# Jacob Galajda

Jacob Galajda works on Dupely, a shopping tool that compares product listings to identify duplicates and help shoppers find lower prices. Posts by Galajda also discuss data infrastructure, analytics, and using reliable data in AI projects.

- Profile: https://b2binfluencer.marketing/creators/jacob-galajda/
- LinkedIn: https://www.linkedin.com/in/jacob-galajda
- Topics: artificial-intelligence, ecommerce, data-quality
- Based on 14 dated posts, latest 2026-09-16

## Summary

They are most useful for practitioner-led B2B content on data readiness, analytics, CRM, and AI foundations.

- Best for: B2B SaaS or data vendors running acquisition-readiness or data-governance campaigns.
- Not for: Consumer packaged goods or lifestyle brands seeking broad reach or paid placements.
- Check first: Ask for recent LinkedIn audience analytics and evidence of roles, industries, and seniority.

### Takeaways

- M&A data cleanup can fail when missing history cannot answer acquirer questions.
- Collect accurate data consistently and preserve historical meaning when CRM fields change.
- Data can be accurate yet hard to use due to unclear relationships or API limits.

## Main ideas

- **Bad data is a systems problem, not just a quality problem**: Jacob Galajda argues that calling bad data a quality issue misses common failure modes: undefined or unclear relationships between records, weak mappings, and hosting systems with restrictive API rate limits. The right strategy depends on the intended use: frequently updated e-commerce prices need different handling from a CRM with fewer records and last-edit sorting. Before large AI projects, they inspect the data before discussing token budgets. Feeding old or out-of-scope data into an AI layer worsens the output.
- **Historical data continuity decides whether diligence can tell the real story**: Jacob Galajda describes helping with an M&A data cleanup where records were scattered across a CRM, spreadsheets, files from abandoned tools, and locally saved reports. The harder problem was not storage but that existing data no longer answered the acquirer's questions: growth-era metrics, renamed or retired fields, and missing retention, support volume, and pipeline velocity by segment left gaps. To avoid this, they recommend collecting accurate data consistently, even when it feels nonurgent, and understanding the effect on historical continuity before changing CRM fields.
- **Build the data ecosystem in a practical order: CRM, fulfillment, SOPs, KPIs**: For a client's data ecosystem, Jacob Galajda prioritizes a CRM with clean data and automation that reduces manual entry; a fulfillment system with tracking or download capability that is used consistently; documented SOPs with frequent audits; and KPIs based on industry-standard formulas. Dashboards, automation, and AI all depend on the underlying data, so bad data corrupts what is built on top of it, even when there is a paper trail.
- **Claims and metrics should withstand inspection**: Jacob Galajda criticizes benchmark loopholes, alleged plagiarism, inflated user counts, and milestone definitions that get watered down every six months. Their answer is measurement that outside systems can verify: third-party platforms for engagement and fulfillment actualization, event handling that matches third-party reports, and Cognito tables that separate guest users from sign-ups. Their principle: build as if someone will ask you to open the curtain, because someone will.
- **No-code tools are a prototype layer, not a real-time production backend**: Jacob Galajda explores the thesis that cloud hosting systems could absorb next-generation programming languages, with Notion, Airtable, or CRMs acting as app backends through APIs. In practice, they report, this has not arrived: bulk data ingestion against one unnamed CRM repeatedly hit 429 errors and broke the pipeline; Notion either rate-limited or fetched slowly for internal operations; and Google Sheets and Airtable were useful but not real-time. No-code is good for prototypes, but real-time, cost-optimized infrastructure still depends on software developers.
- **Dupely's wedge is duplicate detection with licensed data and in-house infrastructure**: Jacob Galajda compares Alexa for shopping with Dupely and identifies duplicate detection as the product difference: Alexa owns its data and is not geared toward duplication detection, while Dupely licenses data and provides cross-product analysis to identify duplicate Amazon listings. The business model relies on affiliate links rather than consumer payment. They say the solution is deployed in-house rather than wrapping GPT or orchestrating AI agents, and that the backend was purpose-built for large request volumes with vendor fallbacks, security, and uptime monitoring.
- **In consumer shopping, trust depends on speed, cost, and accuracy**: Jacob Galajda frames Dupely's infrastructure work as building a trust layer, not just optimizing a backend. The company moved from in-house computing to ALB-provisioned resources and then to a dedicated cloud SaaS provider for some elements, aiming to improve user speed, cut operating costs, and improve recommendation accuracy without passing costs to users. E-commerce platforms need to trust that Dupely reduces churn and decision fatigue; shoppers need to trust that it works whenever they need it.

## Work to read

- [M&A data cleanup: when the record fails to tell the story](https://www.linkedin.com/posts/jacob-galajda_a-few-years-ago-i-helped-with-a-data-cleanup-activity-7455254594705764352-4Htp)
- [Client data ecosystem: CRM, fulfillment, SOPs, KPIs](https://www.linkedin.com/posts/jacob-galajda_my-team-helped-one-of-our-clients-refine-activity-7353061253931491328-TzVy)
- [Bad data is more than poor quality](https://www.linkedin.com/posts/jacob-galajda_bad-data-is-such-a-broad-term-and-most-activity-7501733581635973120-cSQ3)
- [Dupely vs. Alexa for shopping: duplicate detection as the product](https://www.linkedin.com/posts/jacob-galajda_alexa-for-shopping-compared-to-our-startup-activity-7470104314187960320-VH9o)
- [Speed and trust in a shopping app](https://www.linkedin.com/posts/jacob-galajda_dupely-same-product-better-prices-activity-7425549211238150144-CwcT)
- [Build like someone will open the curtain](https://www.linkedin.com/posts/jacob-galajda_multiple-llms-have-reported-to-find-loopholes-activity-7506065880250769410-LhVB)
- [No/low-code as a backend thesis and its real-time limits](https://www.linkedin.com/posts/jacob-galajda_ive-talked-about-this-with-multiple-colleagues-activity-7500747330497785856-LLfk)

## Brand relationships

- **Dupely** (disclosed_partnership)
- **Amazon** (mention)

## Partnerships

Jacob Galajda's posts describe no sponsorship or brand-partnership format. The commercial relationships are their own product and client work, not paid creator placements. Dupely, the shopping comparison tool they work on, is described as earning through affiliate links rather than charging shoppers; their Dupely posts promote a beta waitlist and sign-up page, not sponsored placements or audience packages. A separate post describes data-ecosystem work delivered to unnamed clients — CRM, fulfillment, SOPs and audit frameworks, KPI and dashboard design — without naming those clients or describing the engagement terms. There is no published format list, rate card, deliverable specification, partnership contact route, or disclosed example of a sponsored collaboration. Before budgeting for a creator partnership, ask whether they accept sponsorships and, if so, what they offer and how results would be measured; confirm the arrangement in writing. Any data-ecosystem engagement would also need to be scoped directly and confirmed as open to new inquiries.

## Alternatives

- Zelle Jeremias Herbolario: For a campaign aimed at a B2B SaaS company preparing for acquisition and seeking a data-readiness and analytics collaboration, Zelle Jeremias Herbolario is the stronger choice only if the buyer is a services business or a vendor selling into agencies, and the problem is disconnected operations rather than diligence. Their two posts argue that agencies do not need another tool; they need the tools they already own to work together. The posts pinpoint the gap between systems: sales knows one thing, delivery another, the client hears updates from a third place, and the founder becomes the person every question returns to while the process lives in someone's memory. They offer a practical diagnostic: what does each tool own, where does information start and move, who owns the next step, and what still depends on someone remembering? The companion post adds a rule: every important piece of client information needs one home — CRM data in the CRM, delivery decisions captured where the team can use them, and SOPs that are easy to find. The fix, they argue, is to make the business less dependent on memory. For a collaboration aimed at agency owners or B2B services operators cleaning up an operations stack, this matches the buyer's vocabulary more closely than acquisition-readiness material.
- Aaron Pascua: For a campaign aimed at the same B2B SaaS buyer, Aaron Pascua is the closer choice when the buyer wants tooling-led implementation content with a visible path to purchase, rather than a practitioner argument about diligence. Their posts describe a rebuild after three relaunches, two of which failed, and the systems built along the way: a content tracker across platforms, a working CRM showing which deals are alive and which proposals are going cold, and personal planning databases. The cost of poor systems is concrete: at two clients, things are fine; at eight, deals go cold because nobody follows up, work gets done twice, and Sunday night goes to rebuilding context that existed on Wednesday. The second post gives a before-and-after a campaign can walk through: a two-to-five-minute search tax across Asana, Google Sheets, and Docs; a single source of truth; per-department dashboards for sales, marketing, ops, finance, legal, and executive reporting; then templates, automations, and an AI layer over the workspace. This suits a buyer who wants the collaboration to show what the solution looks like in a named platform and lead to a template or workspace offer.
- Katie Stone: For a campaign aimed at education or tutoring businesses, Katie Stone is the better choice. They also fit a collaboration seeking a firsthand account of putting AI into an existing human workflow — a narrower need than the acquisition brief, but one this material serves well. Their posts detail a report workflow: survey results downloaded from Airtable, anonymised, and given to Claude alongside earlier reports; percentages that came back correct because the tool runs code rather than predicting numbers; and the writer retaining the judgment calls, including selecting participant quotes and reading every word before it goes out. Time per report fell from thirty to forty minutes to about fifteen. The second post describes a client-facing audit method: a WhatsApp group where the client braindumps for a week before the audit, then a session to check whether the apparent problem is the real one, followed by a bespoke written audit rather than a template. This is teachable, inspectable material for a campaign about AI processes that keep a person in the loop.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/