Your PoC, RAG system, copilot or AI agent works in principle, but is not yet reliable enough for production.
I evaluate the existing AI system with realistic cases: RAG and retrieval quality, LLM and agent evaluation, tool calls, guardrails, permission boundaries, regressions, latency and cost. The outcome is a traceable failure analysis with prioritised fixes and clear go/no-go criteria.
Independent · stack-neutral · remote across DACH
Measurable quality and release criteria
Prioritised root causes instead of vague symptoms
Concrete fix, guardrail and go-live plan
Repeatable tests for future changes
A demo usually shows the happy path. In everyday use, retrieval failures, changing data, ambiguous questions, incorrect tool calls, weak permission boundaries and unnoticed regressions determine whether an AI system is actually dependable.
The exact scope depends on the existing system. The key is to make quality measurable along the real workflow and distinguish root causes from symptoms.
We measure whether relevant content is retrieved, whether answers are grounded in sources and whether the business task is solved correctly.
For agents, the final result is not enough. The steps, tools and parameters leading there matter as well.
We show whether the application remains stable, observable and economical under realistic conditions.
Controls are evaluated where data, tools or autonomous actions create actual risk.
We start with the existing system and its real business tasks. Every step produces a result your team can use immediately.
We isolate the critical workflow and translate business expectations into measurable quality, security and operational goals.
Representative real cases and golden questions become a repeatable test set. The current version establishes the baseline for quality, latency and cost.
We test retrieval, groundedness, edge cases, regressions, tool calls, permissions and relevant prompt-injection scenarios, tracing causes rather than symptoms.
You receive prioritised actions, guardrail and observability recommendations, residual risks and clear criteria for fixing, rolling out, launching or stopping.
The results are structured so your team or implementation partner can continue immediately – without creating a new dependency.
The sprint is not tied to a particular model cloud, vector database or observability platform. Existing open-source, EU-cloud, private-cloud and on-premise stacks are considered.
The existing experdoo reference documents a self-hosted AI architecture with RAG, multiple agents and clear tenant, data and permission separation in productive use. That experience matters for production readiness because evaluation has to cover the interaction between retrieval, permissions, tool use and operations.
The greatest leverage comes when a pilot, RAG system, copilot or agent already exists and a sound decision about fixes, rollout or go-live is due.
When evaluation identifies concrete changes to APIs, data flows, RAG, workflows or tool integrations.
When private cloud, on-premise, EU hosting or hybrid architecture is part of the reliability or security problem.
The sprint suits existing RAG and knowledge systems, internal copilots, document-based workflows and agents with tool or API access. Scope is limited to one commercially relevant workflow.
With a representative evaluation set and separate measurement of retrieval and answer quality. Depending on the use case we examine relevant hits, groundedness, correctness, citation accuracy and task success. There is no universal threshold; release criteria must fit the business process.
No. The evaluation is most useful before go-live, for a stalled pilot or ahead of a substantial model, prompt, provider, index or architecture change.
The sprint combines domain quality, agent behaviour, reliability and selected AI-specific security risks. It does not replace a comprehensive penetration test or legal and regulatory assessment.
Data access and the test environment are agreed in advance. If sensitive data should not be processed externally, the sprint can run in a suitable existing EU, private-cloud or on-premise environment.
Briefly describe the existing system, its current state and the largest uncertainty. I will assess whether a bounded production-readiness sprint is a sensible next step.