ViaCatalyst
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AI Hardening & Observability

Enterprise AI Hardening & Observability

Ship AI systems with the trust layer executives expect: scoring rubrics, drift monitoring, prompt and retrieval traces, cost controls, and risk-aware guardrails.

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Evals Rubrics for grounding, policy, and task success
Tracing Prompt, retrieval, tool, latency, and cost telemetry
Drift Monitoring for behavior, data, and quality changes
Guardrails Least-privilege controls and human validation

AI risk does not disappear after a successful demo. Inputs drift, prompts regress, retrieval misses, model behavior changes, token costs spike, and business users lose confidence when failures are invisible.

ViaCatalyst installs the evaluation, observability, and governance baseline required to operate LLM products and agent workflows in production.

Outcomes

What this service is designed to improve.

Automated scoring rubrics for RAG quality, task completion, and policy alignment

LLM performance monitoring across latency, cost, hallucination risk, and drift

Production guardrails for access control, prompt injection, and high-risk actions

Core capabilities

Focused capabilities.

Automated Evaluation

We define measurable rubrics that test retrieval quality, answer grounding, policy alignment, and workflow completion.

  • Golden datasets for critical questions, edge cases, and negative tests
  • LLM-as-judge and deterministic checks with confidence thresholds
  • Release gates that block regressions before prompt, model, or retrieval changes ship

Runtime Observability

We instrument AI systems so teams can inspect traces, identify failure modes, and control spend.

  • Tracing across prompts, retrieval context, tool calls, model responses, and user actions
  • Latency, token, cache, and model routing metrics for AI cost control
  • Dashboards for drift, failure clusters, user corrections, and escalation rates

Security Guardrails

We reduce exposure from prompt injection, over-permissioned agents, data leakage, and unsafe execution.

  • Least-privilege runtime design across tools, APIs, data stores, and secrets
  • Input and output filters for sensitive data, policy violations, and unsafe commands
  • Human-in-the-loop validation for actions that affect money, data integrity, or customers

Process

How we deliver.

01

Define quality targets, failure modes, policies, and operational thresholds

02

Build eval datasets, scoring rubrics, tracing, and release gates

03

Connect runtime dashboards for cost, latency, drift, safety, and user feedback

04

Review incidents, tune guardrails, and improve model and retrieval performance

Technology

Evaluation and observability tooling.

OpenTelemetry

LangSmith

Helicone

Arize Phoenix

PostHog

Datadog

Prometheus

Grafana

pytest

Ragas

DeepEval

Great Expectations

Operational impact

Representative benchmark.

Helped convert an AI feature plan from a demo-only workflow into a production release plan with scorecards, traces, and safety gates.

Created executive visibility into answer quality, hallucination exposure, latency, and unit economics before expanding usage.

Related case patterns

Representative work.

Financial Balance Sheet Projection Pipelines

Automating projection workflows across ledgers, historical quarter data, assumptions, validation checks, and review queues.

Financial Services

B2B SaaS AI Feature Integration

Adding an AI capability layer to a live B2B SaaS product without creating a brittle prompt wrapper or exposing tenant data.

SaaS Platforms

Agentic Operations Readiness

Assessing whether a manual internal workflow is ready for autonomous agents, deterministic gates, and production observability.

AI Operations
Can you work inside our existing cloud and security model?

Yes. We design around your current identity, network, data, CI/CD, and approval boundaries, then recommend only the changes needed to make the AI system production-ready.

Do you use public models with proprietary data?

We can use commercial LLM APIs, private endpoints, or self-hosted models depending on risk profile. Proprietary data is isolated from public training paths and access is designed around least privilege.

Can we start with a short diagnostic before committing to build?

Yes. Most engagements begin with a paid architecture audit or two-week discovery sprint that produces a capability map, risk register, and implementation roadmap.

Architecture inquiry

Discuss AI Hardening

Bring us an existing AI workflow or planned release and we will identify the monitoring, eval, and guardrail gaps that matter most.

Next step

Add the trust layer before AI becomes business-critical.

Book a focused architecture audit and we will map the data, agent, evaluation, and security work required for a reliable first release.

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