AI ships faster than it can be supervised
Assistants, agents and RAG pipelines reach production — followed by questions most organisations cannot answer today.
“What does AI cost us — and who pays?”
The model provider's invoice is a single number. Without a split by system, client, user and cost centre you can neither budget nor charge back.
“Why did the system answer this way?”
Prompts changed ad hoc, agent decisions without a trace, unknown sources behind answers. When an incident hits, all that is left is guessing.
“Will we pass the audit?”
Regulators and auditors want proof: who changed a prompt, what the model saw, whether personal data ended up in logs. A dashboard screenshot is not enough.
All AI traffic through one gateway. Above it — the tower.
Applications never talk to models directly. On the way through, every call gets an identity, passes policy checks and leaves a complete trail. Connecting an existing system means changing an address and a key — not rebuilding the app.
Identity
System, client, end user and cost centre attached to every call — automatically, at the gateway.
Policy
Prompt outside the registry? Audit mode files a compliance gap. Enforce mode hard-rejects the call with a clear message.
Data protection
National IDs, emails, names and IBANs are masked before anything reaches the logs. If masking fails, content is redacted — never leaked.
Evidence
Cost, tokens, latency, prompt version, agent decisions and data sources — one complete trace per call.
Six things you get as standard
Full observability
Every call with cost, tokens, latency and prompt version. Agent reasoning and model response on a single trace.
Costs & budgets (FinOps)
Cost per system, client, user and cost centre with hierarchy and allocation rules. Budgets with 80/95/100% thresholds, forecasting, controlling export.
Prompt governance
A central registry with versions and draft → staging → production promotion behind approvals. Enforce mode blocks unregistered prompts — visibly, in a report.
Data protection (PII)
Personal data — including national identifiers — masked before it reaches the logs. Strict mode: the model never sees sensitive data at all.
Quality measured continuously
Golden sets, an AI judge scoring production samples hourly, human review, A/B prompt tests with statistical significance and a regression gate in CI/CD.
Explainability & audit
“Why this answer, why not another”, data provenance down to source and version, incident replay, one-click audit artifact export.
Where Watch Tower makes the most sense
Banks & insurance
A regulated sector where “trust us” does not fly. Policy enforcement, data masking, audit evidence and EU-only data — the entry conditions for AI in production.
Enterprises with many AI systems
Several teams, a dozen use cases, one model invoice. Watch Tower splits costs across cost centres, guards budgets and shows what works versus what merely costs.
Companies building AI products
Assistants, agents and RAG for end customers: cost per client and user, quality measured on golden sets, safe prompt changes without a deploy — rollback in a minute.
Public sector & critical infrastructure
Full sovereignty: the platform runs entirely on your own infrastructure, built on proven open-source components, sending nothing to third-party analytics SaaS.
Numbers from a live deployment
gateway overhead (p50) with full tagging, governance and masking on the path
of calls fully traced: cost, prompt, decisions, data sources
from a prompt version switch to production effect — no deploy
cheaper model turned out best in our quality benchmark — because we measured instead of guessing
Case study: DigitalFleet
Our fleet management product runs behind Watch Tower in production: a multi-agent AI assistant and a RAG pipeline, costs attributed per client and per user, prompts of five agents in a central registry with hard enforce — and every answer, from agent decision to data sources, leaves a complete, auditable trail.
Case study: FleetFlow
FleetFlow — our agentic fleet-management platform — runs behind the same gateway: the orchestrator and AI sub-agents leave one complete, shared trace, and costs are tagged per system and cost centre. Connecting the whole platform meant issuing a single key — zero changes to the application’s orchestration.
Case study: LynxFlow
The LynxFlow LLM layer — data extraction from insurance RFQs — runs entirely through the gateway: budget and costs guarded per cost centre, PII masked in logs, prompts in the central versioned registry. The auditability the insurance sector requires — from the very first call.
Case study: PZU iFlota
iFlota — the platform Poland’s largest insurer offers its fleet clients — is built on our DigitalFleet stack, the same one that runs behind Watch Tower. A reference from a regulated market: an enterprise-grade deployment at PZU.