Clarvia designs LLM applications, RAG systems, AI agents, and workflow automations with evaluation, monitoring, review gates, and handover built into the scope.
Bureau is the operating layer behind our work. Around it, we design, build, automate, and advise on production AI systems.
A structured methodology combining discovery, design, development, evaluation, deployment, and handover into a process with measurable release criteria.
Every engagement follows this framework, adapted to fit your unique requirements and timeline.
We start by understanding your business, users, and goals. Through research and analysis, we define a clear roadmap that aligns technology with outcomes.
System design, prototyping, and user flows that establish a strong foundation. Every decision is intentional, every interface considered.
AI-assisted implementation works against agreed acceptance criteria, with code review, tests, and explicit release decisions.
QA, security review, deployment, monitoring, and handover include reviewable evidence and documented limitations.
We embed with your organization to understand your challenges, build solutions tailored to your needs, and ensure successful deployment every time.
Every engagement is structured around a real workflow, explicit review points, and evidence your team can inspect before expanding the system.
Delivery principles
From discovery to handover, the scope connects implementation with evaluation, deployment, monitoring, and operational ownership.
Test coverage, security review, performance checks, and acceptance thresholds are selected for the system and recorded with the release.
We start with one bounded workflow, define acceptance criteria, and expand only after the first release is measured and stable.
Regular updates, clear communication, and full visibility into progress. You stay informed and in control at every stage.
We use practical standards to keep delivery grounded. The goal is simple: ship useful AI systems, show how they behave, and leave your team with controls they can run after we hand over.
Every engagement starts with a short risk map. We identify where AI can act alone, where a person must review, and which data should never enter the model path. This keeps the first release useful without giving the system more authority than the business can defend.
NIST AI Risk Management FrameworkPrompt injection, data leakage, weak tool boundaries, and unsafe fallbacks are treated as product risks, not late-stage polish. We test the paths that matter before launch and keep those checks in the runbook after handover.
OWASP Top 10 for LLM ApplicationsSearch engines and AI assistants need clear facts about who you are, what you offer, and which pages matter. We keep visible content, service pages, schemas, sitemap entries, and AI discovery files aligned so crawlers do not have to guess.
Schema.org structured data vocabularyGood AI work starts with a task that repeats. The input is clear. The right answer can be checked. The cost of delay is high. These are the places where a small system can pay back fast. We do not begin with a broad change plan. We begin with one narrow flow, prove it works, and then widen it.
We also look for work that drains team time but does not need fresh judgment every minute. Support triage, finance checks, report drafts, internal search, data clean-up, and admin handoffs are strong first targets. Each one has a clear owner, a clear test, and a clear path back to a person when the system is unsure.
Before we build, we write down what success looks like in plain terms. What should change for the team? Which steps should be faster? Which errors should drop? Which data can the system use? Which team owns the result? Clear answers make scope smaller. They also make launch easier. The first version can be simple, measured, and useful on day one. If it works, we add more steps. If it does not, the team has learned early and the cost stays low. That keeps the work plain for buyers and clear for the people who will run it.
Every engagement is structured around reviewable acceptance criteria, evaluation evidence, and an operational handover.
Before implementation starts, the workflow has a baseline, a measurable success condition, and explicit rules for exceptions and human review.
Each release records the scenarios tested, observed failures, reviewer decisions, and known limits so quality claims can be checked.
Architecture notes, monitoring, runbooks, access boundaries, and clear ownership help the client operate and improve the system after handover.
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