Product Studio  ·  Enterprise AI Infrastructure

Where Raw Agent Data
Becomes Enterprise Trust

We build infrastructure products for enterprise agentic AI. Evaluation platforms, observability tooling, and compliance primitives — each designed for fleets, not demos.

CAS Framework — Product 01 → Request Early Access
Zero-Egress by Architecture
OTel Native — CNCF Standard
Kubernetes-First Deployment
DSPy Semantic Evaluation
ClickHouse Fleet Analytics
Why We Exist

Enterprise AI needs
infrastructure that
earns trust

Agentic AI is not a feature. It's infrastructure. And infrastructure that touches enterprise data, financial decisions, and customer relationships cannot be shipped without evaluation, observability, and compliance primitives that match the threat model.

Every product we build starts from the same constraint: no raw data leaves your VPC, ever. Trust is not a dashboard. It's a property of the architecture.

🔬
Semantic evaluation over rule engines
Regex rules break on prompt variation. We use DSPy structured signatures — reproducible, typed, auditable verdicts with explicit reasoning chains. Compliance requires auditability, not just scores.
🔒
Zero-Egress is an architecture property
Not a toggle, not a configuration flag. Every product is designed so that evaluation, redaction, and policy enforcement run inside your VPC. Only math crosses the boundary — scores, flags, and topology.
⚙️
Platform teams first, always
One Helm chart. One Kubernetes label. Zero SDK proliferation. We design for the platform team who needs to instrument a fleet of 200 agents without touching a single agent codebase.
Products

What We Build

Enterprise AI infrastructure that ships with the architecture properties your legal and compliance teams can actually sign off on.

Product 01  —  Now Available for Early Access
CAS Framework
Compliance  ·  Adherence  ·  Signals

Evaluation platform for enterprise agentic AI. Reconstructs your agent fleet's execution DAG from OTel traces and overlays per-node CAS scores across three evaluation dimensions — in real time, inside your VPC, with zero code changes to your agents.

Compliance Policy Adherence Agentic Patterns DAG Visualiser Zero-Egress
Explore CAS Framework → Architecture ↗
Live DAG — Agent Execution  ·  CAS Scores
SupervisorAgent 0.96
MCPTool.github 0.91
PII_Scanner 0.99
DataAnalysisAgent 0.74 ⚠
Slack_Notification 0.51 ✗ BLOCKED
3
Evaluation Dimensions
Compliance · Policy · Patterns
0
Bytes Egressed
Raw data stays in VPC
90%
Eval Cost Reduction
LLM Cascade routing
<5ms
Policy Propagation
Zero pod restarts
Coming 2026  ·  Q3
Agent Audit Vault
Immutable, tamper-evident audit log for every agent decision. WORM storage with cryptographic attestation — built for regulated industries where CAS scores alone aren't enough.
Research Phase
Policy Intelligence
Automated policy discovery from historical agent traces. Learns what your agents actually do, surfaces implicit patterns, and generates DSPy signature proposals for human review.
Research Phase
Fleet Cost Shield
Real-time token budget enforcement and cost projection for multi-agent systems. Prevents runaway spend before it hits the invoice — with per-agent, per-project, and per-workflow controls.
Engineering Principles

How We Build

The constraints that every product must satisfy before it ships. Non-negotiable across everything we make.

01
Zero raw data exits the VPC
Every product in our stack evaluates, redacts, and processes agent data entirely inside the customer's infrastructure. Only derived signals — scores, flags, topology — cross any network boundary. This is a hard architectural constraint, not a configuration option.
02
Platform teams deploy in minutes, not weeks
If onboarding requires touching agent codebases, pinning SDK versions, or coordinating with AI engineering teams, we've failed. Our deployment model is one Helm chart and one Kubernetes label. Everything else is automatic.
03
Evaluation must be auditable and reproducible
A CAS score that can't be explained in a compliance review is worse than no score. Every evaluation produces a structured verdict with an explicit reasoning chain. DSPy signatures are versioned, typed, and deterministic given the same inputs.
04
Non-blocking by default
Evaluation infrastructure should never be in the critical path of agent execution. If our sidecar fails, your agents continue running. If our eval engine is slow, your agents aren't slow. Observability cannot create fragility.
05
Open standards, zero lock-in
We build on OTel (CNCF), Kubernetes, and standard protocols. A customer who removes our product should be left with cleaner infrastructure, not a dependency tangle. Proprietary protocols are a trap we won't set for our customers.
06
Ship for regulated industries first
If it clears healthcare, finance, and government procurement, it will work everywhere. We design for HIPAA, SOC 2, and FedRAMP constraints from the start — not as an afterthought. The hardest customer is the right customer to design for.
Get In Touch

Building Enterprise
Agentic AI?

We work with a small number of enterprise teams who are operating agent fleets in production. If that's you, we'd like to talk.

Explore CAS Framework → contact@aidatarefinery.io