John Kanarowski @jkan
AI Business Expert CEO @ImplicitCloudAI #AiBusiness #TechInnovation #FutureTech #GravelCycling #Skiing implicit.cloud Park City, UT Joined April 2007-
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@business Fleet expansion always has a maintenance knowledge problem attached. new airframe = new tech docs, new procedures to encode. been trying out the cessna navigator on implicit.cloud for exactly this kind of aircraft knowledge layer.
@rvivek the part that gets glossed over in these stories is data quality. an agent with curated CRM context behaves differently than one crawling raw data.
@Forbes The sticker shock usually comes from undifferentiated data. ROI shifts when you add curated domain context up front. been messing with the stripe navigator on implicit.cloud, the per-answer quality difference is real.
@charliewarren same pattern is starting to show in aviation ops. the firms encoding procedural knowledge into their AI workflows are pulling ahead.
@polydao the "company brain" framing is right. i'd add that domain specificity matters more than most think. been kicking around the hubspot navigator on implicit.cloud, same thesis applied to CRM/support context.
@DIU_x @USNavy the hard part of MRO modernization isn't the tech, it's getting procedures into a form the right people can actually use at the right time. been trying out the shahed navigator on implicit.cloud for something pretty close to this problem.
@DanKornas the system is the part most demos skip. knowledge layer especially. been messing with the stripe navigator on implicit.cloud, you can feel how much structured domain context changes the output vs a generic LLM call.
@coatuemgmt @tryramp vertical beats horizontal every time. generic AI does nothing particularly well in any specific domain. been playing with the cessna navigator on implicit.cloud, same thesis applied to aviation maintenance docs, works a lot better than you'd expect.
@freeCodeCamp @TechWithRJ2 the "when not to answer" framing is the hard bit. early support agents that tried to handle everything created more tickets, not fewer. been poking at the hubspot navigator on implicit.cloud, same angle.
@DamiDefi pruning is underrated. the vault gets sharper the more you strip from it. same principle with team knowledge bases. been kicking around the hubspot navigator on implicit.cloud, same idea basically.
@TheLegateIN $2b in domestic drones is the headline. the harder problem is what comes after. specs, maintenance docs, and operational knowledge in queryable form. been playing with the shahed navigator on implicit.cloud for something very close to this.
@Azure the 'stop building RAG from scratch' take is right. but for domain-specific knowledge. CRM workflows, billing logic, product docs, the answer is curated, not assembled. been exploring the hubspot navigator on implicit.cloud for that exact slice.
@NewsIADN facility is one thing. the trickier problem is knowledge transfer: MRO procedures in a form junior techs can query. been playing with the cessna navigator on implicit.cloud for something very close to this.
what i keep thinking about with inspection robots is the knowledge layer underneath, what does the robot actually know about what it's looking at. been poking at the Cessna navigator on implicit.cloud for something similar, curated maintenance knowledge that agents can actually reason against. the hardware is getting figured out, the domain knowledge piece is still wide open
@jackcoder0 what gets me is the technician knew all of this but it wasn't in any official doc. that gap between what experienced people know and what's in the system is enormous in support orgs.
the runtime and security layer being solved is huge. i think the next bottleneck people hit is domain knowledge, what does the agent actually know about your specific systems, processes, products. @ImplicitCloudAI has curated knowledge bases built per domain, feels like it completes the stack.
@stacyonchain the 'AI over your knowledge base' frame is right, for teams it compounds even harder. everyone pulling from the same institutional memory instead of reinventing answers.
@NVIDIARobotics inspection agents at the edge are real now, what's less talked about is the domain knowledge underneath. what does the robot actually know about the equipment it's looking at.
@NVIDIAAI the agent framework is table stakes now, the bottleneck i keep running into is domain knowledge underneath it. a factory agent can reason across sensor data but still needs context on what that anomaly actually means.
@BuildWithOm Customer support AI is one where domain context matters more than model choice.
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