Every AI call in your company,
in one place.
A single control plane for every LLM your teams use — Claude, GPT, Gemini, internal models, and Claude Code. See every request, enforce policy before it reaches a model, and keep an audit trail of everything your AI does.
Single sign-on- Role-based access
- Full audit trail
- Runs in your environment
The problem
Your engineers are already using five different AIs. You can see none of them.
Claude, ChatGPT, Gemini, an internal model, Claude Code in the IDE — your teams are using all of it, today, without a shared layer of visibility or control. That means:
No idea what's actually being sent to external model providers- No way to see who is pasting PII, secrets, or customer data into a prompt — or to stop it before it reaches a model
- No policy on which models can touch which data — every team decides for itself
- No cost attribution by team, project, or developer
- No audit trail when a regulator, auditor, or customer asks for one
- Every interaction vanishes the moment the response returns — nothing to trace, debug, or investigate later
Point tools patch pieces of this. None of them own the whole problem.
What we built
One gateway. Every model. Full control.
Gatelyr sits between your teams and every LLM they call — through the browser, the API, or the IDE. Nothing changes for the people using it. Everything changes for the people responsible for it.
Observability
Every request, every model, every team — captured, traced, and searchable. See exactly what was asked, what each model returned, and what it cost, across every provider from one screen.
Security
PII, secrets, and sensitive data detected on every request and blocked, redacted, or logged for audit before it ever reaches a model — with who sent it recorded either way.
Governance
Decide which models can be used, what data can leave, and when a human has to approve — enforced automatically on every call, with an audit-ready record of every decision and change.
Cost Intelligence
Spend tracked and attributed down to the team, the project, and the individual call. Set budgets, get alerts before the invoice lands, and answer "who's spending what" without assembling a single spreadsheet.
Who it's for
Built for the people responsible for AI
Your teams get one gateway that stays out of their way. The people accountable for security, spend, and compliance get the controls they've been asked to produce.
CISO / Security
Nothing leaves unseen
Every prompt is inspected before it reaches a model — PII and secrets blocked, redacted, or logged per your policy, with who sent it recorded and every event exportable to the SIEM you already run.
CTO / Platform
One gateway, not ten integrations
Every team, tool, and app connects through a single base URL. Keys are issued and revoked centrally, and providers can be added or swapped without touching application code.
CFO / Finance
An AI line item with an owner
Every token attributed to a user, team, and cost center — with budgets and alerts that warn before a limit is crossed, not after the invoice lands.
Compliance / Legal
Rules that are actually enforced
Which models can touch which data, and who must approve what — enforced on every call, with an audit-ready history of every interaction and policy change to hand your next compliance review.
Cost & attribution
Know exactly who is spending what, on which model, from where
Because every call flows through the gateway, every token is attributed — not just counted. Break usage down by user, department, and location however your org is structured, and see the dollar cost behind it before finance does.
By user
See exactly which employee sent which prompt, to which model, and how many tokens it used.
By department
Roll usage up by team or business unit to see which parts of the org are driving AI spend.
By location
Break down usage by office, region, or deployment to spot uneven adoption or shadow usage.
By model
Compare token consumption and cost across Claude, GPT, Gemini, and internal models side by side.
Token counts
Input and output tokens tracked per request, per user, per model — no estimation, no guesswork.
Approximate cost
Every token count converted to an estimated dollar cost, so engineering and finance leaders can see spend building up before the invoice arrives.
Under the hood
Three gates between every prompt and every model
Every request passes through a pipeline before it reaches an LLM — each gate is a checkpoint where you inspect, enforce, and record, so nothing leaves the building unseen and nothing happens without a trail.
Inspect
Incoming prompts are scanned for PII, secrets, and prompt-injection patterns. Anything that shouldn't leave your environment is blocked, redacted, or logged for audit before it goes anywhere — and who sent it is recorded either way.
Enforce
The request is checked against your policies — which model is allowed for this data, which budget it falls under, whether it needs human sign-off — enforced automatically on every call and recorded for audit.
Route & Observe
The cleared request goes to the right model for the job. The response is scanned on the way back, and the full interaction — prompt, policy decisions, tokens, and cost — is recorded as a permanent, searchable audit record.
Coverage
Wherever your teams already work
Your own apps and internal tools
Drop-in SDK or API base URL override, full visibility from day one.
Claude Code, Codex, etc.
Route through Gatelyr and see exactly what every developer is asking, and what it costs, per repo and per task.
Backend services
SDK wrapper with clean streaming support and every call logged, scanned, and attributed.
Other model providers
OpenAI, Gemini, and internal models through the same unified interface.
MCP tool calls
Visibility into every tool invocation your agents make, not just the model call around it.
A dedicated internal chat UI
So employees have a better reason to use your system than claude.ai, instead of a policy telling them not to.
Browser traffic to claude.ai or chatgpt.com directly cannot be intercepted — TLS makes that unsolvable. Your own chat UI is how you make that a non-issue rather than a policy you have to enforce.
Grounded in your own knowledge
Answers that actually know your company
A general model doesn't know your codebase, your policies, or last quarter's decisions. Gatelyr finds the internal documents relevant to a request and adds them to the prompt automatically — so answers are specific to your company, and every one shows exactly which sources it drew on.
Automatic grounding
Relevant internal documents are retrieved and injected into the prompt on their own — no plugin to invoke, no extra step for the user.
Simple ingestion
Upload text, Markdown, or PDF files; chunking and embedding happen automatically, with nothing to wire up.
Separate per use case
Knowledge for internal chat stays isolated from knowledge for coding agents, each tuned on its own.
Cited answers
Every grounded response points back to the internal documents behind it, so people can check the source instead of trusting a black box.
Strict data isolation
One organization's documents are never visible to another — grounding never crosses a tenant boundary.
No more "it doesn't know our stuff"
Turn scattered internal knowledge into something every AI answer can draw on, instead of asking employees to paste context in by hand.
Enterprise-ready
The controls your security review will ask about
Security reviews ask the same questions everywhere: who can sign in, who can see what, where does data live, and what record exists when something goes wrong. Gatelyr answers them in the product — every AI interaction logged with masked credentials, full policy-decision history, and PII-redaction events, tied to who sent it.
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Single sign-on
Sign in through the identity provider you already run — no new accounts to provision or passwords to manage.
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Roles & access control
Users, groups, and role-based permissions decide exactly who can see and change what, across every part of the platform.
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Policy per organization
Detection and enforcement configured independently per org or business unit — a regulated division doesn't share rules with an internal lab.
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Full audit trail
Every request, policy decision, and redaction event kept as a permanent, searchable record — detections logged without re-exposing the data itself.
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Runs in your environment
Deploy inside your own cloud, or keep data within a specific country or region where residency rules require it.
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SIEM & monitoring export
Usage, security, and audit events flow to the monitoring and SIEM tools your team already relies on.
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Alerts & notifications
Security detections and budget thresholds routed automatically to the people who need to know.
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Central key management
API keys for teams and providers issued, scoped, and revoked from one place — not spread across spreadsheets.
Stop flying blind on AI usage.
See every model call your company makes, in one dashboard, in weeks — not quarters.
Questions
Frequently asked questions
What can we see once traffic runs through Gatelyr?
Every request and response, who sent it, which model handled it, the tokens and cost it used, and every policy decision along the way — all captured, searchable, and exportable for audit. Nothing an AI does in your company is invisible anymore.
Do we have to replace our chat tools?
No. Backend services, Claude Code, and internal tools route through the gateway with a one-line config change. And for teams who want a company-run alternative to public consumer AI tools, the built-in internal chat UI is available now — with the same guardrails and audit trail as everything else.
What happens to data that passes through the gateway?
Sensitive data is detected before requests reach any model and — per the policies your team configures — blocked, redacted, or logged for audit, with who sent it recorded either way. Full request/response logs are stored in your own infrastructure, under your retention rules.
Which models does this support?
Anthropic and any OpenAI-compatible provider work today — just point the client at the gateway's base URL. Other providers and internal or self-hosted models run through the same unified interface, so you can add or switch providers without touching application code.
Can Gatelyr run in our own environment?
Yes. Gatelyr is built to fit how your organization is required to handle data — run it inside your own cloud environment so sensitive data never leaves your control, or keep data within a specific country or region where residency rules require it. You can start simply and move to a more restrictive setup later without starting over.
How do employees sign in?
Through single sign-on with your existing company identity provider — no new account to provision, no separate password to manage. Combined with users, groups, and role-based permissions, people get exactly the access their role allows and nothing more.
Does Gatelyr integrate with our SIEM and monitoring tools?
Yes. Usage, security, and audit data can be sent to the monitoring and SIEM tools you already rely on, so AI activity and alerts show up where your security team already looks instead of in one more dashboard.