Use cases
Bigwave is a general-purpose feedback-loop platform; these are four high-leverage recipes — concrete walkthroughs for getting specific outcomes from the platform.
Compliance-bound fine-tuning →
For: regulated teams (healthcare, finance, defense, legal) who need to fine-tune on production data without it leaving the VPC.
You'll learn: how to deploy Bigwave behind a corporate firewall with proxy_headers SSO, how to land an audit-defensible fine-tuning run, and how to pull the artefacts a GRC team will sign off on.
Self-hosted coding-assistant backend →
For: enterprise platform teams whose engineers want Cursor / Continue / Aider but whose AppSec won't allow code-to-cloud LLMs.
You'll learn: how to wire Bigwave into Continue.dev, how to capture thumbs feedback on completions, and how to fine-tune a code model on the team's own corpus via DPO.
Agent eval harness →
For: ML teams shipping production agents who need a binding deployment gate that catches trajectory-level regressions.
You'll learn: how to build a golden-set of expected agent trajectories, how to wire trajectory-match + hallucination + jailbreak scorers, and how to make eval-as-a-gate refuse a regressed deploy.
Customer-support tone tuning →
For: mid-market support orgs whose AI replies sound like an industry-average bot, not their brand.
You'll learn: how to capture agent edits to LLM-drafted replies as preference signal, how to run a tone-match scorer, and how to fine-tune a model that stays on-brand under pressure.
Each use case includes:
- The specific problem and why it's hard with off-the-shelf tools
- The Bigwave components involved and how they wire up
- Step-by-step engineering walkthrough with copy-paste commands
- The artefacts you'll have at the end
- An honest list of what's not covered