Solution
~/solutions/forward-deployed-ai-engineers

Model bugs need more than a vague ticket.

> Model regressions, eval failures, and customer-specific AI issues usually start in messy threads. Strukt keeps prompts, logs, screenshots, and code work tied to the same issue.

Reality

What usually happens

•A customer says the model answer changed overnight.

•Someone drops a screenshot, prompt, and logs in Slack.

•The eval failure is discussed in another thread.

•By the time a ticket exists, half the debugging context is gone.

Fix

What Strukt does

•Creates or updates tickets from model regression threads.

•Attaches prompts, eval notes, logs, screenshots, and customer details.

•Links GitHub work, PRs, and commits.

•Keeps debug and deployment status visible in Slack.

Outcome

Why teams use it

•Fewer half-empty model bug tickets.

•Less time rebuilding the customer-specific setup.

•Prompt and eval context stays with the fix.

•AI rollout work is easier to hand off.

Flow

Example workflow

•Customer reports a bad model response

•Prompt, logs, and screenshot get attached

•Strukt creates or updates the issue

•Engineering links eval and GitHub work

•The customer-facing team sees the fix move

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