AI ROI and Governance Platform
Join every dollar of AI spend to what your teams actually shipped — then route it cheaper, screen every prompt, and keep every agent inside its limits.
Cost per merged PR. Not cost per token.
The impact join
A proxy sees tokens leaving. It cannot see whether the pull request merged, whether it was reverted, or how many review rounds it took. OptScale AI reads both sides and puts them on the same row.
Platform
Five pillars of AI governance, ordered deliberately: you cannot safely route to a cheaper model until you can measure whether the output still holds up. That is why ROI comes first.
Join per-actor cost to merged PRs, features and tickets from GitHub and Jira. Build any metric in the wizard in under ten minutes.
✓Cost per merged PR, per feature, per team
✓Engineering, Finance and Adoption metric packs
✓Persona dashboards and scheduled reports
✓Cohort-framed and confidence-labeled by defaultыull prompt visibility – no masking
Routes every query across local models, public LLMs and MCP servers at 2 ms routing overhead — then strips the tokens you never needed to send.
✓Token compression: ~25% avg on coding-agent traffic
✓Model output arbitrage and cache alignment
✓Role-based access & department billing
✓Local LLM training & data sovereignty
Enterprise security layer that filters content, detects PII and prevents data loss — enforced inline, in both directions.
✓Content filtering & PII detection
✓Policy enforcement at gateway level
✓Data loss prevention (DLP)
✓Compliance-ready audit trails
Register the agents you already built with LangChain, CrewAI or custom code. OptScale AI doesn't run your agents — it governs them.
✓ Cost, time & recursion limits per agent
✓ Anomaly detection (loops, drift, token bursts)
✓ Block unauthorized MCP & vector stores
✓ Flag insecure operations live
Per-team and per-agent usage, model benchmarking per task, and efficiency measured against delivered outcomes rather than raw token counts.
Individual views ship off by default. Org-gated, small-group suppressed (no cohort under five), audit-logged, with self-view transparency. Team and org analysis works fully without ever enabling them.
How It Works
A clear path to AI governance without disrupting your existing workflows
01
Point OptScale AI at your existing models, LLM providers, and corporate services. Our MCP servers auto-discover available tools and data sources.
02
Set guardrails, routing rules, and role-based permissions. Decide which teams access which models, and how data flows between them.
03
Register your existing agents – built with LangChain, CrewAI, or custom code. Set cost limits, time limits, recursion limits, and whitelist authorized MCP servers and vector stores per agent.
04
Track every interaction with full tracing. Continuously optimize costs, catch anomalies, and scale governance as your AI adoption grows.
Lossless AI cost optimization
Four levers stack on the gateway — compression, cache alignment, prompt trimming and model selection. Each is reported separately, because each behaves differently on your workload.
✓Token compression — shrinks retrieved context, tool output and history inline, reversibly.
✓Cache alignment — keeps provider KV caches hitting, so repeated context bills at the cached rate.
✓System-prompt trimming — strips fixed instruction and tool-schema overhead re-sent on every call.
✓Smart model selection — sends what remains to the best-value model that still meets measured quality.
~25%
average on coding-agent traffic, measured with provider prompt caching already enabled
up to 97%
on highly structured, repetitive data formats
Baseline is net tokens billed against a passthrough with native caching on — not an uncached baseline. Compression and prefix caching are antagonistic; we measure net, end to end.
The cost of ungoverned AI
Not hypotheticals. These are the four exposures every organization running AI without a control plane carries today.
Code, contracts and PII pasted into consumer LLM accounts, with no record it happened.
Teams on their own provider keys — invisible to both invoice review and DLP.
Recursion loops that surface on next month’s bill, after the budget is gone.
“Who sent what, to which model?” — a question you will eventually get in writing.
Governance is not overhead on top of the ROI story — it is what makes the savings safe to take.
AI governance & evidence
Routing traffic to a cheaper model is only safe when every prompt is screened, every agent is bounded, and every decision is on the record — as evidence the platform generates, not evidence you assemble by hand at audit time.
Content filtering, PII detection and redaction, and DLP applied on the request path in both directions — not in a report you read next week.
Cost, time and recursion limits per agent. Unauthorized MCP servers and vector stores blocked. Insecure operations flagged before they run.
Runaway loops, behavior drift and token bursts detected in real time — and reported as a separate waste line, never blended into feature cost.
✓Model & agent inventory — every model, agent and MCP server in use, with owner and scope.
✓Decision and redaction logs — what was blocked, masked or rerouted, and under which policy version.
✓Monitoring records — anomaly detections, threshold breaches and their resolution.
✓Evidence export — audit-ready extract for your own certification or a customer questionnaire.
GDPR Art. 27 representation and EU AI Act Art. 22 representation — held separately, as the two obligations require. On-premises and air-gapped deployment available, with content never leaving your environment.
Org-gated · small-group suppression under five · audit-logged · self-view transparency. Team and org analysis works fully without ever enabling them. Measurement, not surveillance.
Integrations
Pre-built MCP servers for the tools your teams already use, with new connectors shipping monthly
⚡ Salesforce
📑 Jira
📖 Confluence
💻 GitHub
📄 Google Docs
💰 QuickBooks
📧 Slack
📊 HubSpot
🔒 Okta
☁️ AWS
📉 Notion
Over 100 connectors
Pricing
Start your 14-day free trial
Cloud-hosted SaaS or on-premises — same platform, your terms.
Per seat per month — requests pool across your team. Start free, scale when ready.
Annual license based on cluster size. Unlimited users and requests within cluster capacity.
Per seat per year – save 20% vs. monthly. Requests pool across your team.
A seat is one human user or one agent type — all agents of the same type share a single seat. Model costs passed through at provider rates
Ready to Govern Your AI?
Join the enterprises building responsible, cost-effective AI operations with OptScale AI.
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1250 Borregas Avenue Sunnyvale, CA 94089, US
UK Office:
124 City Road, London, EC1V 2NX, United Kingdom
