We build the business-outcome evaluation layer that nobody else has. Custom rubric engines, simulation environments, KPI dashboards, and governance gates, so your AI systems are measured against YOUR definition of success, not generic benchmarks.
Most AI eval measures technical metrics, token count, latency, BLEU score. We measure what your business actually cares about: did the agent solve the customer's problem? Did it follow policy? Did it cost less than the human alternative?
Rubric-First Development
Before we build an agent, we build the rubric that defines success. Every feature is evaluated against the rubric before it ships. This means you never ship AI that 'works in the demo' but fails in production.
Governance Gates Built In
Quality, safety, and performance gates aren't an afterthought, they're baked into the pipeline. Every agent promotion passes all three. Failed gates mean automatic rollback. Compliance records are audit-ready.
Eval-Driven CI/CD
Agents are software. They need version control, quality gates, and deployment strategies. We build evaluation-driven promotion pipelines: blue/green deployments, instant rollback, and every version immutable.
Architecture
Build vs Buy
We build what's unique to your business. We use open source where the problem is already solved. This is how we deliver custom eval infrastructure at a fraction of the cost of building everything from scratch.
Layer
Approach
Why
Rubric Engine
Build
Your definition of success is unique. Generic eval tools can't capture business-specific quality criteria. We build a custom rubric engine that encodes YOUR standards.
Simulation Environment
Build
Your agents operate in your specific context. Off-the-shelf simulations don't reflect your real-world conditions. We build a simulation that mirrors your production environment.
KPI Dashboard
Build
Business-outcome KPIs are specific to your business. We build dashboards that surface the metrics your leadership actually cares about.
Governance Gates
Build
Your compliance requirements are unique. We build policy gates that enforce YOUR rules, not someone else's defaults.
RAG Evaluation
Use Open Source
RAG eval is well-standardized. RAGAS, DeepEval, and similar tools provide solid retrieval and generation metrics. No need to reinvent.
LLM-as-Judge Metrics
Use Open Source
Open-source judge frameworks exist and work well. We calibrate them to your domain but don't rebuild the infrastructure.
Regression Testing
Use Open Source
Standard regression frameworks handle test management, execution, and reporting. We plug them into our rubric engine.
Observability
Use Open Source
LangSmith, Langfuse, Phoenix, mature observability platforms exist. We integrate them, not rebuild them.
Pricing
Two ways to engage
Start with the infrastructure, upgrade to a full partnership when you're ready to build. Or start with the partnership, we'll set up the infrastructure as part of it.
Eval Infrastructure
Maintenance & Monitoring
$500/mo
Ongoing evaluation infrastructure: rubric engine hosting, simulation environments, KPI dashboards, regression test suites, and observability tooling. We maintain and operate the eval layer so your team builds on top of it.
Custom rubric engine, your definition of success, not generic benchmarks
Simulation environment for testing agent behavior under real-world conditions
KPI dashboard with business-outcome metrics (not just technical metrics)
Governance gates: quality, safety, performance checks before any agent ships
Regression testing suite, catch degrades before they reach production
Observability and monitoring, know what your AI is doing in real time
Monthly eval reports with trend analysis and improvement recommendations
Full development partnership where eval drives the build cycle. Every agent feature ships through the evaluation pipeline, quality gates, safety checks, performance benchmarks. We build the agents AND the rubric that proves they work.
Everything in Eval Infrastructure, plus:
Full-cycle AI agent development with eval-driven promotion pipelines
Custom rubric design, we interview your team and encode what "good" means for YOUR business
LLM-as-judge metrics calibrated to your domain and quality standards
A/B evaluation: compare agent versions against business outcomes, not just vibes
Adversarial testing built into CI, prompt injection, edge cases, failure modes
