Tell me what you are trying to build, fix, govern, prove, or launch, and I will point you to the public Folium page that fits. It uses public routes only, so do not send private data here.
Evaluation reviews
Measure AI before it becomes business-critical.
AI quality cannot be judged by a single impressive answer. Folium Systems builds evaluation reviews that check the process, model behavior, source grounding, safe-tool routing, browser paths, latency, staff usefulness, and escalation behavior before a system becomes operational.
Operating comparison
Compare the narrow tool path with the Folium operating path.
This route can include models, retrieval, automation, or software, but the buyer outcome is broader: a controlled operating capability with human review, records, launch gates, and ownership.
| Operating question | Narrow tool path | Folium Systems path |
|---|---|---|
| What is being built? | A standalone tool, prompt, chatbot, connector, or single AI feature. | Measure AI before it becomes business-critical. as one service lane connected to workflow software, trusted knowledge, agents, APIs, governance, proof, and operating handoff. |
| How is control preserved? | Control is often added later through settings, policy notes, or manual cleanup. | Control is designed into source registers, permission maps, human gates, logs, blocked actions, recovery paths, and launch rooms. |
| How does the business know it is ready? | Readiness may depend on a demo, vendor promise, or isolated answer-quality check. | Readiness is proven through reviewable surfaces, scorecards, browser checks, known limits, support ownership, rollback triggers, and evidence records. |
What Folium Builds
Clear systems, reviewable records, and a path your team can operate.
Score the process, not just the answer
We define test cases around the job the AI must perform, the sources it can use, the tools it may touch, and the failures it must avoid.
- Agent and process eval sets
- RAG faithfulness and citation checks
- Safe-tool routing and refusal tests
- Browser and user-journey regression records
- Reserved held-out test set
- Model owner grid and compatibility matrix
Promotion scorecards
The result is a plain-language scorecard that shows what passed, what failed, what changed, and whether the process is ready for demo, sandbox, pilot, or production review.
- Critical-failure checks
- Human anchor and reviewer rubrics
- Latency and reliability checks
- Model, prompt, and retrieval release notes
- Persisted promotion verdict and independent readback
- Promotion, rollback, and deactivation record
Quality review
A quality review scores the work the business actually depends on.
Folium checks model behavior, retrieval, agents, browser paths, staff usefulness, and failure handling before a process advances.
- 01 Define cases Write test prompts, user paths, source expectations, tool limits, and failure examples.
- 02 Run candidates Compare prompt, model, retrieval, agent, or process versions against the same business job.
- 03 Score records Measure grounding, correctness, safe refusals, routing, latency, accessibility, and usefulness.
- 04 Repair failures Fix critical misses, stale sources, weak prompts, broken routes, or unsafe tool behavior.
- 05 Promote or hold Approve, pause, rollback, sandbox, or rerun based on a plain-language scorecard.
Review Point
Model changes are compared with records.
Folium packages this as visible review material so owners, staff, and reviewers can decide whether to refine, launch, pause, or expand.
Review Point
RAG and agent failures become visible before launch.
Folium packages this as visible review material so owners, staff, and reviewers can decide whether to refine, launch, pause, or expand.
Review Point
Quality checks protect staff, customers, and owners.
Folium packages this as visible review material so owners, staff, and reviewers can decide whether to refine, launch, pause, or expand.
Start here
Bring the next AI step under control.
You do not need to know every model name, runtime option, or integration path. Tell us what is slow, risky, expensive, confusing, or disconnected. We will help translate it into a practical AI systems plan.
- 01 Scope
- 02 Build
- 03 Prove
- 04 Operate