Ori Shimon builds AI-assisted tools and processes for go-to-market and revenue operations teams, including internal pipeline software, HubSpot audits and process changes.
Build a thin layer over the tools you already own instead of buying another platform · A fix is not shipped until something checks the behaviour in production · Renewal savings come from cutting the lines you do not use, not from asking for a discount
Ori Shimon builds AI-assisted tools and workflows for go-to-market and revenue operations teams.
Their work ranges from an internal pipeline app layered over existing systems to HubSpot audits and safer ways to manage AI-assisted changes.
In one post, they report a 56% lower HubSpot renewal after comparing the contract with actual product usage.
Marketers interested in practical RevOps tooling and applying AI to business systems will find concrete examples, not just predictions.
Shimon explains the operational problem and how to test a solution.
The pipeline app example shows how existing CRM, call-recording and enrichment tools were brought together to surface stalled deals; one measure of success was reps using the app without prompting.
The HubSpot renewal review turns contract and usage data into specific cuts rather than a vague request for a discount.
Ori Shimon writes about running go-to-market and revenue operations on infrastructure you own, with AI coding agents doing the heavy work.
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Two convictions run through the posts: build a thin layer over the CRM, call recordings and enrichment tools you already pay for rather than buying another platform; and never let an agent change production data without a check that can actually fail. The same instinct applies to software spend: Shimon argues that most renewal savings come from comparing the contract with real usage, not asking for a discount.
Build a thin layer over the tools you already own instead of buying another platform
Ori Shimon argues that the best GTM software their team uses is not sold by any vendor.
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The team knows its own bottlenecks better than an outside vendor can, and current AI makes it practical to build a layer over existing systems without reinventing the wheel or getting locked into one provider. That layer sits over the CRM, call recordings and enrichment tools like AmpleMarket, bringing them together in one place.
Ori Shimon describes an internal platform that gives every rep an open pipeline, highlights bottlenecks and lets reps flag deals that need a push.
AI enriches each opportunity using the same tools, so every deal carries a real picture, not just a name and a number.
The proof it worked was behavioural: reps started opening it on their own to run their pipeline, without them in the room.
Ori Shimon leaves the bigger question open.
In a post to RevOps and GTM Ops peers, they ask whether anyone has replaced HubSpot with an in-house or lighter AI-based setup.
Some of it now looks very doable; some looks like a trap.
Have people built their own, moved to something cheaper, or started and bailed halfway?
The case for a layer on top is settled; whether the system underneath stays is not.
Renewal savings come from cutting the lines you do not use, not from asking for a discount
Ori Shimon argues that companies overpay on software not because vendors refuse to discount, but because nobody has the hours to compare the contract with actual usage.
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The method: read every contract line for cost, discount and quiet renewal terms; pull real usage from the API; compare the two; then take a list of lines to remove into the renewal. That turns negotiation from a request into a correction.
The comparison found a contact tier three and a half times bigger than the actual audience, seven licensed seats against three in use, and 470 workflows, with only a handful needing the paid automation tier.
The company had been paying for capacity it outgrew because nobody had the hours to check, Shimon says.
Give an AI agent write access to the CRM only behind staged checks and human approval
Ori Shimon is enthusiastic about pointing an AI coding agent at HubSpot to map the pipeline, audit workflows and make bulk changes across thousands of records through plain conversation, cutting work that took days to about 30 minutes.
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The counterweight is a fixed rule set: start read-only, verify the counts, build the change switched off, then approve it. Speed and safety are compatible if the sequence is enforced.
Ori Shimon cites the cost of skipping the sequence: one careless API write wiped the actions from 182 workflows.
The posts point to a cheat sheet with the full playbook.
Across the writing, the pattern is to aim an agent at real operational data, then verify before anything is written.
See Ori Shimon’s signature build: an internal go-to-market platform layered over tools the team already paid for.
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It shows why a RevOps or GTM Ops leader might build rather than buy.
Ori Shimon describes building an internal platform on top of existing CRM, call-recording and enrichment tools such as AmpleMarket.
The team couldn’t track pipeline accurately or see what was stuck, what needed attention and what was moving as planned.
The platform gives each sales rep a view of their open pipeline, highlights bottlenecks and lets reps flag deals that need a push.
AI enriches each opportunity using the same tools, giving each deal a fuller picture than just a name and a number.
Reps began opening the platform on their own to run pipeline without Ori Shimon in the room.
Shimon’s argument: teams know their problems better than vendors do, and current AI lets them build a layer on top of existing software without reinventing the wheel or being locked into one provider.
For brands planning a campaign
For a B2B SaaS company improving GTM operations, this is direct evidence of custom internal tooling and process design. A campaign or consulting engagement could focus on building a lightweight layer over existing systems rather than replacing them.
See how AI can inform a concrete software-spend decision, with the four-step method behind a 56% cheaper renewal.
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Ori Shimon used Claude Code to compare a HubSpot contract with actual usage and renewed 56% cheaper.
The process: review every contract line for cost, discount and quiet renewal terms; pull actual usage from the API, including contacts marketed to, seats assigned versus seats used, workflows and dashboards; compare the two; and enter the renewal with a list of lines to remove rather than a request for a discount.
The comparison found a contact tier three and a half times bigger than the actual audience, seven licensed seats against three in use, and 470 workflows, with only a handful requiring the paid automation tier.
Shimon says the company had been paying for capacity it had outgrown because nobody had the hours to check.
For brands planning a campaign
For a SaaS spend optimization or RevOps brief, this is a clear proof point: the work produced measurable savings and a stronger negotiating position. A campaign should lead with the audit method and the outcome, not general AI claims.
This post explains how to verify an AI-built fix in production—the discipline behind internal tools and a key piece often missing from stories about AI.
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Ori Shimon runs an internal pipeline app on top of the CRM, with an issue board for investigating bugs, gaps and ideas and tracking them through their lifecycle.
Claude Code usually writes the fix prompt, which Shimon sends to Lovable; the issue then moves to verification.
Claude Code drives a real browser against production, runs the scenario and checks whether the behavior changed.
The rule: if the app crashes or the result is unclear, the answer is unknown.
There are five outcomes: fixed, broken, indeterminate, unverifiable and pending.
An early version treated no clear answer as fixed, defeating the point, so the checker gets tested too.
For brands planning a campaign
For GTM engineering or RevOps consulting, this shows a quality-control mindset buyers can evaluate. A campaign could focus on reliable AI-assisted internal tools and how fixes are verified, rather than tool novelty.
This post sets out practical rules for using an AI coding agent on a system of record.
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It’s useful for anyone weighing speed against the risk of bulk API changes.
Ori Shimon runs HubSpot through an AI coding agent that maps the pipeline, audits workflows and makes bulk changes across thousands of records through plain conversation, turning days of work into 30 minutes.
The rule is to stay in the loop: start read-only, verify the counts, build with changes switched off, then approve.
Shimon learned this after one careless API write wiped the actions from 182 workflows.
The post links to a cheat sheet with the full playbook.
For brands planning a campaign
A campaign about AI-assisted RevOps should address governance and guardrails. This example supports a message about speed with safe, read-only-first operational discipline.
See the open question Ori Shimon is testing with peers, not a finished recommendation.
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The conversation remains unresolved.
Ori Shimon asks RevOps and GTM Ops professionals whether anyone has replaced HubSpot with an in-house system or a lighter setup built around AI.
Shimon says some of it looks very doable now and some looks like a trap.
The post asks readers to share whether they built their own, moved to something cheaper, or started and bailed halfway.
For brands planning a campaign
This could support a discussion, research or community-input campaign, but don’t treat it as proof that Ori Shimon completed a HubSpot replacement. Its practical value is in framing the tradeoff.
Building an internal GTM pipeline layer on top of existing tools1
Verifying AI-written fixes against production behavior1
Auditing SaaS contracts against actual usage before renewal1
Running HubSpot through an AI coding agent with guardrails1
Asking whether HubSpot can be replaced by an in-house or lighter AI setup1
Building an internal GTM pipeline layer on top of existing tools
Ori describes building an internal pipeline app on top of the CRM, call recordings, and enrichment tools such as AmpleMarket.
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It gives each rep their open pipeline, surfaces bottlenecks, and lets them flag deals that need a push.
AI enriches each deal from those tools, adding context beyond a name and a number.
Ori says reps started opening the tool on their own to run their pipeline.
Build a custom layer over existing sales and marketing systems rather than replace the stack, using AI to consolidate and enrich deal data.
For brands planning a campaign
A B2B SaaS company evaluating GTM operations work can assess Ori for hands-on pipeline software and internal tooling. Their experience is relevant to GTM engineering or RevOps engagements focused on visibility, rep adoption, and getting more from existing systems rather than buying another platform.
Verifying AI-written fixes against production behavior
Ori explains that an internal pipeline app runs on top of the CRM, with an issue board for bugs, gaps, and ideas.
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Claude Code usually writes the fix prompt; Ori sends it to Lovable, then moves the issue to a verifying state.
Claude Code drives a real browser against production, runs the scenario, and checks whether the behavior changed.
The checker uses five outcomes: fixed, broken, indeterminate, unverifiable, and pending.
If it crashes or cannot tell, the answer is unknown.
Ori says an early version treated no clear answer as fixed, defeating the point.
Separate fix delivery from verification, use a real browser against production, and treat uncertainty as unknown rather than success.
An early version of the checker treated no clear answer as fixed; Ori now tests the checker itself.
For brands planning a campaign
This shows how Ori approaches quality control for AI-assisted internal tools. Brands considering similar CRM or workflow automation projects can assess whether this verification discipline fits their needs, especially where a false fix could disrupt revenue operations.
Auditing SaaS contracts against actual usage before renewal
Ori used Claude Code to compare a HubSpot contract with actual usage.
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The contract review covered each line, its cost, discount, and renewal terms.
The usage review pulled data from the API: contacts marketed to, seats assigned versus seats used, workflows, and dashboards.
Ori found a contact tier three and a half times bigger than the real audience, seven licensed seats against three in use, and 470 workflows, with only a handful needing the paid automation tier.
Rather than ask for a discount, Ori brought a list of lines to remove to the renewal and renewed 56% cheaper.
Compare contract terms with actual API-level usage, then negotiate by removing line items.
For brands planning a campaign
A B2B SaaS company looking to reduce software spend can assess Ori for a RevOps or GTM operations engagement focused on SaaS cost reduction, license rationalization, and renewal preparation.
Running HubSpot through an AI coding agent with guardrails
Ori says they use an AI coding agent to map HubSpot pipelines, audit workflows, and make bulk changes across thousands of records through plain conversation.
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Work that took days can take 30 minutes.
Their rule is to stay in the loop: start read-only, verify counts, build with changes switched off, then approve.
Ori also describes a costly exception: one careless API write wiped the actions from 182 workflows.
Use AI-assisted bulk operations on CRM data, but start with read-only checks, verify counts, build with changes switched off, and require explicit approval before writing.
Ori cites a careless API write that wiped the actions from 182 workflows as the reason for the guardrails.
For brands planning a campaign
This shows practical experience with AI-assisted CRM work and an explicit safety model. It is most relevant to organizations seeking faster HubSpot administration and bulk data changes with a documented approach to preventing destructive writes.
Asking whether HubSpot can be replaced by an in-house or lighter AI setup
Ori asks RevOps and GTM Ops peers whether anyone has replaced HubSpot with an in-house or lighter setup built around AI.
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They say some of it looks very doable now, while some looks like a trap. The post asks whether people built their own, moved to something cheaper, or started and bailed halfway.
Use an open question to peers to gather real experiences before deciding on a major platform change.
Ask Ori for an example of a completed platform replacement and the final recommendation.
For brands planning a campaign
Ori is exploring build-versus-buy decisions in GTM systems and asking practical operational questions. The post does not establish a completed HubSpot replacement engagement, so treat it as a starting point for discovery.
Across the posts, Ori uses HubSpot, Claude Code, Lovable, and AmpleMarket to build pipeline apps, issue boards, contract audits, workflow changes, and API-level usage analyses.
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They favor short, practical explanations, numbered steps, and direct questions to peers. Their recurring approach is to connect AI coding agents to operational data, then add verification or approval steps before taking action.
Combine AI coding tools with direct CRM and contract data, then package the process into steps, rules, or a cheat sheet.
For brands planning a campaign
For tool vendors and B2B SaaS companies, Ori's posts show a creator who works in the details of GTM operations and names specific software. That makes Ori relevant to campaigns about GTM engineering, RevOps automation, HubSpot administration, and AI-assisted internal tooling.
The five posts disclose no sponsorship, affiliate or client relationship.
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They mention tools Ori Shimon uses for GTM and RevOps work: HubSpot as the platform beneath an internal pipeline app, Claude Code and Lovable in the build-and-verify loop, and AmpleMarket as an enrichment source. Ask for details if partnership terms matter.
HubSpot
Mention
HubSpot is the CRM and marketing platform Ori Shimon’s team uses.
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In one post, they describe auditing the contract against actual usage and renewing 56% cheaper after finding an oversized contact tier, seven licensed seats against three in use, and 470 workflows mostly outside the paid automation tier.
Another describes using HubSpot through an AI coding agent to map the pipeline and make bulk record changes; one careless API write had previously wiped actions from 182 workflows.
A third asks peers whether they have replaced HubSpot with an in-house or lighter AI-based setup.
Claude Code is the AI coding agent Shimon uses across several workflows: reviewing the HubSpot contract line by line and pulling actual usage from the API for a renewal comparison; writing fix prompts, then driving a real browser against production to verify whether a fix shipped; and operating HubSpot for pipeline mapping and bulk changes.
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They describe staying in the loop, working read-only first, and building with switches off before approving.
AmpleMarket is one of the enrichment tools used by the internal pipeline platform, alongside the team’s CRM and call recordings, giving each opportunity deal context instead of just a name and number.
Ori Shimon writes about running go-to-market and revenue operations on systems a team owns and controls.
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Start with the post on building an internal pipeline app as a layer over the existing CRM, call recordings and enrichment tools.
It sets out the argument the other posts apply: teams know their own bottlenecks better than a vendor does, and AI now makes it practical to shape the tools they already pay for instead of buying another platform.
Two posts show how that argument works in practice: one verifies an AI-written fix against production behaviour rather than trusting a pushed change; the other runs HubSpot through an AI coding agent, with read-only checks, count verification and explicit approval before anything is written.
The contract and usage audit post is the clearest example of applying the same habit to software spend: it compares contract terms with actual usage and arrives at a renewal with lines to remove rather than a request for a discount.
Read the peer question about replacing HubSpot with a lighter in-house setup last; it shows which part of the build-versus-buy debate they still treat as unresolved.
Both write about whether a B2B software team should build its own CRM-adjacent system or keep buying.
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Both push the decision past the demo and into operations: integrations, reporting people can trust, permissions, security, and who can run the system after its builder moves on.
Sergey answers with cost modelling and partner selection: a three-year total cost of ownership comparison across SMB, mid-market and enterprise scenarios, plus a five-point test for vetting RevOps agencies. Ori answers from inside a build, describing the internal pipeline app and the verification rules that keep AI-assisted changes to live CRM data safe.
Both publish practical, detailed walkthroughs of HubSpot and CRM capabilities for revenue teams, naming the specific features and tools involved and explaining how reps or admins would actually use them day to day rather than summarising a release note.
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Chris explains vendor features from the outside — how to put each skill in HubSpot's Claude plugin to work, what changes in a portal when Deal Progression enters beta, and which settings teams will forget to switch on. Ori describes systems they built and audited: an internal issue board, production verification of AI-written fixes, and a contract-versus-usage analysis pulled straight from the API.
Both work through concrete, reproducible HubSpot setups for RevOps practitioners and write implementation notes for the team doing the configuration, not buyers being sold an outcome.
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Orlando's posts stay inside native HubSpot features — drafting a discovery playbook with Breeze and routing a shared support inbox into a ticket queue with an AI first pass and a human handoff. Ori works a level down, using AI coding agents and the API for bulk changes, usage audits and contract review on top of whatever stack a team already runs.
The only audience figure published on Ori Shimon’s profile is 3,476 LinkedIn followers, as of 8 October 2026.
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Their posts do not identify those followers by geography, age, gender, seniority, job function, employer or company size, and include no audience survey.
For a campaign aimed at a B2B SaaS company tightening GTM operations and software spend, this is a fit question, not a scale question.
The audience is small and the subject narrow, making this best suited to a targeted touch with practitioners who own GTM/RevOps tooling decisions, not a broad awareness buy.
The five posts dated 1 July – 5 October 2026 speak to people who run the stack themselves: auditing a HubSpot contract against actual usage and renewing 56% cheaper; running HubSpot through an AI coding agent with read-only-first verification; making bulk changes across thousands of records; building an internal pipeline app on top of the CRM with its own issue board; and asking other RevOps and GTM Ops professionals whether they have replaced HubSpot with something lighter.
Two posts close with a direct peer ask or a saveable asset, suggesting content for working practitioners rather than a general business readership.
This supports relevance to a campaign focused on tightening GTM operations and software spend, but does not establish who follows.
The small sample cannot show whether a topic affected reach, and the posts publish no engagement numbers for comparison.
Ask whether Shimon has audience survey results or platform analytics covering geography, job function, seniority and company size, along with recent results for comparable topics and formats (views, reactions, comments, and any sponsored or promoted posts).
Without those details, the follower count is the only measured audience input; plan for a narrow expert-audience placement.
Across five posts spanning July to October 2026, Ori Shimon names HubSpot, AmpleMarket, Claude Code and Lovable as software they use or evaluate in GTM and RevOps operations, not as disclosed paid partnerships.
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The posts publish no rate card, package, sponsored-format description or inquiry route.
Brands should approach them directly to negotiate an engagement, likely focused on GTM operations, RevOps tooling, or AI-assisted build or audit work.
A B2B SaaS company with a bloated HubSpot contract and a pipeline the sales team doesn’t trust wants a short RevOps or GTM engineering engagement to cut software spend, surface pipeline problems, and avoid replacing the CRM.
Reasons
The brief closely matches Ori Shimon’s contract-audit work. They’ve described comparing a HubSpot contract line by line with real API usage and renewing 56% cheaper. The audit found a contact tier three and a half times larger than the real audience, seven licensed seats against three in use, and 470 workflows where only a handful needed the paid automation tier. That’s the work this brief calls for: identify unused paid capacity and go into renewal with specific lines to remove, not a general discount request.
They’ve described building a pipeline layer on top of the CRM, call recordings, and enrichment tools such as AmpleMarket. The layer shows each rep their open pipeline, surfaces bottlenecks, and lets reps flag deals that need a push. The stated sign of success was reps opening it on their own to run pipeline. For a company looking to improve GTM operations without buying another platform, this is relevant evidence of hands-on build work and adoption.
Shimon’s stated guardrails address the risk of CRM changes. They’ve described using an AI coding agent on HubSpot for bulk changes across thousands of records, following a rule: read-only first, verify counts, build it switched off, then approve. They also describe one careless API write that wiped the actions from 182 workflows, explaining why those guardrails exist. A buyer concerned about destructive CRM changes can evaluate that discipline directly.
They’ve described a separate verification method for AI-written fixes: drive a real browser against production, run the scenario, and confirm the behavior changed. Outcomes are fixed, broken, indeterminate, unverifiable, and pending, with unknown used when the checker can’t tell. That supports the quality-control needs of a GTM operations engagement.
The LinkedIn follower count is 3,476, observed 8 October 2026. This is a narrow expert engagement, not a broad awareness play.
When to choose a specialist
Poor fit based on the posts reviewed.
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Brief
A B2B SaaS vendor wants a paid LinkedIn sponsorship with guaranteed reach to promote a new RevOps automation product to senior revenue leaders, backed by a documented rate card and campaign performance metrics.
Reasons
The posts don’t describe sponsorship or partnership formats, show a rate card, sponsored package, media kit, or give a partnership inquiry route. Ask whether Shimon accepts sponsorships and what terms they offer.
The audience is described only by its follower count: 3,476 LinkedIn followers, observed 8 October 2026. There’s no data on geography, job function, seniority, company size, or engagement. A vendor seeking guaranteed reach to senior revenue leaders should ask for audience data before proceeding.
The content is practitioner-to-practitioner. Shimon writes about auditing a HubSpot contract, running HubSpot through an AI coding agent, building an internal pipeline app, and asking RevOps peers whether they’ve replaced HubSpot. Two posts close with a direct peer ask or a saveable asset. That format can work for expert content, but doesn’t establish broad reach for a sponsored campaign.
Before deciding, ask for sponsorship terms, disclosure practices, an audience survey, and past sponsored-post performance. Without that evidence, treat the fit as unproven, not disqualified.
A worked campaign example
Campaign angle
HubSpot renewal audit plus a pipeline visibility sprint. This is a consulting or GTM engineering engagement, not a sponsored placement. The buyer is a B2B SaaS company that already runs HubSpot, suspects it pays for unused capacity, and wants better pipeline visibility without replacing the CRM.
Why this angle works
The pitch draws on Shimon’s signature work rather than generic AI claims. It pairs a concrete cost-reduction task with an operations task, both tied to examples from the posts: a contract-versus-usage audit, a pipeline layer built on existing tools, bulk-change guardrails, and production verification. The main limitation is audience scale: this suits a targeted expert engagement, not a mass-reach campaign.
Worked steps
Discovery: pull the HubSpot contract lines and compare them with real API usage. Check contacts marketed to against the contracted contact tier, seats assigned against seats used, workflows against those that actually need the paid automation tier, and dashboards.
Deliverable one: a line-item removal list for the renewal call. The goal is to bring specific lines to cut, backed by usage numbers, rather than ask for a discount. This follows the method Shimon described when the renewal came in 56% cheaper.
Deliverable two: a light pipeline layer on top of the existing CRM, call recordings, and enrichment tools such as AmpleMarket. Each rep sees their open pipeline, bottlenecks stand out, and reps can flag deals that need a push. AI enriches each opportunity from the same tools so deals carry context instead of just a name and a number.
Quality control: start with read-only checks, verify counts, build changes switched off, and approve before any write. For AI-written fixes, apply the production verification rule: fixed, broken, indeterminate, unverifiable, or pending, with unknown when the checker can’t tell.
Success measures: the renewal removes unused capacity, reps open the pipeline view without prompting, and AI-assisted changes are verified against production behavior rather than assumed to be live.
Choose Sergey Danishevskiy when the buyer has not settled the build-or-buy question and needs to decide before operations work begins.
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The work models the three-year total cost of owning HubSpot versus building a custom CRM across three B2B SaaS scenarios, assuming AI-assisted development: an 8-seat, $1M ARR company at $61K against $225K; 45 seats and $15M ARR at $480K against $1.85M; and 250 seats and $100M ARR at $2.8M against $6M.
The same material lays out what estimates must include once a prototype works: migrating real data, fixing permissions, maintaining email, calendar, billing, and product integrations, making reporting trustworthy, and ensuring someone other than the person who prompted the system can run it.
A second post offers a five-point screen for RevOps partners: whether they diagnose before quoting, build rather than advise, plan for their own exit, cover the full revenue loop from marketing handoff to retention, and tie accountability to outcomes rather than hours.
This is useful when a buyer is choosing a partner or defending a budget case to a board—not when HubSpot is already staying and the buyer wants to cut the bill.
The posts offer decision support and cost modeling, not evidence of cost reductions in a live account.
They show no contract audit, renewal outcome, or unused tier lines identified against real usage.
A buyer paying for capacity they no longer use gets an argument for keeping the CRM, not a method for cutting line items.
Choose Chris Tveter when the buyer has decided to stay on HubSpot and needs to get its native AI features switched on and adopted by reps.
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The material walks through the free HubSpot Sales Plugin for Claude skill by skill, with a recommended use for each: call prep before a meeting, a daily brief as the first task of the morning, logging a call in plain language right after hanging up, drafting a recap email from deal context, a pipeline pulse before the weekly review, contact lookup, contact import from a CSV or pasted list, and a catch-all for field updates and CRM questions.
It also flags a safeguard: Claude shows the proposed change before writing to the account.
A second post covers HubSpot Deal Progression in public beta: CRM updates suggested after each call, email, or meeting; a next best action; a drafted follow-up; a new Catch-Up tab; methodology configuration for MEDDIC, BANT, or SPICED; branded buyer deal plans; and a re-engagement prompt after 14 days of silence, along with the admin switches most teams miss.
This is a good fit when the gap is rep adoption of tools the company already pays for.
The content covers product adoption and vendor-feature configuration.
It shows no contract-versus-usage audit, renewal saving, or custom layer built on top of the stack.
A buyer with spend tied up in unused tiers and oversized contact counts gets enablement, not a smaller bill, and would still need someone to do the cost work.
Choose Orlando G. when the buyer’s operations problem sits inside a specific team process, not in the contract.
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The two posts cover repeatable process builds: using HubSpot Breeze to draft a discovery-call playbook framework, choosing among the generated options, correcting the answer-type setting HubSpot tends to default wrong, and attaching the playbook to the contact record so each question and answer lands on the timeline for whoever works the deal next.
The second routes a shared support inbox into a HubSpot ticket queue, lets AI handle the first pass from the knowledge base, and hands off to a human with an assigned owner who must read the chat log before replying.
This suits a buyer whose support email lands in one person’s inbox or whose reps conduct inconsistent discovery calls, and who wants to fix those processes with tools already in place.
The scope is narrower than a full RevOps engagement and offers no cost evidence.
Both posts walk through HubSpot capabilities; neither shows a contract audit, renewal outcome, or custom system built on top of the stack.
A buyer whose goal is reducing software spend would be choosing process improvement instead.
This profile covers five LinkedIn posts by Ori Shimon about internal GTM pipeline tooling, a HubSpot contract and usage audit, running HubSpot through an AI coding agent, production verification of AI-written fixes, and a peer question about replacing HubSpot.
The posts do not give publication dates, so no timeline of development is claimed, and they cover no other platform or complete account history.
The follower count is 3,476, observed 8 October 2026.
Similar creators include Sergey Danishevskiy, Chris Tveter and Orlando G.; two recent posts from each are dated between September 10 and October 8, 2026.
Ori Shimon's posts do not mention sponsorship, affiliate or client terms, so no partnership formats are documented here; this does not mean none exist.
