AI outputs faster than teams can validate
Velocity is no longer the bottleneck. Reliability is.
AI Augmentation Lab
We build the systems that make AI safe to ship.
Zephyr Labs augments AI with guardrails, runtime controls, and verification across code and customer channels.
We do not train models. We make models usable inside high-stakes production systems.
Prompt-only controls fail under production complexity
Policy needs to be enforced in workflow, not left to model behavior.
Most teams lack durable runtime infrastructure
Channels need idempotency, replay safety, and operational observability.
The Mechanism
Understand. Constrain. Prove.
One mechanism powers how we improve AI-assisted engineering and customer-facing AI channel operations.
01
Understand
Extract standards and architectural patterns from the codebase that actually exists.
Applies to developer workflows and production runtime behavior.
02
Constrain
Enforce policy and workflow gates so AI actions follow required sequences.
Blocks unsafe mutations and routes the agent back to compliant steps.
03
Prove
Verify behavior with runtime assertions and telemetry-backed evidence.
Confirms systems behave correctly in dev, staging, and production.
Product House
Multiple products. One operating thesis.
Recon / Intel
Best for: Teams that need to map standards and architectural reality before scaling AI contributions.
First outcome: Machine-readable standards baseline for policy and verification.
Recon / Policy
Best for: Organizations that need enforceable workflow controls for AI-assisted engineering.
First outcome: Gated mutation flow with deterministic rule enforcement.
Recon / Observability
Best for: Teams that need runtime proof, not compile-time confidence.
First outcome: Assertion-driven telemetry loop tied to quality gates.
Durable Channel Runtime
Best for: Teams deploying SMS/chat/voice experiences that cannot lose context or delivery guarantees.
First outcome: Production-ready event-driven channel infrastructure.
Experience Layer
Best for: Operators launching domain copilots across multiple vertical workflows.
First outcome: New experience launched on shared orchestration primitives.
Choose Your Path
Start where your risk is highest.
Safer AI code delivery
Use Recon to convert undocumented standards into enforceable engineering policy.
Reliable AI messaging channels
Use Zephyr channel runtime for durable customer-facing SMS, chat, and voice workflows.
Launch domain copilots without rebuilding core infra
Use the experience layer to ship vertical copilots on a shared orchestration foundation.
Proof
Measured outcomes, not narrative claims.
847
patterns extracted per enterprise-scale repo
100%
AI mutations gated before merge path
<200ms
runtime assertion checks (typical path)
1
unified mechanism across code + channels
Engineering quality
Teams move from subjective prompt reviews to measurable policy and verification gates.
Channel reliability
Customer conversations gain durable handling, replay safety, and clear operational traceability.
Cross-product leverage
Shared primitives reduce reinvention and accelerate launches across new experiences.
Engagement Model
How we work with teams.
01
Architecture Sprint
Map your current system, risk boundaries, and highest-value augmentation opportunities.
02
Production Build
Implement the selected path in your repo with policy, runtime, and operational controls.
03
Ongoing Optimization
Measure outcomes, expand to adjacent products, and harden quality at scale.
Pricing
Modular products, clear starting point.
Codebase Intel
$16/mo
Policy
$16/mo
Observability
$16/mo
Recommended Starting Package
Recon Suite (Intel + Policy + Observability)
$37.50/mo
$450/year
Next Step
Start with the system that removes your highest-risk AI bottleneck.
We will map your current architecture, choose the right starting product path, and define an execution plan built for production.