2 weeks · fixed fee
AI Production Readiness Review
Know what to build before you fund it. An evaluation baseline on your data, a target architecture, a run-cost model and a go/no-go plan.
Test your AI the way your customers use it
If an AI system talks to your customers, it needs testing every time it changes. We build evaluation suites and simulated customers that check each release across chat and voice, and dashboards that show quality in production.
What is LLM evaluation?
Manual QA cannot keep up with AI releases
LLM evals
Simulated customers
Voice and chat
Quality monitoring
LLM evaluation measures an AI system’s answers against agreed criteria on a fixed set of real cases, so you can tell whether a change made it better or worse. Without it, every prompt or model update is a guess.
On one conversational AI platform, our continuous testing layer found defects 88% faster, cut mean time to resolution by 60% and lowered QA spend by 48%.
01
Evaluation suite
Test cases from real conversations and tasks, scored for correctness, faithfulness to the retrieved sources, tone and escalation.
02
Simulated customers
AI-driven users that rephrase, interrupt and change intent the way real callers and chat users do.
03
Regression testing in CI
Every prompt, model, knowledge or flow change runs against the suite before it ships.
04
Production quality dashboard
Resolution rate, failure clusters and hand-offs to people, tracked across channels.
Start with the AI Quality Baseline:
Chat assistants
Support, sales and internal assistants on web, app and messaging.
Voice agents and IVR
Speech flows, interruptions, accents and hand-offs to people.
RAG and knowledge answers
Retrieval recall, faithfulness to sources, citations and answers that go stale.
Agent actions
Whether agents took the right steps with the right tools.
Safety and policy
Prompt injection, data leakage and off-policy replies.
Cost and latency
Price per conversation and response time under load.
Want a scored report on your live assistant in two weeks?
Read how continuous AI testing found defects 88% faster and cut QA spend by 48%.
How we engage
Start small, with a fixed scope
6–9 weeks · team of 4
Pilot-to-Production Sprint
One workflow, live in production. Built or rebuilt, connected to your tools, with human approvals where the stakes are high and monitoring against the baseline.
Monthly · from 3 months
Forward-deployed AI engineers
Senior AI and data engineers who join your team, work in your tools and ship on your roadmap.
Part-time · monthly
Fractional AI CTO
Senior leadership for your AI roadmap: model and vendor choices, team design, architecture reviews and board reporting.
What clients say
Teams that kept working with us
From my experience, it is hard to find a team who have both excellent technical and commercial skills. Busy Rebel has never let us down, so we continue to work with them. The most important criteria we have as a team is creating & maintaining high standards; Busy Rebel has this in spades.
I've had the pleasure of working with a few software houses in the past, but none of them compares to the team at Busy Rebel. Their sense of mission and purpose is unparalleled, and their in-house skills are excellent. I always know that I can rely on them to get the job done, and done well, even if we are working remotely.
Thanks to the contributions of Busy Rebel, our organization as a massive worldwide distributor has passed all phases of digital transformation, gained a competitive advantage, and somewhere has revolutionized our industry. Busy Rebel is extremely productive digital partner, and internal stakeholders are particularly impressed with the vendor's dedication to success and attention to detail.
Tell us what you’re trying to ship
AI testing and evaluation: FAQ
What is LLM evaluation?
Measuring an AI system’s outputs against agreed criteria on a fixed set of real cases, so each change can be compared with the last. For RAG that means scoring retrieval and generation separately: did the system find the right context, and is the answer faithful to it (Es et al., 2023). It combines automated checks, model-graded scoring and human review. Model graders are calibrated against human labels, because LLM judges show documented position and verbosity biases (Zheng et al., 2023).
What is the AI Quality Baseline?
A two-week, fixed-scope engagement: we test your live chat or voice assistant with simulated customers and give you a scored report of where it fails, how often and what to fix first.
Do you need access to our production system?
We need a test environment or a safe way to call the live assistant, plus a sample of real interactions. We sign an NDA and can work with anonymised data.
Can you test an assistant another vendor built?
Yes. The suite tests behaviour from the outside, so it works whoever built the assistant and whichever model it uses.
