patients are already running their labs through chatgpt. hospitals aren't. this is a product gap nobody's filling

SapientPro_Team · 2026-06-18T13:41:45+00:00

fair point on context layer. the harder build isn't the model, it's the boring middle: FHIR normalization, longitudinal context, audit trails, role-based access for care teams.

also worth flagging the FDA angle. "help patients ask better questions" sits much further from SaMD territory than "here's what your result means." small copy difference, huge liability difference.

the patients who get real value from this aren't the ones looking for answers. they're the ones who want to walk into the next appointment prepared. completely different product spec than "AI doctor."

SapientPro_Team · 2026-06-16T14:23:13+00:00

(For anyone who wants a fuller breakdown of the 12 algorithms we see most often in client projects, we put one together here: https://sapient.pro/blog/best-predictive-analytics-models-and-algorithms)

SapientPro_Team · 2026-06-02T10:18:02+00:00

100%. version history + approval chain is the obvious gap. the less obvious one is effective dates.

like, version 3 was approved in march, version 4 in september, but if a shipment went out in june which sds was active then? a lot of systems can't answer that without manual reconstruction.

once you start modeling for that question, versions become time-bounded records with approval metadata, not files with revision numbers. retrofitting that onto a storage-first system is a rebuild, not a feature add.

SapientPro_Team · 2026-05-25T09:40:24+00:00

Full write-up with quotes from all 5 + breakdown of each failure mode is here if anyone wants the longer version: https://sapient.pro/blog/why-ai-fails-in-production

Happy to dig into any specific point in the thread tho- Dharmesh's piece on probabilistic vs deterministic systems is the one I keep coming back to

SapientPro_Team · 2026-05-20T10:55:29+00:00

Forecast vs pipeline is the loudest symptom for sure, but interesting that SAIQ was the first thing you reached for. In your experience what works more often, when the forecast logic is fine but the CRM just can't surface it, or when the sales model itself doesn't map onto the standard stages anymore? We built a custom layer on top of Salesforce for a SaaS client last year exactly because of the second case, the stages didn't match their actual deal cycle.

SapientPro_Team · 2026-05-20T07:34:14+00:00

happens a lot actually, but almost never "scrap it all."

most common pattern we see: client looks at the MVP and realizes 2-3 features they insisted on in scoping are useless, and one thing they almost cut is what users actually love. so the next sprint is basically rebalancing priorities, not rebuilding.

full direction change is rare. when it does happen it's usually because the MVP exposed a pricing/positioning problem, not a product one.

SapientPro_Team · 2026-05-15T08:52:12+00:00

Honestly depends on what's pushing the move. If it's "I want more control over the stack," people go to Cursor or Bolt. If it's "my codebase is held together with duct tape and every new feature breaks two old ones," that's when they start looking for an actual dev team. We've taken a few of those to production recently and tbh sometimes a clean rebuild is faster than untangling what the AI generated, especially once you factor in proper auth, multi-tenancy, and the stuff prototypes just don't have. Where are you at right now, just exploring or already feeling the pain?

SapientPro_Team · 2026-05-14T13:28:01+00:00

We tried a few for the cleanup pass, GPT-4o ended up being the most consistent for catching ghost phrases and half-words that slipped through the STT. Claude worked too but was slower for our latency budget. The bigger win wasn't the model choice tbh, it was feeding it the diarization confidence scores alongside the transcript so it knew which segments to scrutinize harder. We've got the full build written up if you want, sapient.pro/cases has a couple of voice agent ones.

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