika, karar ve rapor kimlikleri evidence ledger içinde replay edilebilir kalır.

05 / PROMOTE

Enforce only after review.Yalnızca incelemeden sonra uygula.

Shadow findings become controls only with outcome checks and explicit change approval.Shadow bulguları yalnızca outcome kontrolleri ve açık değişiklik onayıyla kontrole dönüşür.

PUBLIC SAMPLE / EVIDENCE LOCK

500,000 decisions, one reproducible run identity.500.000 karar, tek yeniden üretilebilir koşu kimliği.

DECISION MIX: 380,241 allow · 116,290 downgrade · 3,469 block
EVIDENCE: input SHA-256 · summary SHA-256 · run ID · generation time
PRIVACY MODE: metadata only · prompts off · local evidence volume
ENGINE: Calybris Core 0.5.7 · deterministic policy decision

OBSERVED SAMPLE 33.36%

Catalog-estimated savings rate in the synthetic replay. It is a scenario output, not a realized customer claim.Sentetik replay içindeki katalog-tahminli tasarruf oranı. Gerçekleşmiş müşteri iddiası değil, senaryo çıktısıdır.

SHADOW PILOT

Observe before control.Kontrolden önce gözlemle.

The seven-day protocol keeps production routing unchanged, mirrors metadata asynchronously and leaves raw events inside the customer environment.Yedi günlük protokol üretim rotasını değiştirmez, metadata'yı asenkron aynalar ve ham olayları müşteri ortamında bırakır.

PRODUCTION GATES

Cost control is not production-ready without quality evidence.Kalite kanıtı olmadan maliyet kontrolü üretime hazır değildir.

COVERAGE: ≥99% mirror coverage reconciled with customer telemetry
DATA QUALITY: field allowlist verified · no prompt capture
OUTCOMES: downgrades reviewed against task result, CSAT or approved label
CONTROL: enforcement remains locked until explicit promotion approval

Built by Emir Hüseyin İnci Systems Engineer · Bursa, Türkiye Systems that decide. Proof that stays. Work with me →