The batch record review for lot 22B completed. All 248 in-process checks pass against the batch record and spec v3. One fill-weight deviation at step 4 sits inside the validated range and is routed to QA. Recommend conditional release pending QA sign-off. Here’s what you should do next:
Next Steps
- 1. Confirm the CoA — review the generated certificate of analysis against spec v3 before it posts.
- 2. Attach the deviation record — link the fill-weight deviation and its validated-range justification for audit.
- 3. Route to QA sign-off — the conditional release waits on the QA Lead; nothing posts without it.
Step 4 fill-weight read 0.48% under target. The validated range is ±2%, so it is in-range. I have cited the reading to the batch record and the QC log.
Done. The deviation record, its validated-range justification, and the CoA are linked to lot 22B and routed to the QA Lead. Nothing posts until they sign off.
A. Chen. I have notified them and added the review to their queue; you will get a note the moment they approve.
What a generic tool can’t promise. One intelligence, custom-built on your lab’s own data: deterministic, cited to source, and running every surface you need
Custom-built, not configured.
Built from your protocols, your data, and your team’s judgment: how your QA team reviews a batch, how Ops reorders, how QC reconciles. Phyvant observes the real work and encodes it into procedures you can open, read, and version. Not a template you fill in; a model that only exists because of how your lab works, and no one else can have it.
It never guesses.
Ask about a lot and the answer arrives with its sources attached: the batch record, the QC log, the spec version. A figure it cannot cite is withheld and routed to a person, never invented. And because repeated work runs as a fixed procedure, the same question gets the same answer, run after run, every step logged.
The intelligence stays yours.
Everything it learns is captured as a versioned, cited asset: rules, precedents, mappings, the encoded procedures of your lab. It lives in your environment, never trains anyone else’s model, and exports anytime, even if you stop working with us. It is not a model on rent.
Bring your hardest workflow
Get to know Phyvant
Yes. Phyvant’s inventory management platform tracks every reagent, sample, cell line, and lot live across freezers and sites, with stock and expiry visibility, and drafts reorders before a run is blocked, so nothing runs out or expires unnoticed.
Phyvant does not improvise. Every answer is traced to a source record in your own data, and if the source is not there it says so instead of inventing a number, a lot, or a result. Because repeated work runs as a fixed, deterministic procedure, the same question returns the same answer every time.
Yes. Once you validate an analysis for one program, Phyvant’s data analyst platform clones it to the next and you adjust only what differs. A workflow proven in one indication becomes the starting point for the next instead of being rebuilt from scratch.
It is custom-built. Phyvant’s intelligence is built from your protocols, your data, and how your team works, not a one-size model with your logo on it. That is the core difference: a generic tool gives every lab the same answers, while yours only exists because of how your lab works.
You own the intelligence. The documented procedures and captured judgment, how your lab runs inventory, analysis, and operations encoded, are an asset you keep and reuse even if you stop working with Phyvant. It is not a rented model that leaves you with nothing.
Yes. Phyvant runs inside your own environment or on-premise, in your own VPC. Your sequences, assays, and IP never leave your perimeter and are never used to train anyone else’s model, and every answer is cited to its source.
Yes. Every procedure runs the same way every time and every step is logged, so a result is reproducible and defensible. When QA, a reviewer, or a regulator asks how a number was reached, the full cited trail is there.
No. Phyvant builds and maintains the platforms, and your scientists and operators keep working the way they already do. The intelligence gets sharper the more your team uses it.