Devin Bhushan is most useful for concrete factory AI time studies and continuous improvement content aimed at manufacturing operations leaders.
Best for
Industrial technology brands selling plant-floor software, seeking co-marketing or pilot partnerships with continuous-improvement leaders.
Not for
General enterprise AI platforms or non-manufacturing brands wanting paid sponsored posts for broad business audiences.
Key takeaways
One post reports 126 time studies across 32 processes from 144 hours of video.
AI can analyze the wrong step if people do not identify the actual bottleneck.
Greg Brue joins Squint as advisor to build Lean Six Sigma into the agent.
Check first: Confirm whether they accept sponsorship and request audience demographics and comparable results.
Main ideas
Stop getting ready for AI and start using it · The tool is only as good as the bottleneck it is pointed at · Proof comes from the continuous-improvement work the agent replaces
Devin Bhushan is known in this material for building Squint’s AI tools for factory operations, particularly tools that study manufacturing work and help teams improve processes.
The posts cover a reported large-line study in Singapore, a changeover analysis and the case for using AI to address hidden waste in manufacturing.
They also show that experienced process judgment remains essential: choosing the right bottleneck and defining a correct result still require human expertise.
Bhushan makes AI’s role concrete with specific factory tasks, time studies and operational outputs.
One post reports 126 time studies across 32 processes, drawn from 144 hours of video and completed in one day, compared with 78 days for an earlier study of the same line.
Another explains a key limit: AI can analyze the wrong step if people don’t identify the actual bottleneck.
The combination of speed claims and an explicit need for Lean Six Sigma judgment makes the work useful to readers interested in applying AI on factory floors.
A brief webinar reaction shows enthusiasm, not measured results.
Devin Bhushan argues that AI continuous-improvement agents belong on factory floors now, not after a readiness or transformation program, and that results count only when the tool targets the process that actually constrains the plant.
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The five posts move between an economic case—AI infrastructure demand colliding with flat manufacturing productivity—a method—get the data, understand the actual work, then change the process—and a measurement claim: time-and-motion studies produced from raw video in a fraction of the manual effort. One theme runs through them: the software is ahead of the industry's ability to choose where to point it, so Lean Six Sigma judgment and floor experience are part of the product, not an add-on.
Stop getting ready for AI and start using it
Devin Bhushan casts readiness and transformation projects as delay.
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In their account, AI on the factory floor is manufacturing's low-hanging fruit, and it works with a plant's existing systems out of the box.
Integration efforts and multi-year rollout planning only push the gains further out.
The reasoning comes down to a mismatch between supply pressure and process capability: demand for what factories make is rising faster than the systems and processes built to serve it.
The constraint is how work gets done, not whether the tools are mature.
The post names the equipment suppliers build for the AI buildout—turbines, transformers, switchgear and chillers—and says some items carry backlogs running for years, while US manufacturing productivity has barely moved in over a decade.
The closing instruction is direct: "Stop getting ready for AI and start using it."
A later post repeats the warning: a facility not using agents for continuous improvement "is going to lose."
The urgency argument first appears as an industry case in the manufacturing-demand post, then returns as a blunt competitive warning after the Singapore line study, where Bhushan pairs it with a specific before-and-after result.
The tool is only as good as the bottleneck it is pointed at
Devin Bhushan separates automation speed from process choice.
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A time-and-motion study is worthless if the recorded step was never the constraint: the line gets optimized around a problem that was not costing the plant money.
Bhushan says the industry has never had to build bottleneck judgment into a product because no product needed it before.
That judgment means knowing which process actually costs money, a skill built over years on the floor rather than through model quality.
The hiring post describes an AI that watches 18 minutes of a changeover with no narration, then returns a full time-and-motion study, classifying each segment against the seven wastes and preparing it for line rebalancing.
Bhushan states the limit plainly: point the camera at the wrong line and you optimize a step that was never the bottleneck.
Squint is hiring a Head of Lean Six Sigma, reporting directly to Bhushan in San Francisco, to teach the sales team to find a bottleneck on the first call, work with engineers on what "correct" means for the model, stay inside customer pilots until the numbers move, and speak credibly about what the technology can and cannot do.
Proof comes from the continuous-improvement work the agent replaces
The posts measure value against the manual study the team no longer had to do: hours of footage in, classified time studies out, compared with the engineer-weeks the same work previously took.
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The claim also extends to outcomes on the line: findings on waste, safety and ergonomics, and a rebalance that changes how many operators the line needs.
At a facility in Singapore the team had never seen, Bhushan reports capturing 144 hours of video on a large discrete manufacturing line and producing 126 time studies across 32 processes, along with reports on waste, safety and ergonomics.
The team then rebalanced the line for fewer operators while holding cycle time.
The post says the same line was last studied and balanced in 78 days of manufacturing-engineer work; Squint completed the job end to end in one day.
Bhushan asks how to reach Guinness World Records to report a record.
A separate post describes the Lean Manufacturing agent automating time-and-motion studies, reducing waste and variance, and improving flow.
Get the data, understand the actual work, then improve the process
Bhushan presents this as the method behind the product and as common ground between statistics and AI models: both depend on ground truths.
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The sequence is observation first, judgment second, change third—not the reverse. Bhushan also describes what the agent removes from the job—the stopwatch and clipboard—and why it suits the task: it brings more patience, memory and context to repetitive studies than a person working by hand, freeing manufacturing engineers for higher-order work.
Announcing that Greg Brue joined Squint as an advisor, Bhushan describes Brue as a Six Sigma pioneer who has spent decades teaching companies to eliminate waste.
Brue helped build Lean and Six Sigma practices into the Lean Manufacturing agent.
In the post, Brue says that if he had the tool when he was younger, he could have helped hundreds more companies in his career.
Bhushan says the partnership aims to accelerate continuous-improvement experts' work so they can make a bigger impact.
Across the posts, the agent's role shifts from automating a study to running continuous improvement full time. The manufacturing-demand post describes it as taking the stopwatch and clipboard out of engineers' hands; an earlier post frames it as an advisor helping encode Lean and Six Sigma practice into the product.
Pair AI builders with people who have walked the factory floor
Bhushan treats floor experience as part of the team, not a customer-side precondition.
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The posts describe a group of AI builders learning manufacturing quickly and needing someone who knows where constraints live—a role the hiring post calls a sherpa. The stated commercial priority is to partner with continuous-improvement experts rather than displace them, positioning the tool as an accelerant for their work.
The advisor announcement describes build work with Greg Brue directly inside the Lean Manufacturing agent.
The hiring post describes the team as world-class AI builders obsessed with "The Goal" and learning manufacturing at a pace Bhushan says he has never seen.
A later post reports that the Lean Manufacturing Agent was discussed at a community webinar, where a participant named Zakary is quoted saying that using the tool "has been mind-blowing"; that is a guest reaction reported by Bhushan, not a measured result.
Shows what the AI does in a real plant, with a concrete before-and-after claim.
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It makes a strong starting point for newcomers seeking evidence of speed and scope.
Devin Bhushan describes walking into an unseen facility in Singapore, capturing 144 hours of video on a large discrete manufacturing line, and creating 126 time studies across 32 processes.
The AI generated reports on waste, safety, and ergonomics, then rebalanced the line for fewer operators while maintaining cycle time.
They say a manufacturing engineer last studied and balanced the same line in 78 days, while Squint did it end-to-end in one day.
The post asks how to contact Guinness World Records to report a new record and warns that facilities not using agents for continuous improvement will lose.
For brands planning a campaign
For a manufacturing technology partner, this is usable proof of speed and plant-floor output.
It supports a pilot focused on one line and a measured reduction in study time.
Independently validate the record claim and 78-day comparison before repeating them as established facts.
Shows who shapes the domain expertise behind Squint's work, beyond the software engineers, and how the company is building credibility in continuous improvement.
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Devin Bhushan announces that Greg Brue has joined Squint as an advisor.
They describe Brue as a Six Sigma pioneer who has spent decades teaching companies to eliminate waste and improve operations, and say they learned from watching Brue walk the floor, evaluate work content, ask questions, and lead teams to better processes.
Brue helped build Lean and Six Sigma practices into the Lean Manufacturing agent so customers can automate time and motion studies, reduce waste and variance, and improve flow.
The post links statistics and AI models as both relying on ground truths, then lays out the approach: get the data, understand the actual work, and improve the process.
For brands planning a campaign
For a brand evaluating credibility, a named advisory relationship with a Six Sigma figure signals domain grounding.
A partnership could involve continuous-improvement experts in validation or training.
Ask what Brue's role covers; the post does not specify its scope or establish payment or endorsement.
Shows what problems remain after automation and what operator judgment Squint still needs, offering a clear view of the human gap in the product.
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Devin Bhushan describes an AI that watches 18 minutes of a changeover with no narration and returns a full time-and-motion study, with each segment classified against the seven wastes and ready to rebalance the line.
They say the work used to take a clipboard, a stopwatch, and weeks with a consultant.
But the tool can optimize the wrong line if the camera is pointed at a step that was never the bottleneck, so someone must know which process actually costs money.
Squint is hiring a Head of Lean Six Sigma to report directly to Bhushan in San Francisco, teach the sales team to find a bottleneck on the first call, work with engineers on what "correct" means for the model, run customer pilots until the numbers move, and speak credibly about what the technology can and cannot do.
For brands planning a campaign
For a manufacturing technology company, this identifies a specific integration gap: bottleneck selection and process judgment.
A partnership could supply Black Belt expertise, co-develop pilot criteria, or train sales and engineering teams.
It also shows Squint is seeking experienced operators, not only AI builders.
Explains why this matters now and the economic case Bhushan makes, including the supply-and-demand argument behind the product.
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Devin Bhushan connects the AI buildout to industries supplying data centers and power and cooling equipment, naming turbines, transformers, switchgear, chillers, and the electrical gear between the grid and the rack.
Backlogs on some items run for years, yet US manufacturing productivity has barely moved in over a decade.
They argue the systems and processes were not built for this kind of supply-side change.
In their account, the biggest lever is AI on factory floors, where manual work between an order and a finished good is full of hidden waste.
Squint built an AI agent that runs full time-and-motion studies automatically, taking the stopwatch and clipboard out of manufacturing engineers' hands.
The post urges readers to stop readiness projects, integration efforts, and multi-year rollout planning: "Stop getting ready for AI and start using it."
For brands planning a campaign
For a manufacturing technology partner, this supports positioning around immediate deployment rather than transformation programs. Test the claim with plant-level time studies, and address the risk of overpromising readiness, integration, and multi-year planning.
Offers an early glimpse of market reaction after launch and social proof, best read alongside the more detailed examples.
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Devin Bhushan says the Lean Manufacturing Agent launched a few weeks earlier, became a hot topic in the industry, and was discussed at a community webinar.
The post quotes Zakary saying that using the lean manufacturing tool "has been mind-blowing."
Bhushan calls it an exciting time in US manufacturing.
For brands planning a campaign
For a brand, this is useful as social proof or an event and webinar angle, but weak proof on its own.
Pair it with the Singapore line study and the changeover example to support a credible ROI case.
Ask for measured performance or independent validation before using the webinar reaction as evidence.
Community, advisory, and hiring signals around the product3
Human Lean Six Sigma judgment as the bridge to AI adoption2
Manufacturing capacity, AI infrastructure demand, and flat productivity1
AI agents that automate time-and-motion studies
The clearest recurring subject is using AI to turn factory video into continuous-improvement analysis.
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Devin Bhushan describes an AI agent that watches changeover video with no narration and returns a full time-and-motion study, classifying every segment against the seven wastes and preparing the line to be rebalanced.
In one Singapore example, the work covered 144 hours of video from a previously unseen large discrete manufacturing line, produced 126 time studies across 32 processes, and generated reports on waste, safety, and ergonomics.
The post says the same line had last been studied and balanced in 78 days of manufacturing-engineer work, while Squint completed it end to end in one day.
Another post says the Lean Manufacturing agent automates time-and-motion studies, reduces waste and variance, and improves flow.
Video-first analysis, segment classification against the seven wastes, and line rebalancing.
For brands planning a campaign
A manufacturing technology partner can build the campaign around measurable replacement of manual stopwatch-and-clipboard studies. It should supply video or direct access to work content, agree on process scope, and involve a continuous-improvement expert who can identify the real bottleneck.
Human Lean Six Sigma judgment as the bridge to AI adoption
Devin Bhushan argues the tool is ahead of the industry’s ability to use it.
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Pointing the camera at the wrong line can optimize a step that was never the bottleneck, so even the best tool needs someone who knows which process is actually costing money.
The posts describe hiring a Head of Lean Six Sigma to teach sales teams to find a bottleneck on the first call, work with engineers on what “correct” means for the model, stay inside customer pilots until the numbers move, and speak credibly about what the technology can and cannot do.
A related post announces Greg Brue, described as a Six Sigma pioneer, joining Squint as an advisor and helping build Lean and Six Sigma practices into the Lean Manufacturing agent.
The stated approach is to get the data, understand the actual work, then improve the process.
Get the data, observe the actual work, then improve the process; pair AI builders with people who have walked the floor.
For brands planning a campaign
A partnership should include a named continuous-improvement expert and a pilot design that starts with bottleneck selection, not camera placement. Campaign content can use the expert’s credibility to explain why AI needs factory judgment and why analysis of the wrong line fails.
Manufacturing capacity, AI infrastructure demand, and flat productivity
One post frames the commercial problem on the supply side.
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Whole industries are expanding to feed the AI buildout: data centers and the turbines, transformers, switchgear, chillers, and electrical equipment needed to power and cool them.
Backlogs on some items run for years, and demand is growing faster than manufacturing has been asked to absorb in a long time.
At the same time, the post says US manufacturing productivity has barely moved in over a decade.
Devin Bhushan argues those facts do not fit together because systems and processes were not built for this kind of supply-side change, and calls AI the single biggest lever for moving supply.
The opportunity, they argue, is hidden waste in manual work between an order coming in and a finished good going out, especially continuous-improvement work that AI can run continuously.
For brands planning a campaign
A brand can position AI continuous improvement as a supply-response story for manufacturers facing long backlogs and capacity pressure. Lead with throughput, cycle time, and labor redeployment evidence rather than generic AI readiness: the posts reject multi-year readiness and integration projects in favor of using available tools now.
The posts repeatedly frame adoption as an immediate competitive requirement.
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After the Singapore example, Devin Bhushan writes that a facility not using agents to AI its continuous improvement is going to lose, calling it the next Industrial Revolution.
The manufacturing-demand post says low-hanging fruit is AI, that it works with existing systems out of the box, and that factories should stop worrying about readiness, integration, transformation, and multi-year rollout planning.
It closes by telling readers to stop getting ready for AI and start using it.
Another post says the Lean Manufacturing Agent has been a hot topic since launch and quotes a community webinar participant calling the tool “mind-blowing.”
For brands planning a campaign
The campaign should pair urgency with a concrete first-step offer, such as a pilot on one line or a webinar where a manufacturer sees a time study produced from its own footage.
Community, advisory, and hiring signals around the product
Across the five posts, the product message appears alongside ecosystem signals: an advisor announcement for Greg Brue, a hiring call for a Head of Lean Six Sigma reporting directly to Devin Bhushan in San Francisco, and a community webinar where a participant describes using the lean manufacturing tool as mind-blowing.
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The posts say the team is made of AI builders learning manufacturing quickly and that Squint wants to partner with continuous-improvement experts so they can accelerate their work and make a bigger impact. The hiring post also says the role would sit between the people who fix factories and the people who build AI.
For brands planning a campaign
A brand partnership can use advisory validation, expert co-marketing, and community webinars rather than a pure product launch. The right partner profile includes continuous-improvement practitioners or manufacturers willing to share a line and appear in a joint case story.
The posts show one commercial relationship: Devin Bhushan works at Squint, the AI company whose manufacturing continuous-improvement agents the posts describe.
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They write as an insider, describing time-and-motion studies Squint ran in real plants, a Lean Manufacturing Agent the company launched, and a Head of Lean Six Sigma role reporting directly to them in San Francisco.
Squint
Employment
Bhushan posts as part of Squint, an AI company building continuous-improvement and lean manufacturing agents.
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They describe work Squint performed on a customer line in Singapore (144 hours of video turned into 126 time studies), announce that a Six Sigma pioneer joined Squint as an advisor, and post an open Head of Lean Six Sigma role reporting directly to them in San Francisco.
Devin Bhushan's posts make a running argument for AI-driven continuous improvement in manufacturing.
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Start with the Singapore line study for the concrete before-and-after, then read the hiring post for the bottleneck-judgment caveat and the advisor announcement and industry-demand post for the method and urgency.
The community webinar reaction offers a brief signal of early practitioner response.
The through-line: point AI at the process that actually constrains the plant, and pair it with Lean Six Sigma judgment.
D'Souza argues that AI agents in enterprise and manufacturing should be grounded in Lean Six Sigma discipline before automation.
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They describe autonomous DMAIC, process ontology, and upskilling frontline experts into orchestrators—ideas that overlap with Bhushan's insistence that Lean judgment and bottleneck selection come before applying AI to a process.
D'Souza's teaching centers on a digital-twin sandbox and a formal Define-Measure-Analyze-Improve-Control loop.
Bhushan's examples focus on video-based time-and-motion studies, line rebalancing, and a specific manufacturing agent.
D'Souza also writes about personal wisdom and exit planning for business owners, a broader life-and-coaching register absent from Bhushan's posts.
Velaga writes about continuous improvement in manufacturing and questions how Lean practitioners choose which waste-reduction opportunities to pursue.
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They argue that improvement work needs advance assessment, what-if analysis, and a production model—a view that connects to Bhushan's point that choosing the wrong process makes an AI study useless.
Velaga advocates scientific scheduling and discrete-event simulation for high-mix, low-volume job shops, arguing that Lean's line-based assumptions mislead in those settings. Bhushan instead advocates an AI agent that classifies video against the seven wastes and rebalances a line, urging adoption now rather than making a case for scheduling software.
Reynolds builds a spatial-computing tool for ergonomic and time-motion studies that automates work manufacturing engineers still do by hand, using a phone camera and AI-driven analytics.
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The approach is close to Bhushan's video-based AI time-and-motion studies and their claim of large productivity gains.
Reynolds' approach combines 3D scanning, motion tracking, and iPhone-based spatial computing through a company spun out of MIT and PTC, with a product on the Microsoft Azure Marketplace. Bhushan emphasizes a manufacturing AI agent and Lean Six Sigma expertise as the bridge to adoption, and argues for urgency rather than presenting an origin story and marketplace milestone.
Devin Bhushan's LinkedIn profile showed 5,473 followers as of 8 October 2026.
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Five posts published 11–30 September 2026 target people who run and improve factory lines: they address Master Black Belts and Lean Six Sigma practitioners directly, describe time-and-motion studies, changeovers, line rebalancing and the seven wastes, and announce a Head of Lean Six Sigma hire and a Six Sigma advisor.
The content is built to attract continuous-improvement and manufacturing-operations professionals, making it the strongest basis here for judging relevance to a campaign aimed at AI-driven continuous improvement.
Before committing spend, ask for an audience survey or platform demographics covering geography, age and gender, plus recent results for comparable topics and formats if considering sponsored placement.
To assess professional attributes such as employer, occupation and job seniority, request survey data specifically; demographics alone will not answer those questions.
Across the five posts, Devin Bhushan promotes Squint's manufacturing AI products and recruits for Squint; they do not offer creator partnerships.
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The posts mention no sponsored post, brand collaboration format, rate, or sponsorship policy.
Their only calls to action invite readers to contact Squint about its Lean Manufacturing agent or link to a Squint job posting—company product and hiring routes, not an official channel for proposing a partnership with Devin.
Ask whether they accept sponsorships and, if so, for their partnership terms.
Brief A: an industrial technology brand selling plant-floor software wants to reach continuous-improvement leaders — Lean Six Sigma practitioners, manufacturing engineers, and plant managers — with a campaign built on a real line study.
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Devin Bhushan is a strong fit if the arrangement is a co-marketing or pilot partnership rather than a paid sponsored post.
The writing addresses Master Black Belts directly and uses the vocabulary these readers know: time-and-motion studies, changeovers, the seven wastes, line rebalancing, cycle time, and operator count.
It offers concrete operational examples, not general AI commentary: 144 hours of video from a previously unseen line in Singapore became 126 time studies across 32 processes, while a changeover video produced a full study with every segment classified and ready for rebalancing.
Bhushan argues that AI-driven continuous improvement is available today and tells readers to stop readiness and integration projects and start using it — the same message a plant-floor software brand would want to promote.
There is also practitioner credibility for co-marketing: Six Sigma figure Greg Brue is described as joining Squint as an advisor and helping build Lean and Six Sigma practice into the product, and Squint is hiring a Head of Lean Six Sigma to work between factory floors and engineering.
The main constraint is that Bhushan writes as a Squint executive, promoting Squint's product and recruiting for the company.
A brand in the same manufacturing-AI category would be asking a competitor's operator to promote a rival.
Ask whether Squint will participate in a joint pilot or co-produced case story; the posts provide no sponsorship format, rate card, media kit, or partnership contact.
When to choose a specialist
Brief B: a general enterprise AI platform, or a brand outside manufacturing, wants a paid sponsored-post series of broad “AI transformation” thought leadership for a general business audience, priced against reach and impressions, with standard sponsored-content disclosure.
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Devin Bhushan is a poor fit.
The posts' calls to action point to a Squint product and a Squint job posting, and the writing reads as company communication from inside a role, tied to one product, one hiring need, and one advisory announcement — not independent creator content into which a brand can slot a separate product.
The audience is manufacturing-specific, which makes the content useful to Brief A but narrow for a general business audience.
A follower count is available, but no demographics export, engagement data, or per-post performance record; ask for those before considering a reach-priced buy.
If the brand sells manufacturing AI, the conflict described in Brief A applies in reverse.
The urgency messaging — that facilities not using agents to improve continuously will lose — is also tied to a competitor's product claim, which another brand would have to adopt or contradict.
A creator or publication with documented audience data and sponsored-content practice would be a more defensible choice.
A worked campaign example
For Brief A, build a campaign around what is already public: a single-line pilot documented end to end, plus one expert-led session.
Step one is choosing the bottleneck, not filming.
Bhushan's hiring post says the tool can optimize a step that was never the bottleneck, so a continuous-improvement practitioner should select the line and constraint and agree in advance on what counts as improvement — cycle time held, operator count, or waste removed.
Step two is to capture and analyze that line: film a changeover or a full shift, run the study, classify every segment against the seven wastes, and propose a rebalanced line.
Step three is the published asset: a co-authored case story with the plant's improvement lead, showing the cost of the manual study before and after, with the plant named or anonymised as it chooses.
This follows the Singapore post's sequence — walk in, film, produce studies, rebalance — but uses the new pilot's measured numbers.
Step four is a live session: a community or customer webinar pairing the brand's product with a named Black Belt, following the advisory announcement and community webinar already visible in the posts.
Step five is an honest guardrail.
Before repeating the Singapore figures — 144 hours of video, 126 time studies, 78 days of engineering work replaced by one day — get the customer's written sign-off or omit them and use the pilot's results; the posts do not independently verify those numbers, and a record attempt is not a measurement.
First, ask whether any partnership or sponsorship terms exist.
If not, propose a joint pilot with clearly attributed roles rather than a paid placement.
Consider Benjamin for a campaign about applying spatial computing and AI to factory work, especially ergonomics or time-and-motion analysis.
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In a post about taking a product to the Azure Marketplace, Benjamin describes using phone-camera data to analyze physical work and says early users reported productivity gains in ergonomics analysis and time-motion studies. This offers a closer angle on spatial technology and product deployment than Devin Bhushan’s posts, which focus more directly on AI-led continuous improvement and line studies.
Benjamin’s posts connect their work to factory analysis but don’t show the same emphasis on Lean methods, bottleneck selection, or line rebalancing as Devin’s.
The productivity gains are claims in the post, not independently verified.
Confirm Benjamin’s identity and commercial availability before considering a partnership.
Consider Prasad for a campaign explaining production scheduling in high-mix, low-volume job shops, rather than promoting an AI continuous-improvement agent.
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One post uses city traffic as an analogy for how jobs move through a job shop; another argues that scheduling and simulation can help estimate how improvement efforts affect throughput, work in progress, and lead time. These examples offer a specialized angle for a scheduling or production-planning brief.
The posts focus on scheduling, simulation, and production-system behavior, rather than the camera-based time studies and automated line rebalancing at the center of Devin’s posts. Confirm Prasad’s identity and commercial availability before considering a partnership, and check that a scheduling-focused campaign matches the product and audience.
Consider Paul for a campaign about preparing enterprise teams to deploy agentic AI with process discipline and simulated testing.
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In one post, Paul argues for mapping processes with Lean Six Sigma before deploying agents and using a digital twin to test decisions before they affect live operations. That framing may suit a campaign aimed at leaders evaluating deployment risk rather than practitioners assessing a specific factory-line study.
Paul’s posts frame the topic at the enterprise deployment level, rather than offering the direct account of factory-floor time studies or line rebalancing found in Devin’s posts. Confirm Paul’s identity and commercial availability before considering a partnership.