E1 | STT.ai

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S… Speaker 4 (E1)

All right, everyone,

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S… Speaker 4 (E1)

thanks for joining.

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S… Speaker 4 (E1)

I know we've been reviewing deployment readiness almost nonstop this week,

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S… Speaker 4 (E1)

but we still need to resolve a few concerns before the enterprise rollout window opens

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S… Speaker 4 (E1)

next Thursday.

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S… Speaker 1 (E1)

Yeah,

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S… Speaker 5 (E1)

and leadership keeps asking whether the internal knowledge assistant is actually stable

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S… Speaker 5 (E1)

enough for organization -wide deployment.

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S… Speaker 1 (E1)

Honestly,

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S… Speaker 6 (E1)

the infrastructure itself is mostly stable.

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The bigger problem is still the retrieval hallucination behavior

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S… Speaker 6 (E1)

we observed during long -context queries.

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especially when multiple indexed policy documents are retrieved simultaneously.

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S… Speaker 7 (E1)

The embedding alignment becomes inconsistent once the

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S… Speaker 7 (E1)

context ranking starts drifting.

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S… Speaker 8 (E1)

I noticed the same thing during the benchmark validation runs yesterday.

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S… Speaker 8 (E1)

The hallucination rate wasn't catastrophic,

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S… Speaker 8 (E1)

but some responses still generated unsupported references from

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S… Speaker 8 (E1)

unrelated departments.

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S… Speaker 4 (E1)

And that's exactly the kind of issue we can't afford during enterprise

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S… Speaker 4 (E1)

deployment.

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S… Speaker 5 (E1)

Right.

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S… Speaker 5 (E1)

If employees start receiving inaccurate compliance guidance from the

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S… Speaker 5 (E1)

system, rollout credibility drops immediately.

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S… Speaker 1 (E1)

Hmm.

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S… Speaker 6 (E1)

And the difficult part is that the hallucinations aren't

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S… Speaker 6 (E1)

happening consistently.

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S… Speaker 6 (E1)

Most retrieval sessions look fine until larger context chains

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S… Speaker 6 (E1)

get introduced.

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S… Speaker 7 (E1)

That's probably tied to the embedding similarity threshold again.

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S… Speaker 7 (E1)

Once retrieval confidence weakens slightly,

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S… Speaker 7 (E1)

the ranking layer starts introducing loosely related chunks into the context

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S… Speaker 1 (E1)

window.

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S… Speaker 8 (E1)

Which then increases the probability of contextual blending during generation.

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S… Speaker 1 (E1)

Okay,

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S… Speaker 4 (E1)

before we go deeper into the architecture issues,

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S… Speaker 4 (E1)

let's align on where deployment currently stands overall.

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S… Speaker 5 (E1)

From the product side,

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S… Speaker 5 (E1)

onboarding preparation is mostly complete.

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S… Speaker 5 (E1)

Documentation teams already finalized the employee rollout

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materials for the March 24th deployment phase.

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S… Speaker 6 (E1)

Infrastructure scaling is partially ready too,

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S… Speaker 6 (E1)

but GPU allocation still looks tight during concurrent retrieval

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S… Speaker 6 (E1)

heavy inference sessions.

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S… Speaker 7 (E1)

Especially once longer document chains enter the retrieval pipeline.

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S… Speaker 4 (E1)

So today we need to decide three things.

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S… Speaker 1 (E1)

First,

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S… Speaker 4 (E1)

whether hallucination risk is acceptable enough for rollout.

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S… Speaker 1 (E1)

Second,

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whether the retrieval pipeline requires recalibration before

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launch.

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S… Speaker 4 (E1)

And third,

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S… Speaker 4 (E1)

whether the current infrastructure allocation can realistically support

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enterprise traffic.

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S… Speaker 8 (E1)

Makes sense.

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S… Speaker 1 (E1)

Yeah,

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S… Speaker 5 (E1)

because leadership wants a final readiness update by Monday morning.

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S… Speaker 2 (E1)

All right.

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S… Speaker 6 (E1)

So looking at the latest benchmark results,

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inference latency itself actually improved after the retrieval

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S… Speaker 6 (E1)

cache optimization patch.

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S… Speaker 3 (E1)

Right.

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S… Speaker 7 (E1)

But retrieval grounding accuracy became less stable during longer

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S… Speaker 7 (E1)

policy document interactions.

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S… Speaker 8 (E1)

Some of the compliance response evaluations also showed

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S… Speaker 8 (E1)

inconsistent citation grounding during multi -document retrieval.

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S… Speaker 4 (E1)

How serious are we talking?

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S… Speaker 8 (E1)

Moderate concern,

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S… Speaker 8 (E1)

honestly.

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S… Speaker 8 (E1)

The system usually retrieves the correct source category,

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S… Speaker 8 (E1)

but occasionally references procedural details from

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S… Speaker 8 (E1)

adjacent departments that weren't actually part of the validated context.

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S… Speaker 5 (E1)

So, basically,

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S… Speaker 5 (E1)

the model sounds confident even when retrieval alignment weakens.

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S… Speaker 3 (E1)

Exactly.

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S… Speaker 7 (E1)

And that's what makes hallucination detection difficult,

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S… Speaker 7 (E1)

because the generated responses still appear semantically plausible.

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S… Speaker 1 (E1)

Hmm.

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S… Speaker 6 (E1)

I think context window truncation might also be contributing.

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S… Speaker 6 (E1)

Once retrieval volume exceeds the allocation threshold,

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S… Speaker 6 (E1)

lower -ranked chunks get removed inconsistently.

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S… Speaker 4 (E1)

Could that distort retrieval continuity enough to affect generation

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S… Speaker 4 (E1)

reliability?

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S… Speaker 3 (E1)

Potentially,

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S… Speaker 1 (E1)

yes.

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S… Speaker 7 (E1)

Especially during chained procedural queries,

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S… Speaker 7 (E1)

where multiple document dependencies exist across different knowledge sources.

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S… Speaker 8 (E1)

I noticed that during the finance policy validation cycle yesterday.

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S… Speaker 8 (E1)

Some generated summaries merged reimbursement rules with procurement approval workflows.

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S… Speaker 1 (E1)

Yeah,

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S… Speaker 5 (E1)

leadership definitely won't like that.

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S… Speaker 6 (E1)

An increasing context allocation further creates another problem,

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S… Speaker 6 (E1)

because GPU memory utilization is already approaching the concurrency threshold

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S… Speaker 6 (E1)

during heavier inference loads.

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S… Speaker 4 (E1)

So we're basically balancing retrieval accuracy against

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S… Speaker 4 (E1)

infrastructure scalability.

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S… Speaker 3 (E1)

Pretty much.

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S… Speaker 5 (E1)

Could embedding recalibration improve retrieval precision enough

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S… Speaker 5 (E1)

without expanding the context window?

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S… Speaker 3 (E1)

Possibly.

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S… Speaker 7 (E1)

I think the semantic similarity weighting still needs adjustment,

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S… Speaker 7 (E1)

especially for overlapping enterprise terminology across departments.

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S… Speaker 1 (E1)

Hmm.

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S… Speaker 8 (E1)

And some indexing inconsistencies probably

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S… Speaker 8 (E1)

make the retrieval drift worse too.

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S… Speaker 4 (E1)

What kind of indexing inconsistencies?

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S… Speaker 8 (E1)

A few archived policy documents were still linked to outdated metadata

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S… Speaker 8 (E1)

categories during the audit review.

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S… Speaker 1 (E1)

Wait,

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S… Speaker 6 (E1)

seriously?

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S… Speaker 1 (E1)

Yeah.

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S… Speaker 8 (E1)

Not a huge percentage,

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S… Speaker 8 (E1)

but enough to introduce retrieval confusion during broader semantic searches.

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S… Speaker 7 (E1)

That actually explains part of the hallucination pattern we saw in the HR

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S… Speaker 7 (E1)

compliance queries.

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S… Speaker 5 (E1)

So the retrieval problem might not be entirely generation

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S… Speaker 1 (E1)

-related?

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S… Speaker 3 (E1)

Exactly.

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S… Speaker 7 (E1)

Some of it could originate from indexing integrity issues before generation even

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starts.

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S… Speaker 2 (E1)

All right.

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S… Speaker 4 (E1)

Then the indexing audit becomes much more important before rollout

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S… Speaker 1 (E1)

approval.

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S… Speaker 6 (E1)

And honestly,

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S… Speaker 6 (E1)

GPU scaling still worries me too.

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S… Speaker 6 (E1)

If enterprise traffic spikes during onboarding week,

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S… Speaker 6 (E1)

retrieval latency could increase again under concurrent load.

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S… Speaker 5 (E1)

Would employees actually notice the delay?

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S… Speaker 6 (E1)

Probably during long procedural searches or multi -document compliance

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S… Speaker 6 (E1)

requests.

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S… Speaker 8 (E1)

Especially if retrieval retries trigger additional embedding comparisons

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S… Speaker 8 (E1)

internally.

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S… Speaker 7 (E1)

And those retries also increase GPU allocation

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S… Speaker 7 (E1)

pressure because the retrieval layer keeps expanding semantic search depth

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S… Speaker 7 (E1)

dynamically.

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S… Speaker 3 (E1)

Hmm.

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S… Speaker 1 (E1)

Okay.

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S… Speaker 4 (E1)

So right now,

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S… Speaker 4 (E1)

we still have unresolved risk from three directions simultaneously.

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S… Speaker 4 (E1)

hallucination behavior,

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S… Speaker 4 (E1)

indexing reliability,

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S… Speaker 4 (E1)

and infrastructure scaling.

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S… Speaker 5 (E1)

That's not exactly ideal a week before rollout.

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S… Speaker 1 (E1)

No,

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S… Speaker 6 (E1)

but I don't think the situation is catastrophic either.

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S… Speaker 6 (E1)

Most benchmark evaluations still passed within acceptable

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S… Speaker 6 (E1)

enterprise thresholds.

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S… Speaker 1 (E1)

Agreed.

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S… Speaker 8 (E1)

The system performs well overall.

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S… Speaker 8 (E1)

The concern is mainly edge case retrieval reliability during

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S… Speaker 8 (E1)

complex query chains.

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S… Speaker 7 (E1)

And those edge cases are exactly where employees are most likely to rely

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heavily on the assistant.

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S… Speaker 1 (E1)

Right.

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S… Speaker 4 (E1)

Especially during policy interpretation or compliance escalation

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S… Speaker 1 (E1)

workflows.

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S… Speaker 5 (E1)

Do we think delaying deployment entirely is necessary at this point?

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S… Speaker 1 (E1)

Personally,

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S… Speaker 6 (E1)

no. But I do think retrieval recalibration and indexing

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S… Speaker 6 (E1)

validation need to finish before final approval.

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S… Speaker 3 (E1)

Agreed.

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S… Speaker 7 (E1)

I want another embedding alignment validation cycle completed before

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S… Speaker 1 (E1)

Tuesday.

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S… Speaker 8 (E1)

and I'd prefer the indexing audit finalized before the leadership readiness review

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S… Speaker 8 (E1)

Monday morning.

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S… Speaker 4 (E1)

Can we realistically complete both in time?

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S… Speaker 7 (E1)

The recalibration work probably can,

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S… Speaker 7 (E1)

yes, assuming GPU allocation stays available tonight.

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S… Speaker 6 (E1)

I can reprioritize inference workloads temporarily to free additional GPU

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S… Speaker 6 (E1)

capacity for the retrieval validation cycle.

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S… Speaker 3 (E1)

Okay.

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S… Speaker 5 (E1)

And for leadership communication,

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S… Speaker 5 (E1)

are we still presenting rollout as on schedule?

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S… Speaker 4 (E1)

Conditionally on schedule.

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S… Speaker 1 (E1)

Meaning?

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S… Speaker 4 (E1)

Meaning deployment proceeds only if hallucination validation

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S… Speaker 4 (E1)

improves,

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S… Speaker 4 (E1)

the indexing audit passes,

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S… Speaker 4 (E1)

and infrastructure benchmarks remain stable through the final concurrency

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S… Speaker 1 (E1)

review.

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S… Speaker 8 (E1)

That sounds reasonable.

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S… Speaker 3 (E1)

Yeah,

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S… Speaker 7 (E1)

I think that's the safest position right now.

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S… Speaker 5 (E1)

Same here.

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S… Speaker 2 (E1)

All right,

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S… Speaker 4 (E1)

let's summarize responsibilities before we finish.

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S… Speaker 4 (E1)

Emma Handel's embedding alignment recalibration and retrieval validation need

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S… Speaker 4 (E1)

to be completed tonight.

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S… Speaker 4 (E1)

Jack finalizes.

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S… Speaker 4 (E1)

The document indexing audit and hallucination benchmark review also

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S… Speaker 4 (E1)

need final verification.

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S… Speaker 4 (E1)

Michael manages temporary GPU resource allocation has

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S… Speaker 4 (E1)

to be monitored during the overnight validation cycle.

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S… Speaker 4 (E1)

Sarah prepares the Enterprise Rollout Readiness Report for leadership,

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S… Speaker 4 (E1)

and we reconvene Monday afternoon before final deployment authorization.

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S… Speaker 5 (E1)

Got it.

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S… Speaker 8 (E1)

I'll send the updated audit findings once the metadata validation finishes.

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S… Speaker 7 (E1)

And I'll rerun the retrieval grounding evaluations tonight,

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S… Speaker 7 (E1)

after recalibration completes.

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S… Speaker 6 (E1)

I'll monitor GPU utilization during the overnight benchmark

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S… Speaker 6 (E1)

runs in case concurrency spikes again.

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S… Speaker 1 (E1)

Perfect.

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S… Speaker 1 (E1)

Hmm.

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S… Speaker 5 (E1)

Honestly,

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S… Speaker 5 (E1)

this conversation makes it pretty clear how difficult enterprise retrieval

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S… Speaker 5 (E1)

systems become once multiple departments and overlapping policies

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S… Speaker 5 (E1)

are involved.

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S… Speaker 1 (E1)

Exactly.

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S… Speaker 4 (E1)

Retrieval quality isn't just about model capability anymore.

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S… Speaker 4 (E1)

It's also about indexing integrity,

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S… Speaker 4 (E1)

contextual grounding,

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S… Speaker 4 (E1)

infrastructure scaling,

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S… Speaker 4 (E1)

and validation reliability all working together simultaneously.

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S… Speaker 8 (E1)

And even then,

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S… Speaker 8 (E1)

probabilistic retrieval behavior still introduces uncertainty under complex

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S… Speaker 8 (E1)

query conditions.

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S… Speaker 7 (E1)

Which is probably unavoidable to some extent in large -scale

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S… Speaker 7 (E1)

enterprise knowledge systems.

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S… Speaker 2 (E1)

Right.

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S… Speaker 4 (E1)

The goal isn't eliminating uncertainty completely.

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S… Speaker 4 (E1)

It's keeping retrieval behavior reliable enough that enterprise users

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S… Speaker 4 (E1)

can trust the system operationally.

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S… Speaker 2 (E1)

All right.

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S… Speaker 6 (E1)

Hopefully the recalibration cycle gives us cleaner validation results

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S… Speaker 1 (E1)

tomorrow.

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S… Speaker 5 (E1)

Let's hope so.

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S… Speaker 2 (E1)

Okay,

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S… Speaker 1 (E1)

everyone.

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S… Speaker 4 (E1)

Thanks for the update.

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S… Speaker 4 (E1)

We'll regroup Monday afternoon.

E1

Samandráttur

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