- -35% review time with red/green tests
- Bias and brand guardrails enforced pre-launch
- SME approval loop with audit logs
Applied AI Lab
From pilot to production.
Examples of POCs and MVPs that shipped with measurable signals: adoption, safety, and speed.
- +24% activation from telemetry-led paths
- Multi-language variants with consistency checks
- Feature-fit nudges based on real usage
- First drafts in hours, with human QA checkpoints
- Knowledge extraction into reusable playbooks
- Alt text and transcript automation baked in
- Pilot → MVP → production with safety gates
- Telemetry to catch drift and failures
- Training and enablement for teams
Signals that matter
We measure adoption, safety, and speed.
Every case study is pinned to the signals that prove value—no vanity metrics.
+24% activation
Telemetry-led personalization and adaptive walkthroughs.
Bias/brand checks
Red/green tests, audit logs, and SME approvals before release.
POC in 2–3 weeks
Eval’d RAG/agentic patterns with rollback paths and training.
Case spotlights
A few we can talk about.
Each spotlight covers the playbook, stack, and outcomes. Ask for a deeper walkthrough to see the full telemetry.
AI QA for localized content
Red/green tests for bias, brand, and compliance across 6 languages.
Adaptive walkthroughs for activation
Telemetry-led branches and nudges tied to product usage signals.
RAG for support playbooks
Source-citing answers with freshness checks and hallucination tests.
Tool-using agents for ops
Bounded tool use with approvals, audit logs, and rollback paths.
See the full playbook
Pick a case and we’ll show you the stack, evals, and outcomes.
QA, personalization, RAG, or agentic tooling—ask for a deep dive with telemetry and governance details.