Regression suites, red-team libraries, and policy-as-code—shipping models with evidence, not optimism.

Ship prompts and models with tests—safety metrics your risk committee can read

We centralize prompt versions, model endpoints, and evaluation datasets—CI runs faithfulness, toxicity, and jailbreak probes before anything reaches production. Guardrail layers combine regex, classifiers, and LLM judges with configurable escalation to human review. Dashboards track drift when upstream models change—alerts fire when win rates drop on golden sets, not when Twitter notices first.

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LLM Evaluation, Safety & Guardrails Platform Development

01 // THE MANDATE

Regression suites, red-team libraries, and policy-as-code—shipping models with evidence, not optimism.

We centralize prompt versions, model endpoints, and evaluation datasets—CI runs faithfulness, toxicity, and jailbreak probes before anything reaches production. Guardrail layers combine regex, classifiers, and LLM judges with configurable escalation to human review.

Dashboards track drift when upstream models change—alerts fire when win rates drop on golden sets, not when Twitter notices first.

02 // ENGINEERING

Development process

Structured phases—from discovery to launch—with clear ownership and handoff points.

Risk taxonomy (weeks 1–3)

Failure modes, acceptable thresholds, escalation paths.

MVP (weeks 3–10)

Eval runner, dashboards, prompt versioning, basic guardrails.

Pilot team (weeks 8–14)

One product surface; CI gate.

Expand (weeks 12–18)

Red-team library; executive reporting.

Operate (ongoing)

Model upgrades; new policies; incident retros.

03 // CAPABILITIES

Core Capability Matrix

The building blocks of your solution

Datasets

golden prompts; PII-safe fixtures.

Metrics

accuracy; refusal rates; latency cost.

Harness

batch eval; pairwise judging optional.

Guardrails

input filters; output filters; escalation.

Versioning

prompt git; model pins; rollback.

Red team

scenario libraries; scheduled runs.

Compliance

audit log; approval workflows optional.

Integrations

LangSmith-style hooks optional; CI plugins.

API

runtime policy fetch; shadow mode.

RBAC

who can promote to prod.

04 // DELIVERY LIFECYCLE

The strategic roadmap

Milestones and checkpoints—each phase has a clear outcome before the next begins.

Milestone 01Delivery

Weeks 1–3: Evaluation criteria aligned with legal.

Milestone 02Delivery

Weeks 4–8: First automated nightly evals.

Milestone 03Delivery

Weeks 9–14: CI blocking on regressions.

Milestone 04Delivery

Weeks 15–18: Organization-wide policy templates.

Milestone 05Delivery

Ongoing: Quarterly red-team exercises.

05 // PRODUCT SCOPING

Choosing your path

Two engagement models—start lean and iterate, or commit to a full platform build from day one.

MVP

Speed & essentialism

Phase 1
MVP: offline eval jobs, golden datasets, toxicity and jailbreak checks, prompt/model version store, Slack alerts, read-only stakeholder dashboard. Excludes full federated human labeling and on-device inference. Proves discipline before org-wide rollout.
Recommended

Full product

Enterprise maturity

All-in
AI governance suite: multi-team workspaces, regulatory report packs, human feedback loops, runtime guardrail mesh, integration with model marketplace approvals.

06 // PARTNERSHIP

Why work together

A single accountable partner across strategy, build, and go-live—not a revolving door of vendors.

John Hambardzumian
Direct collaboration

End-to-end ownership: discovery, architecture, implementation, and launch—with clear communication and production-grade engineering.

  • Discovery & alignment
  • Systems that scale
  • Implementation depth
  • Clear comms

07 // CLARITY

Frequently asked

Provider-agnostic harness—OpenAI, Anthropic, Azure, and open weights supported with parity tests.

Ready to start?

Tell me about your product goals and timeline—I'll respond with a clear path forward.