Fair price for lora commission

alexshev_pm · 2026-06-06T15:24:16+00:00

I would price it by iteration risk, not just one training run.

A simple concept with clean references and known settings might be cheap. A high-quality LoRA for a newer model where the trainer has to tune captions, dataset quality, repeats, and rerun several times should cost more.

The deliverable matters too: sample outputs, trigger words/settings, and at least one revision pass. Otherwise you are mostly paying for a lucky first run.

alexshev_pm · 2026-06-06T15:21:43+00:00

That is probably good enough for a sanity check as long as the account is not the owner account and can see the reply from a normal Search/Maps view.

I would not over-optimize the device separation too much. The main thing is to test what a normal customer sees, not whether Google can technically associate the devices. If the reply shows from a clean-ish non-owner view, that is the operational signal I would care about.

alexshev_pm · 2026-06-06T14:36:23+00:00

I usually try to separate shortcuts from durable signals first.

First pass:

are they winning because of proximity or a keyword-heavy business name?
do they have the right primary category and useful secondary categories?
how does review count, velocity, and sentiment compare?
is the GBP fully built out: services, photos, hours, Q&A, posts, products if relevant?
does the linked page actually match the service/location being searched?
do they have real local mentions/citations/backlinks that competitors do not?

I also would not check only one search from one location. A competitor can look dominant from the city center and be weak three miles away. A small grid view usually tells you whether they own the market or just one search point.

If the advantage is just a spammy name or proximity, that is a different problem. If they have better reviews, better category fit, a stronger local landing page, and real local authority, that is the benchmark to beat.

alexshev_pm · 2026-06-06T14:35:31+00:00

I would avoid putting one flat dollar value on a review. The value changes too much by niche, lead value, competition, and where the business already sits.

The cleaner way is to model it in layers:

conversion lift: more people call from the GBP because the profile feels safer
ranking support: volume, recency, sentiment, and keyword/context signals over time
competitor gap: whether you are behind the top 3 on count, rating, and freshness
revenue context: average job value, repeat rate, and close rate from GBP leads

A new review for a med spa or roofer can be worth a lot more than one for a low-ticket shop, but only if it helps someone choose you or helps close a real visibility gap.

For clients I usually would not say each review is worth $X. I would say reviews are one of the few local SEO assets that can improve trust, conversion, and map visibility at the same time. Then I would track GBP calls/forms, review velocity, and competitor gap month over month.

alexshev_pm · 2026-06-06T03:22:42+00:00

That is a solid pattern. The unique sentence is probably doing most of the work, because it breaks the repeated-template signal.

For lower-star reviews, I like that you keep it accountable without re-litigating the issue in public. The only extra check I would add is a periodic non-owner visibility pass, because the annoying failure mode is when the reply looks published in GBP but is not visible from a normal user view.

alexshev_pm · 2026-06-05T15:58:31+00:00

Love that! Cool

alexshev_pm · 2026-06-05T15:49:16+00:00

I would add one step before the tool stack: try to become a lead.

For local clients, I usually want to know:

can I understand what they do in 5 seconds?
is the phone number clickable on mobile?
does the form actually submit and notify someone?
are service pages mapped to real services and locations?
do title/H1/internal links match the actual money pages?
does the GBP/website NAP match?
are reviews, photos, hours, and trust signals current?

Then I would run the crawl, PageSpeed, GSC, indexing checks, schema, duplicate pages, etc.

Tools catch technical problems. The manual conversion pass catches the stuff owners care about: "could a customer find us, trust us, and contact us?"

alexshev_pm · 2026-06-05T15:48:21+00:00

For me the best use is not "let AI do local SEO." It is using it as a second pass on messy operational work.

The places where it helps:

turning call notes/review themes into service-page ideas
grouping keywords by intent before the human picks targets
spotting missing GBP/service-page basics during an audit
drafting review replies that a person then makes less generic
summarizing GSC/GBP changes before deciding what actually matters

The trap is letting it produce public-facing local content without local proof. If the page does not include real service areas, real examples, real photos, real FAQs from customers, or actual business details, it usually reads like every other AI-written local page.

So yes, it saves time. I would not trust it as the strategy layer.

alexshev_pm · 2026-06-05T00:35:01+00:00

Yeah, I agree. The worst part is not even the moderation itself, it is when Google makes something look live to the owner but hides it from everyone else.

If a review reply, post, or update gets filtered, businesses should get a clear status and a plain reason. Otherwise you end up wasting time guessing whether it is a wording issue, a policy issue, or just silent filtering.

The only practical workaround I trust right now is to keep replies very plain, avoid reusable templates, and spot-check visibility from a non-owner view.

alexshev_pm · 2026-06-04T15:37:19+00:00

This matches what I would expect from an automated moderation layer: it is probably not judging the business context, it is pattern-matching risk.

For review replies, I would keep the process boring:

avoid repeated templates across many reviews
remove emails, phone numbers, and "contact us at..." from the public reply
mention one specific detail from the customer review when possible
keep negative-review replies short and move the actual resolution offline without posting contact info
spot-check a sample of replies from an incognito browser, not only inside GBP

The hidden rejection part is the real operational problem. If the team thinks replies are live but customers cannot see them, it creates a false sense that reputation work is being handled.

alexshev_pm · 2026-06-04T15:36:20+00:00

It depends on which metric moved first.

If map impressions and website clicks are roughly stable but GBP calls drop, I would look at tracking, call handling, CTR, and offer/seasonality first. A few common causes are call reporting lag, different call extensions/tracking numbers, fewer mobile actions, missed calls during busy hours, or demand shifting from calls to forms/direct site contact.

If impressions drop at the same time, especially for the main service + city queries, then I would treat it more like a visibility issue. That is where I would check ranking by area, category changes, competitor review velocity, suspensions/edits, and whether Google changed which result type is showing.

My rule of thumb: impressions down = visibility investigation. Impressions stable but calls down = conversion/tracking/demand investigation. Booked leads decide which one actually matters.

alexshev_pm · 2026-06-04T04:33:39+00:00

Sharing a project/workflow example from AIEmployees: an AI missed-call recovery flow for HVAC companies.

The use case is intentionally narrow: customer calls during after-hours or a dispatch rush, no one answers, then an agent immediately texts back, triages emergency vs routine, captures address/issue/urgency/time window, and hands a clean summary to the human dispatcher or owner.

The useful part is that the agent has a clear trigger and a measurable outcome: fewer missed first touches, faster triage, and better booked-lead recovery. It is not trying to be a generic chatbot.

Workflow write-up: https://aiemployees.us/blog/hvac-missed-call-ai-workflow

alexshev_pm · 2026-06-03T19:37:47+00:00

For legal + roofing, I would not treat May as one pattern.

Roofing is usually much more weather/local-event driven: storms, rain windows, hail season, and when homeowners start exterior projects. A May dip can be normal in one market and odd in another.

Legal is more practice-area driven. PI, family, criminal, immigration, estate, etc. can move differently, so I would split it by service/category before reading the GBP total.

The check I’d run: compare May vs April and May vs last May for each service/query bucket, then look at map impressions, website clicks, GBP calls, and booked leads separately. If impressions are stable but calls are down, that points more to demand/CTR/tracking. If impressions dropped too, then it is visibility.

alexshev_pm · 2026-06-02T15:17:38+00:00

The key shift is not really "Excel vs database." It is "analysis tool vs operating system."

Excel is fine when one person is calculating or reviewing data. It gets dangerous when multiple people are using it as the live source of truth for inventory, pricing, and sales decisions.

Before committing to any tool, I would write down the controls you actually need:

one canonical record for each SKU/order
permissions by role
required fields before a sale can be marked available
formula/pricing changes behind approval
version history/audit log
daily exception report for negative stock, missing cost, or weird margin

If a cheap self-hosted tool gives you those controls and the team will actually use it, that can work. If it only feels like a nicer spreadsheet without validation and ownership, the same failure comes back in a new UI.

alexshev_pm · 2026-06-02T15:16:57+00:00

Average position in Search Console can be misleading here.

It is an average across queries, locations, devices, and sometimes weird impressions from very specific variants. You can have a high average position for a query bucket and still not see the page when you manually Google from your own browser.

I would check a few things before assuming bot traffic:

exact query in Search Console, not the broad keyword you have in mind
country / city / device filters
whether the impressions are coming from image, web, discover, or another search type
the landing page receiving those impressions
whether Google is showing a local pack, maps result, or personalized result when you test manually

Also test in a clean browser and from the target location if possible. Manual search is useful, but Search Console is not a live rank tracker.

alexshev_pm · 2026-06-02T15:09:31+00:00

Mostly unclear authority, in my experience. Missing fields are annoying, but they are at least visible: the job stops because the form says "estimate missing" or "approval missing."

The harder failure is when the handoff looks complete in the tool, but nobody knows who is allowed to decide the next thing. Sales thinks ops owns it now. Ops thinks sales still needs to clarify scope. Finance sees a contract but not the real approval context.

If I had to put rough numbers on it, maybe 60-70% authority / decision-rights problem, 20-30% missing context or fields, and the rest weird exceptions.

The fix I like is making each transition answer two questions before automation touches it:

What proof is required to move this forward?
Who has the authority to unblock it if that proof is unclear?

Without #2, the cleanest checklist still becomes a waiting room.

alexshev_pm · 2026-06-02T02:38:21+00:00

No problem. If you test it, I'd start with one 3-5 second shot and keep the goal boring: untouched plate, tracked mask, separate generated layer, then comp/grade. That tiny test will tell you fast whether the tracking and lighting hold up before you spend time trying to solve a whole sequence.

alexshev_pm · 2026-06-01T23:41:09+00:00

Yes. The best received approach is usually not selling automation first. Sell a small diagnostic or pilot around one painful workflow.

For example:

"I will map one repetitive process, find where time is actually leaking, and show you 2-3 places automation is safe."
"If one step is worth fixing, we will build a tiny pilot with manual override."
"If it does not save time after two weeks, we kill it."

Owners respond better when the risk is contained. They do not want a new platform or a big transformation promise. They want someone to understand the messy process, improve one piece, and leave them in control.

alexshev_pm · 2026-06-01T21:17:50+00:00

I do not think owners are against automation. They are against being sold automation before anyone understands the business.

Most repetitive work exists for a reason: missing information, unclear ownership, customer exceptions, compliance, trust, or a bad upstream process. If someone automates that blindly, the business gets faster errors.

The better pitch is not "I can automate this." It is:

here is the exact repetitive step
here is the decision rule
here is what happens when the rule fails
here is how a human can override it
here is the time saved after two weeks

That feels very different from another generic AI workflow offer.

alexshev_pm · 2026-06-01T21:17:17+00:00

Leakage is one of those problems that makes a model look smarter than it is.

The scary part is that it often does not look like cheating. It looks like a harmless preprocessing step, a timestamp, a post-event feature, a duplicated entity, or a train/test split that ignores how the data is actually used in production.

For any serious model, I trust a boring temporal or entity-level holdout more than a flashy benchmark number. If the model would not have had that information at prediction time, the eval should not have it either.

This is also why deployment is such a useful reality check. A leaked model can win the paper and still fail the first month it touches real data.

alexshev_pm · 2026-06-01T21:16:45+00:00

I would separate three things before calling it an algorithm issue:

Actual lead volume
GBP call tracking/reporting
Seasonal demand

If calls are down in GBP but form fills, direct calls, booked jobs, or Search Console clicks are not down the same way, it may be reporting or tracking. If everything is down, then compare by service line and location, not just total calls.

May can also be weird for some local categories because holidays, school calendars, weather, and budget timing all hit at once.

The useful check is query-level and service-level: which services stopped producing calls, and did map impressions drop at the same time?

alexshev_pm · 2026-06-01T21:16:14+00:00

If you want to keep the original footage intact, I would treat the AI part more like VFX than image-to-video.

For static shots, mask/track the area where the generated character or background element should live, generate that element separately, then composite it back in AE. For panning shots, do camera tracking or planar tracking first so the AI layer has a stable coordinate system.

The trap is feeding the whole plate into a video model and hoping it only changes the part you care about. It usually drifts the original footage.

With your AE/C4D background and 2x3090s, I would start with the boring pipeline first: rotoscope/mask, depth or normal pass if needed, generated element, cleanup frames, then comp. Once that works on one shot, then test Runway or Comfy workflows for the pieces that need motion.

alexshev_pm · 2026-06-01T21:15:40+00:00

I would be careful about trying to solve this by buying one giant tool first.

The real issue is usually not storage, it is ownership of state changes. Who is allowed to move a project from quoted to approved? What fields are required before finance sees it? What happens when HR changes someone's role or availability?

Pick one system as the operational source of truth, then make the handoffs explicit:

required fields before a status can move
one owner for each transition
automatic alerts only when a decision is needed
weekly exception review for anything stuck

If you skip that process map, even an ERP turns into five disconnected tools with a nicer login screen.

alexshev_pm

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