Rafael Soares writes about data, artificial intelligence, and digital commerce, with a focus on how businesses can use technology to improve customer experiences and decision-making.
Rafael Soares is most useful for explaining how data quality and AI adoption affect business operations.
Best for
A Brazilian B2B data, governance or martech vendor helping enterprise buyers before an AI rollout.
Not for
A consumer brand or global product launch seeking reach, fixed deliverables and usage rights.
Key takeaways
AI projects stall when data is scattered across folders, servers, clouds, email and other sources.
The CIO's job includes selling AI to employees who fear being replaced.
AI assistants still need empathy and humour, and automated service needs good judgment.
Check first: Confirm whether their posts are explanatory or commercial offers before contacting them.
Main ideas
AI projects stall on scattered, unreliable data · The CIO’s real job is selling AI to employees · Ad overload drives buyers away, so companies should structure a CDP
Rafael Soares writes about how businesses apply data and artificial intelligence to digital commerce and customer experience.
Across their LinkedIn posts, they connect technology decisions to practical business problems: scattered data can stall AI projects, employees may fear AI will replace them, and automated customer service still needs empathy and good judgment.
They also comment on e-commerce competition and technology news.
Rafael Soares translates technology topics into operational and human questions.
In one post, data quality is not an abstract technical concern: they describe information scattered across folders, servers, cloud services, email, and other sources, alongside limited staff time and competing emergencies.
In another, they frame AI adoption as a communication challenge for CIOs, including employees’ concern about being replaced.
Their post about humor in AI assistants adds a customer-experience test: warmth must remain empathetic and useful.
They also turn industry news into process lessons, as in their commentary on the CrowdStrike outage and the importance of testing before deployment.
These examples make their work useful to readers interested in the business consequences of data, AI, and e-commerce—not just the technology itself.
The posts also include service invitations and product-category recommendations, so readers should distinguish explanatory points from commercial offers.
Rafael Soares writes about data, artificial intelligence and digital commerce as linked operational and human problems.
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A recurring claim is that AI depends on clean, organised data and employees who understand why change is happening.
They also argue that customer-facing AI still lacks empathy and humour, customer data platforms can reduce ad-driven abandonment, and teams skip implementation testing at their peril.
Their posts often pair third-party statistics or news events with an offer to discuss a project, making the ideas both educational and commercial.
AI projects stall on scattered, unreliable data
Soares argues that data quality is foundational to artificial intelligence, not a preliminary chore.
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Information is dispersed across folders, servers, separate clouds, printed documents, email and social media, making data and AI projects hard to assemble into something coherent.
Many companies, they add, lack an internal technology team, time or both, while other emergencies take priority.
The post ends with an offer for their team to take on that complexity.
The post names where data gets stuck — folders, servers, distinct clouds, printed documents, email and social media — making it difficult to reunite and structure data and AI projects.
Responding to a CIO article, Soares says AI and structured data are already changing business and that the shift is one-way.
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The hard part is cultural: employees have a legitimate fear of being replaced.
They recommend that CIOs explain, educate and train staff, showing how AI can make routines more efficient and possibly less stressful.
Companies, they argue, should clarify that the tools are not meant to replace human decisions but to improve competitiveness and results.
The post turns an article about the CIO’s new mission into a management argument about communication and training, naming employee fear as the obstacle.
Ad overload drives buyers away, so companies should structure a CDP
Soares cites an Accenture finding that 74% of consumers may abandon online purchases because of too many ads.
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They present a Customer Data Platform as the answer, saying it uses machine learning and AI to map consumer and competitor patterns, support decisions, automate and analyse campaign performance, and adjust strategies in real time.
The post explains the concept and ends with an invitation to discuss structuring a CDP.
The 74% figure comes from the cited research, not Soares’s own measurement.
The post opens with an Accenture statistic, defines what a CDP does and offers a conversation about implementing one.
Soares says companies are investing in making AI attendants and assistants feel more natural, but that something essential is missing: the connection created by empathy, sympathy and humour in service relationships.
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The design challenge is to keep humour empathetic and adaptable to whoever is asking while balancing it with helpfulness. The post presents this as an unresolved problem.
The post frames the challenge as making a chatbot funny without compromising its usefulness, while adapting its tone to the person asking.
Soares describes a CrowdStrike update that caused Microsoft blue-screen failures, cancelling or delaying about 28,000 flights worldwide and causing losses in other sectors.
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They argue that teams must run all planned tests and confirm every implementation stage before launching data, AI or e-commerce projects. The post also says it is worth following how the companies involved and affected relate to each other.
The CrowdStrike outage supplies the concrete example; the operational rule is to verify every step before implementation.
Commerce and AI are read through events, partners and entrants
Several posts are brief market observations rather than arguments.
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Soares announces they will attend VTEX Day with the Synerise team and names the stand’s partners: Dunnhumby on personalisation and loyalty, Infracommerce on applied intelligence at scale, and the VTEX Ads Platform on smarter recommendations, ads, search and interactions.
A separate post says Temu is entering Brazil with aggressive marketing, lower prices and local brand partnerships, intensifying competition with Mercado Livre, Americanas, Shopee, Shein and Amazon.
Another cites IDC data showing that the corporate applications market grew 12% in the previous year, linking demand to digital transformation and efficiency.
These posts show the commercial environment and market shifts Soares tracks; unlike the data-quality and CDP posts, they do not end with a service offer.
The Temu post lists incumbent marketplaces and describes Brazil’s e-commerce market as highly competitive and open to new entrants. The VTEX Day post names Synerise’s stand partners and their specialisms.
The shorter market posts sit alongside more developed arguments about data readiness and AI adoption. They show Soares tracking the commerce stack but offer no specific recommendation buyers can act on.
This is the premise behind much of Rafael Soares's content: AI projects succeed or fail based on the state of the underlying data.
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Start here to understand the problem they keep returning to.
Rafael Soares argues that data quality is a pillar of artificial intelligence projects and that companies need to invest in the reliability of the information they already hold.
The specific obstacle is dispersion: data sits in folders, servers, separate clouds, printed documents, email and social channels, making data and AI projects hard to assemble into something coherent.
The post also names practical blockers they see at client organisations — no internal technology team, no time and other issues taking priority as emergencies.
It closes with a direct offer to discuss how their team can take on that complexity.
For brands planning a campaign
Useful as an entry point for a governance, integration or data-platform vendor: the post offers a concrete pain story — scattered data and thin internal teams — that a collaboration could build on. Because it ends with a sales offer, any partnership would need to complement their service pitch rather than replace it.
Shows how Rafael Soares frames AI adoption as a culture and communication problem rather than a tooling problem — an angle a campaign about internal enablement would need to match.
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Responding to an article about the CIO's new mission, Rafael Soares says AI and structured data projects are already changing how business is done and that this is a one-way path.
They highlight two consequences: the CIO role gains prominence, and the cultural challenge is significant because employees have a legitimate fear of being replaced.
The post recommends that CIOs act as communicators, explaining, educating and training staff, and showing how these tools can make routines more efficient and possibly less stressful.
For companies, the post stresses that the tools should not replace human decisions but should aim to improve competitiveness and results.
For brands planning a campaign
A vendor selling AI enablement, training or change-management services could build content around this framing of internal resistance. It also shows the buyer role Rafael Soares speaks to — CIO-level, with awareness of both the technology case and the staff-relations case.
A compact example of a recurring pattern: take a consumer statistic, name the technology category that answers it, and connect it to a service offer.
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The post opens with a cited Accenture finding that 74% of consumers abandon online purchases because of too many ads.
Rafael Soares argues that structuring a Customer Data Platform lets a company stand out, improve its relationship with its audience and be more precise about where it invests in marketing, finance and production.
The post says a CDP uses machine learning and artificial intelligence to map consumer and competitor patterns and trends, automate and analyse campaign performance, and adjust strategies in real time.
It ends with an invitation to discuss structuring a CDP at the reader's company.
For brands planning a campaign
Directly usable by a CDP or customer-data vendor looking for a stat-led content hook with a clear product category attached. Note that the underlying claim is a third-party research figure quoted in the post, not the author's own measurement.
The most specific angle in the set: what conversational AI still cannot do, and why that matters to customer experience rather than model performance.
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Rafael Soares writes that companies are investing in making AI assistants feel more natural, but argues that something essential is still missing: a real feeling of connection through empathy, warmth and humour in service interactions.
The challenge, they say, is to make humour in virtual assistants empathetic and adaptable to whoever is asking, while balancing humour with helpfulness in chatbots.
The post treats this as an unresolved design problem and links to an article on the topic.
For brands planning a campaign
A good fit for conversational AI, chatbot or customer-experience vendors whose product claim is natural, human-feeling service. The diagnostic framing suits content that positions a specific capability as the answer.
Shows the commercial environment Rafael Soares operates in — the companies, events and partner names that appear alongside their commentary.
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This event post announces that Rafael Soares will be at VTEX Day with the Synerise team, described as one of the largest e-commerce events in the world.
The stated purpose is to meet partners and clients and demonstrate behavioural and predictive AI used to generate results and shape consumer experience and company-customer relationships.
The post names partners sharing the stand: Dunnhumby, on personalisation and loyalty through understanding who customers are; Infracommerce, on growth through applied intelligence at scale; and the VTEX Ads Platform, on using data and AI to make recommendations, ads, search and interactions smarter.
For brands planning a campaign
Useful for mapping Rafael Soares's commercial associations when assessing fit. The post names event-stand participants Synerise, Dunnhumby, Infracommerce and the VTEX Ads Platform.
A clear example of how Rafael Soares turns industry news into an operational argument — a move that characterises much of their commentary.
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Rafael Soares describes how an unsuccessful update from CrowdStrike caused the well-known blue screen across Microsoft systems, cancelling or delaying roughly 28,000 flights worldwide and causing losses in other sectors.
The lesson is to run all planned tests and confirm that every stage of an implementation process has been followed before launching projects, whether in data, artificial intelligence or e-commerce.
The post also suggests watching how the companies involved and affected relate to each other.
For brands planning a campaign
A ready-made hook for QA, deployment, risk-management or data-reliability vendors who want to connect their message to a widely recognised failure. The post supplies the news frame and testing argument; a collaboration could add a specific methodology.
In a post on data quality, Rafael Soares argues that AI success depends on reliable, well-organized data.
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They note that companies often have data scattered across folders, servers, clouds, printed documents, emails, and social media, making Data and AI projects hard to structure.
Many companies lack an internal technology team or the time.
Soares offers to discuss having their team manage that complexity and deliver the project, framing the payoff as faster decisions, competitiveness, efficiency, and better use of data and AI tools.
Uses a problem-solution frame: identifies disorganized data as the blocker, then offers a direct conversation as the next step.
For brands planning a campaign
A data platform, governance, or AI company could position Soares as a practitioner who explains the operational mess behind AI adoption and points to services that clean, integrate, and govern data. The post supports an advisory or content collaboration on data-readiness assessments or implementation roadmaps.
Soares shares a CIO article and argues that the CIO's mission now includes communicating about AI internally.
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They emphasize cultural resistance and employees' legitimate fear of being replaced, arguing that CIOs must explain, educate, and train staff, and show how AI improves routines and may make work less stressful. They also say companies should clarify that tools should not replace human decisions, but should aim to improve competitiveness and results.
Uses a news or article hook to build a management argument for communication and training.
For brands planning a campaign
For an AI adoption, change-management, or employee-experience platform, Soares can speak to the human side of deployment, not just model performance. A brand could use this angle for internal-communications content or a webinar on winning employee trust during AI rollouts.
Customer Data Platform as a response to ad fatigue
Citing a The Empowered Consumer/Accenture statistic that 74% of consumers may abandon online purchases because of too many ads, Soares argues that companies should structure a Customer Data Platform (CDP).
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They describe a CDP as using machine learning and AI to map consumer and competitor patterns, support decisions, automate and analyze campaign performance, and adjust strategies in real time. They offer to discuss structuring a CDP.
Uses a third-party statistic as the hook, defines the technology, and ends with a direct offer to discuss implementation.
Carries an explicit implementation offer alongside its educational content, so brands should account for its commercial orientation.
For brands planning a campaign
A CDP, marketing automation, or customer analytics vendor could work with Soares on content linking ad pressure to data infrastructure. The post offers a specific statistic and a practical remedy, making it suitable for a case-led campaign about personalization without overload.
Behavioral and predictive AI for e-commerce personalization and loyalty
Soares posted about attending VTEX Day with the Synerise team, describing the event as a chance to meet partners and clients and show results from behavioral and predictive AI.
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They highlighted partner contributions: Dunnhumby on personalization and loyalty, Infracommerce on scale execution, and VTEX Ads Platform on smarter recommendations, ads, search, and interactions.
Uses event participation and named partners to frame a collaborative, results-oriented view of AI in commerce.
For brands planning a campaign
A retail-tech, loyalty, or personalization brand could evaluate Soares for event-led content or partner-ecosystem storytelling. Their approach connects AI capability to customer experience and commercial results, making it a fit for a co-marketing or conference collaboration.
Marketplace competition and new entrants in Brazilian e-commerce
Soares explains that Temu is about to start operating in Brazil and is likely to intensify competition.
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They point to aggressive marketing, lower prices, and local brand partnerships as drivers of Temu's global growth. They list Mercado Livre, Americanas, Shopee, Shein, and Amazon as incumbent competitors, presenting e-commerce as highly competitive and open to new entrants.
Uses a single market event to illustrate a broader competitive and dynamic e-commerce landscape.
The post is observational rather than prescriptive; it does not include a direct service offer, so it works better as market commentary than as a campaign call to action.
For brands planning a campaign
An e-commerce platform, payments, or retail-media company could use Soares to connect market-entry news to the operating realities of marketplaces. The post suits a commentary or briefing on competitive shifts and what they mean for brands selling online.
Soares observes that companies are investing in more natural AI attendants and assistants.
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They note that AI efficiency is rising, but real connection through empathy, sympathy, and humor remains difficult. The challenge is to make communication empathetic and adaptable, balancing humor with helpfulness so the experience feels natural and efficient.
Frames AI assistants as both an efficiency and relationship challenge, using the tension between humor and helpfulness.
For brands planning a campaign
A customer-service AI, conversational platform, or CX brand could involve Soares in content about the limits of automation and the design of human-like interaction. This angle supports a nuanced discussion of assistant quality rather than a simple efficiency claim.
Testing and implementation discipline after the CrowdStrike outage
Soares describes the CrowdStrike update that caused Microsoft “blue screen” failures, canceling or delaying about 28,000 flights worldwide and causing losses in other sectors.
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They use the incident to stress testing and verifying every implementation step before launching data, AI, or e-commerce projects. A postscript says the relationship between the companies involved is worth following.
Uses a major failure as a cautionary example, turning it into a process lesson for data, AI, and e-commerce projects.
For brands planning a campaign
For an IT operations, cybersecurity, or data-platform vendor, Soares can connect global incidents to practical project governance. A collaboration could focus on release testing, risk management, and the business cost of skipping implementation checks.
Growth in corporate applications and demand for digital transformation
Soares cites IDC data showing that the corporate applications market grew 12% in the previous year.
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They link demand to companies’ digital transformation and search for technology that improves efficiency and productivity.
Leads with an analyst statistic and connects it to business needs.
For brands planning a campaign
A software, enterprise app, or digital transformation vendor could use Soares for market-trend commentary that ties category growth to buyer priorities. The short post works as a signal or conversation starter with CIOs and operations leaders.
Rafael Soares's posts show one commercial association: in a LinkedIn post, they appear with the Synerise team at VTEX Day and describe Synerise's behavioral and predictive AI technology as 'our' technology and its clients as 'our clients.' Ask Rafael to clarify whether the relationship is employment, ownership, or another commercial arrangement.
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Other posts discuss data quality, AI adoption, customer service, and e-commerce competition but mention no additional brand relationships.
Synerise
Disclosed partnership
In a LinkedIn post, Rafael Soares says they will be with the Synerise team at VTEX Day and describes Synerise's behavioral and predictive AI technology as 'our' technology and its clients as 'our clients.' Ask Rafael to clarify whether the relationship is employment, ownership, or another commercial arrangement.
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Original disclosure
Nesta semana estarei com o time da #Synerise no #VTEX Day.
Rafael Soares's posts are easiest to follow by theme, not date.
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The clearest arguments appear in longer posts on data quality as a precondition for AI and customer data platforms as an answer to ad overload.
A second group covers the human side of deployment: communicating AI to employees and the missing empathy and humour in AI assistants.
A third, shorter group tracks market shifts, including a VTEX Day presence with Synerise, Temu's Brazilian entry and IDC figures on corporate application growth.
The CrowdStrike outage post offers a practical cautionary tale about testing before launch.
Start with the data-quality post for the core argument, then the CIO and assistant-empathy posts for how they approach people, followed by the market notes for commercial context.
Amorim writes about choosing operational software to solve process problems rather than chase trends, and the cost of buying technology before fixing a process.
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That aligns with Soares's process-before-tool discipline in data and AI projects.
Amorim covers logistics and warehousing, including WMS, TMS, DMS and cross-docking; Soares covers data quality, AI adoption and customer-facing commerce technology.
Rafael Soares targets a sophisticated B2B audience interested in digital transformation, data governance, and the strategic use of AI and Martech in e-commerce and corporate operations.
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Their content addresses decision-makers, including CIOs, CTOs, and marketing leaders, navigating data quality, AI integration, and competitive market dynamics such as the rise of aggressive marketplaces like Temu. The discussions take an advisory, high-level view of organizational structure and technology adoption rather than consumer-facing marketing.
The posts do not document a partnership programme for Rafael Soares: they include no media kit, rate card, sponsored-content disclosure, or published deliverable and usage terms.
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They do show a working pattern around vendors, events, and direct business conversations.
In an April 2026 post, they described attending VTEX Day with the Synerise team, showing client results from behavioural and predictive AI, and naming Dunnhumby, Infracommerce, and the VTEX Ads Platform as partners at that stand.
Several other posts invite readers to talk directly about data quality, CDP setup, or AI adoption.
Those are business-development conversations, not sponsorship packages; the posts state no payment, exclusivity, or licensing conditions for any collaboration.
Brands should ask whether they accept sponsorships or paid work, and what a collaboration would include and cost.
Strong fit — a Brazilian B2B data, governance or martech vendor helping enterprise buyers get their data in order before an AI rollout.
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The brief: a data-platform, CDP, governance or AI-enablement company selling to Brazilian retail, e-commerce and enterprise accounts wants a content collaboration or advisory role.
The material should explain why AI projects stall on disorganised data, define the technology category that addresses the problem, and give CIO, CTO and marketing readers a concrete next step.
Why it fits.
Data quality is the theme Rafael Soares returns to most.
A post describes data scattered across folders, servers, separate clouds, printed documents, email and social channels, and identifies practical blockers at client organisations: no internal technology team, no time, and other issues treated as emergencies.
It gives a vendor a diagnostic to build a campaign around, rather than a generic AI claim.
A second post applies the same approach to customer data: it opens with an Accenture finding that 74% of consumers abandon online purchases because of too many ads, explains how a Customer Data Platform uses machine learning and AI, and closes with an offer to discuss setting one up.
The posts address the buyers a data vendor targets — CIO, CTO and marketing readers weighing governance and AI adoption.
Their Portuguese-language commentary and Brazilian market references point to local enterprise and e-commerce buyers.
The commercial pattern is repeatable: a news or research hook, a plain explanation of the technology category, then an invitation to talk.
Soares also has a visible event-and-partner presence: a VTEX Day post names Synerise, Dunnhumby, Infracommerce and the VTEX Ads Platform as sharing a stand, giving a vendor a natural route into conference content and partner storytelling.
When to choose a specialist
Poor fit — a consumer brand or global product-launch campaign seeking reach, fixed deliverables and usage rights.
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A campaign aimed at consumers or a non-Brazilian company may need sponsored posts timed to a launch, with a defined deliverable list, rate card, exclusivity and usage terms, reach targets, and a demographic audience profile.
A close variant is a vendor that requires documented client case studies and measured results before co-publishing.
Why it does not fit.
Rafael Soares's posts show no partnership programme: no media kit, rates, deliverable or licensing terms, or sponsored-content disclosure.
Commercial contact comes through direct conversation — two posts invite readers to discuss data quality or CDP work — a business-development move, not a package a brand can buy.
The posts are short first-person commentary, usually attached to a news item or article, rather than launch copy or product messaging.
A CrowdStrike outage read as a testing lesson or Temu's Brazil entry is commentary; a campaign needing a sales call to action would clash with that register.
No employer, job title or client list appears in the posts, and project work is presented as an offer to talk, not as case studies with outcomes.
An AI launch requiring benchmarks or named implementations would therefore have nothing to quote.
Audience measurement is limited to 5,480 LinkedIn followers on one account, with no breakdown by seniority, company size or country, so the published information is insufficient to size a reach-based plan.
The posts are in Brazilian Portuguese, which does not serve an English-language demand campaign, and use only one channel.
Ask for audience demographics, case studies and commercial terms if those are requirements.
A worked campaign example
The angle: "Clean the data before you promise the AI."
Where it comes from: the post that presents data quality as the foundation of AI work, adapted for a data-governance, integration or CDP vendor seeking a content collaboration or advisory role with Brazilian enterprise buyers.
How it runs, in three pieces, using formats Soares already uses: 1. Diagnostic piece. Start with the problem of information scattered across folders, servers, separate clouds, printed documents, email and social channels. Turn it into a short readiness check readers can run inside their own companies, naming the blockers plainly: no internal technology team, no time, and other issues taking priority as emergencies. 2. Stat-led explainer. Lead with the ad-fatigue figure — 74% of consumers abandoning online purchases because of too many ads, attributed to Accenture as cited in Soares's post, not presented as Soares' own measurement — and explain what a CDP does: maps patterns with machine learning and AI, analyses campaign performance, and adjusts strategy in real time. Keep the language plain and end with one clear next step. 3. Event or partner session. Follow the VTEX Day pattern: a stand or session where the vendor and Soares present to e-commerce clients and partners. Any partner co-presentation needs those partners' agreement; Soares's post shows only who shared a stand, not what was agreed.
The decision for the reader: whether their data is ready for an AI project, and which route to take first — a data-quality clean-up or a CDP build.
What the collaboration has to supply: results. Soares's posts describe capabilities and offer to talk, but publish no client outcomes. The vendor must supply and clear any before-and-after figures, customer examples or benchmarks.
What to settle directly before publication: payment, exclusivity, who holds the rights to the material, and where it runs. Soares's posts publish none of those terms. Treat the commercial arrangement as something to negotiate, not an existing package.
A better choice when the vendor's product serves logistics and supply-chain operations, and the buyer's task is getting operations leaders to fix processes before buying systems.
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The posts make that argument head-on: companies spend millions on technology before correcting their processes, and the choice between WMS, TMS and DMS should follow the operation's main pain.
They back it with a plain breakdown of what each system does, who it suits and which indicators to watch (OTIF, logistics cost per order, returns rate, vehicle occupancy).
A second post on cross-docking offers a repeatable format: definition, benefit list, KPIs and named users such as Amazon, Mercado Livre and Walmart.
The delivery is operational and structured, with an explicit warning that no system repairs a broken process on its own.
The posts do not address data quality, governance, customer data platforms or AI adoption, so a data or martech vendor would need to rebuild the campaign premise around logistics. Neither post features event, partner or commercial content, so the conference and co-marketing route seen elsewhere in the posts has no equivalent here.
A better choice when the vendor sells supplier quality, inspection or procurement software to industrial buyers, and the collaboration needs to argue that quality belongs in the sourcing decision, not in pre-audit paperwork.
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The post starts with a concrete buyer question: the supplier delivered on time and at the negotiated price, but the material arrived out of specification; was it still a good purchase?
It answers with a five-step sequence (qualify, specify, measure, handle deviations, develop) and notes that OTIF alone does not cover rejections, non-conformities, repeat failures, rework or corrective-action effectiveness.
That gives a quality-management or supplier-risk vendor a usable campaign spine, and the post closes by asking readers where quality sits in their own process.
The subject is procurement and manufacturing quality, not data or customer experience, so a data-platform vendor would need to rewrite the domain.
The two posts carry essentially the same text, offering one distinct argument rather than a range of content for a multi-piece campaign.
Neither features event, partner or commercial material.
A better choice when the need is to evaluate AI tools and research methods, and the vendor wants a practitioner to show how a tool performs rather than explain a category.
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The post compares OpenAI's Deep Research with Gemini firsthand on the same prompt, shows the full prompt, spells out the criteria (period respected, source quality, analytical depth, cross-report synthesis) and names the shortcomings: Gemini stalls at times and produces text that runs long.
It closes by arguing that the assumption ChatGPT is always the best option is worth revisiting.
A vendor selling a research or AI tool to marketing and UX teams gets a documented test and an honest verdict, which is harder to commission than a category explainer.
The posts do not cover data quality, governance, customer data platforms or enterprise data readiness, so the vendor's original premise does not carry over.
Content cadence is difficult to assess: one detailed tool test and one team-announcement post appeared roughly nineteen months apart.
Ask for a larger sample before relying on a planned content series.
Based on eight public LinkedIn posts published between 2024 and 2026 and read in full.
They cover data quality as a precondition for AI work, the CIO's role in communicating AI to employees, customer data platforms as a response to ad overload, empathy and humour in AI assistants, a VTEX Day appearance with the Synerise team, Temu's entry into Brazil, corporate application market growth, and the CrowdStrike outage read as a lesson in testing before deployment.
The posts are short first-person commentary, usually attached to a news item or article, and several end with an invitation to talk about a project.
No long-form articles, interviews or transcripts were available, and the posts state no employer, job title, client list or published results.
The page treats explanatory argument and commercial offers as separate things.