Jacob Galajda

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.

In the post, Jacob Galajda contrasts e-commerce data, where prices change constantly, with CRM data that can be sorted by last edit time. On AI projects, they say, they inspect the data before discussing how many tokens the system will use each month.

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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.

Jacob Galajda says the deal closed on enough of the story, but not the full one. Cohort-level retention, support volume, and pipeline velocity by segment were incomplete or missing when an acquirer needed them.

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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.

Jacob Galajda says the client's dashboards supported personnel training, decision making, and investor meetings. They close by asking which metric is least enjoyable to track, underscoring the need to choose measures deliberately.

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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.

Jacob Galajda cites Dupely's use of third-party measurement and its distinction between guests and sign-ups as ways to make flubbing numbers difficult, tying transparency directly to how the product was built.

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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.

Jacob Galajda describes a lead-generation project where an elegant bulk ingestion solution ran into repeated 429 errors and stalled the rest of the pipeline—a concrete counterexample to the no-code-as-backend thesis.

Jacob Galajda says they are biased toward the cloud-integrated language thesis, but their own operational experience shows that day has not arrived.

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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 a random sample, Jacob Galajda says Dupely won by a dollar against Alexa for shopping. They attribute the difference to data ownership and a search function designed for duplication detection rather than general product search.

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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.

Jacob Galajda asks whether someone would wait five minutes to save ten dollars at checkout, notes that some proposed backend solutions took more than thirty minutes before failing, and connects that failure mode to the trust shoppers and platforms place in the product.

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Jacob Galajda describes helping clean up data for an M&A deal.

• Records were scattered across a CRM, spreadsheets, files from unused tools, and locally saved reports.
• The bigger problem was that the existing data could not answer the acquirer's questions.
• Growth-era metrics, renamed or abandoned fields, and missing cohort-level retention, support volume, and pipeline velocity by segment left an incomplete story.
• They say the deal still closed, but not on the full picture.
• Two habits could have changed the outcome: collect accurate data consistently, even when it feels nonurgent, and understand the historical impact before changing CRM fields.

For brands planning a campaign

Use this as the agenda for an acquisition-readiness audit: identify which buyer questions the current data model cannot answer, then trace field changes and missing historical categories before diligence starts.

Jacob Galajda says the team helped a client refine its data ecosystem, and the dashboards supported personnel training, decision-making, and investor meetings.

• For a company starting from scratch, they would prioritize a CRM with clean data and automations that reduce manual entry; a fulfillment system with tracking or export capability that staff use consistently; SOPs and frequent audits to keep data clean; and KPIs based on industry-standard formulas to avoid misleading investors or government audiences.
• Dashboards, automations, and AI all depend on the underlying data, so bad data corrupts anything it touches.

For brands planning a campaign

Turn this into a data-readiness checklist: review CRM hygiene, fulfillment tracking consistency, audit cadence, and KPI definitions before adding an AI or analytics layer.

Jacob Galajda argues that bad data means more than poor data quality.

• It can include undefined or unclear relationships, weak mappings, or a hosting system with restrictive API rate limits.
• Future-proofing depends on the intended use: frequently updated e-commerce prices call for a different strategy than a CRM with fewer records and API filtering by last edit.
• Feeding old, out-of-spec, or out-of-scope data into an AI layer degrades the output.
• On large AI projects, they look at the data before discussing token spend.

For brands planning a campaign

Separate data quality, relationship mapping, and access performance in any analytics or AI proposal; API rate limits and update patterns can determine whether a use case is viable.

Jacob Galajda compares Alexa for shopping with Dupely and says Dupely won by a dollar in a random sample.

• The key difference: Alexa owns its data while Dupely licenses it, and Alexa's search is not built for duplication detection.
• Dupely analyzes products across listings to identify duplicates on Amazon and eliminate manual inspection.
• The business model relies on affiliate links, with no consumer payment.
• They say the solution is deployed in-house, not as a wrapper around GPT or a swarm of AI agents, and includes fallbacks, security, uptime monitoring, and purpose-built request handling.

For brands planning a campaign

Useful for explaining a narrow product wedge against a platform giant: define the job to be done, the data advantage or constraint, and the revenue model together.

Jacob Galajda asks whether someone would wait five minutes to save ten dollars at checkout.

• Some proposed backend solutions took more than thirty minutes before failing.
• Over a couple of months, Dupely moved from in-house computing to ALB-provisioned resources, then to a dedicated cloud SaaS provider for some elements.
• The goals were to improve user speed, cut operating costs, and improve recommendation accuracy without passing costs on to users.
• They describe this as building a trust layer for shopping: eCommerce platforms need to trust that Dupely reduces churn and decision fatigue, while users need to trust that the service works whenever they need it.

For brands planning a campaign

For a B2B SaaS diligence brief, this is a concrete example of connecting infrastructure choices to unit economics, user trust, and two-sided marketplace adoption.

Jacob Galajda lists reported problems in technology: LLMs finding loopholes in benchmark grading, alleged plagiarism in math, founders flubbing user counts, and milestone definitions watered down every six months.

• They say Dupely was built to make it hard to flub numbers.
• It uses third-party platforms to measure engagement and fulfillment actualization, event handling that matches third-party-generated reports, and Cognito tables to distinguish guest users from sign-ups.
• Their principle: build as if someone will ask you to open the curtain, because someone will.

For brands planning a campaign

Use this as a metric-integrity test during due diligence: ask what third parties verify, how guests are separated from real accounts, and whether reported engagement matches independent sources.

Jacob Galajda describes a thesis that next-generation programming languages would integrate directly into cloud hosting systems, with Notion, Airtable, or CRMs serving as backends through APIs, CRUD, and search.

• They say that day has not arrived.
• On a lead-generation project, a bulk data ingestion solution against an unnamed CRM repeatedly hit 429 errors and broke the pipeline.
• Notion also either hit 429s or took too long to fetch internal operations data.
• Google Sheets and Airtable are useful, but not capable of real-time performance.
• No/low-code works well for prototypes, but software developers will still power real-time, cost-optimized cloud infrastructure.

For brands planning a campaign

For a build-versus-buy or prototype-versus-production decision, this highlights API rate limits, fetch latency, and the boundary between no-code speed and production reliability.

The harder problem, they say, was that historical data no longer answered the acquirer's questions: growth-era metrics, renamed fields, and retired tracking left gaps in retention, support volume, and pipeline velocity. They recommend collecting accurate data consistently and not changing CRM fields without understanding the effect on historical continuity.

For brands planning a campaign

For a B2B SaaS company preparing for acquisition, Jacob is a credible voice for a data readiness diagnostic. A campaign could frame them around historical data continuity, CRM field governance, and pre-diligence analytics audits rather than generic data cleanup.

Examples: Source 1

They treat dashboards, automation, and AI as downstream of data quality: bad data corrupts anything it touches, even with a paper trail.

For brands planning a campaign

Analytics, CRM, or operations platforms can position Jacob around a plain checklist for data ecosystem priorities. A useful campaign angle is a practical assessment of whether CRM, fulfillment, SOPs, and KPIs are ready for reporting and investor conversations.

Examples: Source 1

It can also mean undefined relationships, unclear mappings, or a hosting system with restrictive API rate limits.

• They note that e-commerce data with frequently changing prices needs a different strategy from CRM data with fewer records and last-edit sorting.
• Before large AI projects, they inspect the data rather than starting with token budget.

For brands planning a campaign

A data quality, observability, or AI infrastructure brand can use Jacob's framing for a pre-AI data audit campaign. The angle is concrete: check relationships, mappings, and API constraints before promising an AI layer.

Examples: Source 1

They say Dupely uses third-party platforms to measure engagement and fulfillment actualization, with event handling and Cognito tables to distinguish guest users from sign-ups. Their stated principle is to build as if someone will ask you to open the curtain.

For brands planning a campaign

Transparency, measurement, or investor-readiness tools can build a campaign around metrics that withstand diligence. Jacob's posts support an angle about third-party verification and separating real usage from vanity numbers.

Examples: Source 1

In practice, they report rate limits, slow fetches, and a lead-generation ingestion pipeline that broke under repeated 429 errors. Their conclusion is that no-code is useful for prototypes, while real-time, cost-optimized cloud infrastructure still depends on software developers.

For brands planning a campaign

Developer infrastructure, API, or cloud platform brands can use this as a grounded counterpoint to no-code hype. A campaign angle could evaluate when a prototype tool becomes an operational bottleneck and where engineering ownership still matters.

Examples: Source 1

They say it makes money through affiliate links, licenses data rather than owning it, and purpose-built its backend for large request volumes, with vendor fallbacks, security, and uptime monitoring. A separate post explains work on speed, running costs, and recommendation accuracy so shoppers do not bear the cost.

For brands planning a campaign

E-commerce analytics, cloud infrastructure, or security brands can work with Jacob on a trust-layer narrative: speed, accuracy, and reliability as prerequisites for shopper and platform trust. The angle should stay specific to duplicate detection and operational readiness.

Examples: Source 1 · Source 2

Other product posts use similar light humor, including a note about s'mores after a failed backend test and a playful line about invoicing a cookout as marketing spend.

For brands planning a campaign

For a launch or awareness campaign, Jacob's format shows Jacob can pair short humor with a clear product call to action. This is a format observation, not evidence of deep technical analysis in the launch post itself.

Examples: Source 1

The posts do not state a title, ownership stake or employment arrangement, so the exact role is unstated; the relationship is clearly commercial and ongoing across several posts. Galajda describes the business model directly: Dupely earns affiliate commissions from retailers and is not paid by consumers, and frames faster search and duplicate detection as the product's trust layer for shoppers and eCommerce platforms alike.

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They note that Amazon owns its shopping data while Dupely licenses data, and that Alexa’s search is not designed for duplicate detection. Galajda calls the sample random and says more comparisons are planned.

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Both treat the gap between systems—not another tool—as the real issue.

Zelle's examples stay within agency operations and founder dependency, while Jacob Galajda applies a similar lens to M&A data diligence, AI data inspection, and a shopping product's duplicate-detection infrastructure.

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Their concern with unclear ownership, duplicated work, and rebuilding context aligns with Jacob Galajda's emphasis on clean data, documented processes, and systems that hold up as a business grows.

Aaron's material centers on Notion templates and operations centralization for small teams; Jacob Galajda focuses more on data readiness for acquisition, AI data constraints, and the backend of a shopping technology product.

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Both she and Jacob Galajda focus on where automation helps and where human judgment still belongs.

Katie's context is education, safeguarding training, and tutoring agency audits; Jacob Galajda's is M&A data readiness, CRM governance, and e-commerce duplicate detection. Her posts also show a more explicit audit-and-service process than Jacob's product and data-infrastructure posts.

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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.

Jacob Galajda's posts focus on operations ownership and handoffs, not acquisition readiness.

• They do not address renamed or retired CRM fields breaking historical continuity, metrics an acquirer asks for, measurement integrity, or third-party verification.
• Nor do they cover data quality issues such as undefined relationships, unclear mappings, or hosting systems with restrictive API limits.
• Buyers specifically seeking M&A data-readiness content should ask for examples that address those issues.
• The comparison rests on two posts; ask for audience figures, roles, and commercial formats before assessing reach or scoping a deliverable.

Examples: Source 1 · Source 2

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.

The posts focus on platform-specific, sales-oriented implementation, not whether historical records can answer an acquirer's questions.

• They do not address KPI definitions using standard formulas, cohort-level retention, pipeline velocity, or measurement integrity.
• A B2B SaaS buyer preparing for diligence would get implementation framing, not the failure modes that matter in a deal.
• The posts also promote a template shop, a relaunch sale, and a custom workspace service; reconcile those offers in writing before budgeting for a vendor collaboration.
• The comparison rests on two posts.
• Ask for audience figures before assessing a paid placement.

Examples: Source 1 · Source 2

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.

The sector is schools and tuition agencies, not B2B SaaS preparing for acquisition.

• Jacob Galajda's posts do not offer diligence material, a CRM field-history argument, metric-definition guidance, or data-governance strategy at the scale a software buyer needs.
• The AI example is a document-production workflow, not an analytics or data-readiness approach.
• A campaign using this material would need to target education businesses or accept that the buyer's central question goes unanswered.
• The comparison rests on two posts; ask for audience composition, reach, and commercial-format information before assessing fit.

Examples: Source 1 · Source 2