GENERATIVE AI_
Ship a production AI request path — not another ChatGPT wrapper
Parallaxis builds generative AI on live workflows for founders and ops leaders: copilots, RAG, and guarded agents with evals, human-in-the-loop, and write-back. We start when the job and data are real — not when the demo looks clever.
Every production request runs this path: Intent → Retrieve → Generate → Guard → Act → Trace. Click the stages on the right.
Production request path_
One production request path. Click a stage — this is what a real production AI request does on every call.
Intent_
Capture the operator ask
Parse the in-workflow request — ticket, invoice, or form — not a blank chat box.
- Workflow-bound
- Operator context
Not a chat window — a workflow path with grounding, guards, and trace
- 01Define the request pathOne workflow path, not a chat utopia
- 02Kill the wrapperSame ask — demo path vs production path
- 03Eval before scalePass rate over vibes
- 04Harden & hand offGuardrails, tracing, ops runbook
02 · WRAPPER VS PRODUCTION PATH_
Same ask. Two very different systems.
Toggle Wrapper demo vs Production path — watch where the path dies or completes. This is the difference between theater and a shippable GenAI path.
Same user ask_
“Summarize this customer ticket and suggest the next action.”
Toggle mode — path morphs_
Request path_
Paste into chat UI
Active_
Model replies from memory
Active_
Ground on ticket + CRM
Never built_
Guardrails + HITL
Never built_
Write back to system
Stops here_
Outcome_
Stops at chat
Looks smart in a slide. No grounding, no write-back, no evals — it never reaches the system of record.
03 · EVAL HARNESS_
Quality is a suite — not “it feels smart”
Select cases. Pass rate is the scoreboard we ship against before production — grounded in real operator scenarios.
Eval suite_
3/4 passing · 75%
Assertion_
Passing_Must cite source docs
Response includes retrieval citations for factual claims
Grounded answers only — no silent hallucination on policy or account facts.
Select cases — quality is a suite, not a vibe
04 · GUARDRAIL CONSOLE_
Production controls that change request fate
Toggle guardrails — the request stream updates Allow / Escalate / Block live. This is hardening, not a slide about “responsible AI.”
Guardrails_· Toggle live
Request stream_
- Summarize ticket #4821allow_
- Draft reply with account numberallow_
- Issue refund $240escalate_
- Call unlisted admin toolblock_
- Escalate tone exceptionescalate_
05 · SHIP RUNBOOK_
What operators and engineers leave with
This is not a demo — it is a runnable production path with docs your team can own after handoff.
Ops runbook_
4 chaptersThe job, systems, success metrics, and non-goals — written for builders.
- Workflow owner and operators named
- Systems of record and write-back paths
- Success metrics beyond “feels smart”
You get_
- Scoped workflow brief and architecture notes
- Working PoC or production path (agreed in SOW)
- Eval harness and baseline quality metrics
- Integration to agreed systems of record
- Human-in-the-loop and escalation design
- Handoff docs / runbook for iteration
Not included_
- Open-ended model research with no workflow owner
- Unscoped “ChatGPT for everything” installs
- AI strategy roadmaps across the whole company (see AI Strategy)
FIT_
Is a production AI request path the right next step?
Self-qualify — Generative AI works when the job and data are real.
Good fit_
- You have a clear workflow and access to the systems of record
- You need copilots, RAG, or agents with production guardrails
- You can define success metrics beyond “it feels smart”
Not yet_
- You are still prioritizing which AI use cases matter (start with AI Strategy)
- No data access or operator time for evals and feedback
- You only want a one-off demo with no path to production
NEXT STEP_
Ready to scope a production AI request path?
Map your systems — or talk through the workflow you want assisted. We will confirm data access, eval approach, and whether AI Strategy should come first.
Common questions
A thin production path on one live workflow: intent → retrieve → generate → guard → act → trace — with evals and HITL where risk matters. Not a blank ChatGPT install.