Most AI features don't fail at launch. They drift after it — we build the layer that catches it.
Quality you can measure, regressions you can catch, costs you can see — built at the boundary, vendor-neutral.
Automated evaluation so you can change prompts and models without breaking quality. · Evaluation on representative, production-like inputs · Regression detection across prompt and model changes · Quality scoring tied to your definition of good · Promptfoo / Ragas-based, reproducible runs
See what the model or agent actually did, not what you hoped it did. · Full traces of prompts, responses and tool calls · Latency, token use and failure-path visibility · Per-feature and per-user breakdowns · Langfuse / Helicone / OpenTelemetry
Enforced at the boundary, not requested in a prompt. · Input and output validation · Grounding and citation validation where outputs must be traceable to source material · PII and policy filtering · Fallback paths when confidence is low
Provider bills that stop being a surprise. · Token spend visibility per feature and per workflow · Runaway-cost alerting and budgets · Cost attribution your finance team can read
Traceable AI-assisted actions, ready for review. · Logged inputs, outputs and decision points · Record of AI-assisted actions that affect users · Evidence that supports internal governance, auditability and parts of AI Act readiness
Quality gated before release, not discovered after. · Evals run in CI before a prompt or model change ships · Block-on-regression rules to reduce the risk of unnoticed quality drops · A safety net for model and provider migrations
We map your AI features, current failure modes, cost exposure and what "good output" means — and where you're currently flying blind.
We define the metrics, representative inputs and traces that matter, and the guardrails the system needs at its boundary.
We build the harness, dashboards, guardrails and cost alerts, wired into your product and deploy pipeline in controlled slices.
We hand over dashboards and an eval suite your team owns and can run. Ongoing watching — re-running evals, tuning guardrails, catching drift — fits inside a partnership where you want it.
AI reliability as numbers your team can act on — not a vibe nobody can defend.
Choose this when:
Most AI features don't fail at launch. They drift after it.
We're deliberate about this, because the market oversells it. We don't operate a 24/7 managed monitoring service — for a senior studio, that's a promise that erodes the moment it scales. What we do instead: build the eval and observability layer so your team can operate it, and offer ongoing upkeep — re-running evals, tuning guardrails, watching drift and cost — through an Engineering Partnership or Platform Support retainer.
Honest up front: nobody buys a managed-ops promise we don't make.
Vendor- and model-neutral. The harness and observability are the default; CI gating, custom scoring and review queues are added where the workflow needs them — never tied to a provider.
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Enterprise-Grade FoundationsVulken FMFacilities management platform for mobile inspections, asset records, compliance checks, and internal operational reporting — combining a field app with a web-based admin system.Read plateIt's the reliability layer for AI in production: measuring output quality (evals), seeing what the model actually did (observability), enforcing limits (guardrails) and watching cost — so you know your AI is still working, and can prove it.
No. AI automation builds the AI feature. This keeps it correct, safe and affordable once it's live. Different jobs, often run in sequence.
We don't run a managed ops desk. We build the eval and observability layer so your team can operate it, and offer ongoing upkeep through an Engineering Partnership or Platform Support retainer. We're upfront about that rather than overselling managed ops.
Evals on representative, production-like inputs, scored against your definition of good — with regression detection across prompt and model changes. Numbers, not vibes.
Yes. We make token spend visible per feature and add budgets and alerts, so cost is a notification you act on, not a surprise at month end.
That's a core use case. A regression eval suite acts as a safety net so you can switch models or providers and see immediately if quality moves.
The audit trails and evaluation evidence feed directly into AI Act readiness and security reviews. See EU AI Act Readiness for the compliance-specific work.
Everything — the eval suite, dashboards and guardrail config live in your repository and run under your accounts. No black-box dependency.
Runaway provider bill, hallucinations reaching users, a prompt change that broke output and no eval harness to catch it — that's a triage situation. See Software Rescue for a 48-hour triage flow.
Software Rescue & Take-overMore insights and best practices on this topic.
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Read→H-Studio builds the reliability layer for AI in production — eval harnesses, LLM observability and tracing, boundary guardrails, cost monitoring and audit trails for SaaS products, agents and internal tools. We make AI output quality measurable, catch regressions before users feel them, and keep provider costs visible — vendor-neutral, with ongoing upkeep through partnership rather than a managed-ops desk.
