The typical B2B commerce audit goes something like this: an agency spends two weeks reviewing your site, produces a 40-slide PDF with technical findings, hands it over in a 90-minute call, and disappears. Six months later, nothing has changed — because the report identified symptoms, not causes. The issues list sat in someone’s inbox while the business continued to underperform in exactly the same ways.
This is not a rare outcome. It’s the standard outcome. And it happens not because the auditors were incompetent, but because most B2B commerce audits are scoped, structured, and delivered in ways that almost guarantee they won’t create change. The numbers bear this out:
- Nearly 80% of B2B website redesigns fail to improve conversion or pipeline — because they change the visual surface while leaving structural and messaging problems intact.
- Roughly 70% of B2B distributors describe their ecommerce initiatives as unsuccessful.
- Close to 50% of manufacturers say their ecommerce investments haven’t delivered the results expected.
- About 70% of digital transformation projects miss their objectives entirely.
If you’ve commissioned an audit before and watched the findings gather dust, you already know what this feels like. At DotcomWeavers, we hear this story often — usually from a client who’s brought us in after a previous audit produced a report instead of a result. Here’s why that happens, and what we do differently.
Table of Contents
Reason 1: They Review the Storefront. They Don’t Look at What’s Behind It.
Most B2B commerce audits assess what a visitor sees: page speed, UX friction, navigation structure, mobile responsiveness, and SEO tags. These things matter. But in B2B commerce, the storefront is a presentation layer on top of a much more complex operating model — ERP, PIM, OMS, WMS, CRM, and the integrations connecting all of them. An audit that doesn’t examine those layers is like a building survey that only checks the paint.
The surface-level audit can’t tell you why pricing is wrong for certain accounts, why inventory shows incorrectly, why checkout fails for specific customer groups, or why orders don’t post cleanly to the ERP. In B2B, many failures that look like platform defects are actually integration or data ownership defects expressed through the platform. The storefront isn’t broken — it’s showing you what’s broken elsewhere.
Manual workarounds make this worse. A process can appear to function perfectly well because staff quietly intervene behind the scenes — patching pricing, re-keying orders, correcting inventory by hand. That makes the issue invisible to a surface audit, but it doesn’t make the process stable. Forrester has found that 66% of B2B firms have poor product or customer data, and over 40% struggle with siloed systems — problems that frequently go undetected because the audit never looked past the storefront.
This is the gap that DotcomWeavers built its B2B Commerce Auditsprocess to close. ERP assessment is always our first step, not an afterthought. We ask for API documentation, order flow diagrams, and time with the ERP administrator in week one — not week four. We map the full estate: how data moves from the ERP to the storefront, how pricing is calculated, where inventory sync breaks down, and how orders return to the ERP after placement. And we treat manual workarounds as diagnostic signals — every workaround represents a system responsibility nobody has clearly owned.
For a deeper walkthrough of what full-scope coverage should include, see our B2B eCommerce Audit Checklist.
Reason 2: They Assess the Platform. They Never Look at the Data Running Through It.
A B2B commerce platform is only as good as the data running through it. A beautifully configured storefront with inconsistent product data, duplicate customer records, and pricing that doesn’t match the ERP will underperform no matter how good the UX is. Yet data quality assessment is consistently the most skipped element of B2B commerce audits — it’s unglamorous, time-consuming, and requires business knowledge most audit teams don’t bring.
On the product side, missing attributes, inconsistent naming, incomplete cross-references, and unsynchronized categories are invisible to a technical audit but devastating to search, navigation, and AI-powered discovery. On the pricing side, mismatches between ERP and storefront pricing for specific customer accounts are among the most common causes of B2B checkout abandonment — and they’re almost never caught in a standard audit. Customer and account data issues — duplicate accounts, missing hierarchy, incorrect permissions, outdated credit terms — shape the buyer experience in ways that never show up in analytics.
The compound effect is what makes this dangerous: dirty data makes every downstream system worse. AI personalization fails on bad customer data. Search fails on incomplete product data. Forecasting fails on inconsistent order data.
It’s also where a lot of agencies quietly stop. DotcomWeavers doesn’t treat data quality as an optional scope — we review product data for completeness, consistency, and AI-readiness, asking whether your catalog can actually be surfaced by the AI-powered search tools your buyers are increasingly using. We compare ERP pricing configuration against what the storefront displays across a sample of customer account types, flagging mismatches with root cause, not just the symptom. We review customer and account data against how your sales team actually structures accounts in the ERP. And we treat data quality findings as business risk, prioritized by revenue impact — not as a footnote of technical debt.
Reason 3: They Hand You a 40-Slide PDF and Walk Away.
The most common complaint we hear from businesses that have had a previous audit isn’t “the findings were wrong.” It’s “we got a long list of issues and no clear idea of what to do first, who should do it, or what it would cost.” A findings list without prioritization, ownership, and sequencing isn’t a deliverable — it’s a to-do list nobody asked for.
The standard output is a report with 20 to 50 issues ranked by severity — high, medium, low — with no business context, no sequencing logic, no owner, and no estimate. IT and ops teams receive it with no clear first step, and because the highest-severity issues often require significant budget, the whole report gets deprioritized. The severity labels themselves can be misleading: a misconfigured caching layer and a broken ERP pricing integration might both register as “high severity,” but they carry completely different business impact, resourcing needs, and urgency.
There’s also a structural incentive problem worth naming. Some audit formats are built to make the findings feel overwhelming rather than actionable — because an overwhelming list is easier to convert into a rebuild proposal or a long retainer than a focused roadmap is.
It’s a trap. DotcomWeavers built its delivery model specifically to avoid. Every finding from our audits is tied to a specific business impact — revenue leakage, customer experience degradation, operational friction, or security and compliance risk — not just a technical severity rating. Findings are sequenced into a 30/60/90-day roadmap: what to fix first because it’s high-impact and low-effort, what to plan for next, and what requires longer-term investment.
Each item gets a suggested owner — platform team, ERP admin, marketing ops, or DotcomWeavers — so nothing sits in an ambiguous “someone should handle this” state. We separate quick wins your team can act on this week from structural remediation that needs planned investment, and we don’t pad the findings to justify the fee or manufacture future work.
Reason 4: They Audit Your Platform Through the Lens of the Platform They Sell.
Most B2B commerce agencies are certified on one or two platforms. Their audits — consciously or not — frame findings through that platform’s capabilities. An Adobe Commerce agency auditing a BigCommerce site will find limitations that Adobe Commerce happens to solve. A Shopify agency auditing an Adobe Commerce build will find complexity that Shopify happens to simplify. Neither agency is lying. But neither is giving a platform-agnostic diagnosis either.
This shows up as a pattern: audits from single-platform agencies disproportionately conclude that the client’s current platform is the problem, and that a rebuild on the agency’s preferred platform is the fix. Sometimes that conclusion is correct — sometimes the current platform genuinely doesn’t fit the business. But it should come from evidence, not from the auditing agency’s commercial relationships. Getting this wrong is expensive: choosing the wrong platform based on a biased audit can turn a 6-month implementation into a 14-month one, which means 8 months of lost revenue and internal credibility. Skills mismatches alone can increase implementation cost by 20–40% through contractor premiums or hiring delays.
This is one of the few structural advantages DotcomWeavers brings to an audit by default rather than by promise: we’re certified on Adobe Commerce, BigCommerce, and Shopify Plus, so we have no financial incentive to steer you toward any one of them.
When a platform recommendation is warranted, it comes from a structured assessment of business requirements, ERP integration complexity, team skills, catalog depth, B2B feature needs, and 3-year TCO. We’re equally direct when a platform change isn’t the answer — many findings are solvable on your current platform without a rebuild. And when a change is warranted, we lay out the full evidence base, including the cost and risk of the transition, not just what the new platform does better.
Reason 5: Promises & Talks, But The Business Never Comes to the Table.
A B2B commerce platform isn’t an IT asset. It’s a business operating system — it touches sales, marketing, operations, finance, and customer success. Yet most commerce audits are scoped and run as IT projects, with IT as the primary stakeholder and business teams consulted briefly, if at all. The result is a technically accurate assessment of a platform that doesn’t reflect how the business actually runs or what buyers actually need.
An IT-only audit can tell you a module is causing database queries to spike. It can’t tell you why 30% of large orders are being abandoned at checkout. Sales knows which customer segments are calling the sales desk instead of ordering online, and why. Operations knows which orders are failing silently and being corrected by hand. Marketing knows which campaigns are sending traffic to pages that can’t convert. And the ERP administrator often holds the key to constraints that were baked in years ago through configuration decisions nobody has connected to today’s storefront problems.
When sales want one thing, ops want another, marketing wants a third, and none of them were part of the audit, the findings won’t have organizational buy-in — which is exactly why they end up ignored. Involving the right stakeholders in identifying the problems is what gets them invested in solving the problems, and it’s a step DotcomWeavers builds into the engagement structure rather than treating it as optional.
Every DotcomWeavers audit requires participation from IT, operations, sales, and marketing — and from the ERP administrator specifically. We run structured discovery sessions with each group to surface what doesn’t show up in analytics: broken workflows, manual workarounds, recurring customer complaints. We connect technical findings to business context — “the ERP sync delay on branch inventory causes 12% of contractor orders to show incorrect availability” is a far more actionable finding than “inventory sync latency is high.” And we involve client stakeholders directly in prioritization, so the roadmap reflects what the business needs fixed, not just what the audit team found technically interesting.
People Talk system, but they don’t see comprehensive pictures with data & AI
Here’s What the Difference Actually Looks Like
At a glance, most B2B commerce audits look similar — a scoping call, some access, a findings presentation. The real differences are in scope, method, and what you’re left with.
| Dimension | Typical B2B Commerce Audit | DotcomWeavers Audit |
| Scope | Technical and UX surface only | Technical + integration + commercial + data + ERP layer |
| ERP Integration | Rarely included or cursory | Always first — ERP defines the risk profile of the entire platform |
| Data quality review | Usually skipped | Product, pricing, and customer data assessed against business reality |
| Deliverable | Issues list or PDF report | Prioritized action plan tied to revenue and operational impact |
| Next step | Often, another engagement or rebuild proposal | Clear 30/60/90-day roadmap that the client team can action |
| Platform bias | Often shaped by agency certifications | Platform-agnostic — certified on Adobe Commerce, BigCommerce, Shopify Plus & Magento Open Source |
| Who participates | Agency team plus maybe one client contact | Agency team plus IT, Operations, Sales, and ERP admin from the client side |
| How findings are framed | Technical problems | Business problems expressed through technical symptoms |
| Not focused on Business KPIs | Rarely tied to revenue, retention, or operational targets | Every finding is mapped to a measurable business outcome — conversion, order accuracy, revenue leakage, or cost reduction |
Before You Hire Anyone for a B2B Commerce Audit, Ask These 5 Questions
- Will you assess our ERP integration or just the storefront?
- What does the deliverable look like? Can I see an example?
- Are you certified on multiple platforms, or primarily one?
- Who from our team do you need access to?
- What happens after the audit?
A Good Audit Doesn’t Just Tell You What’s Wrong. It Changes What You Do Next.
An audit is only valuable if it produces change — and most don’t, because of how they’re scoped, structured, and delivered. A good one shouldn’t read like a forensic report on what’s broken. It should function as a strategic diagnosis that gives the business a clear, sequenced, commercially grounded path forward.
This is the standard DotcomWeavers has held itself to since 2007, across hundreds of engagements with B2B manufacturers, distributors, and wholesalers. Our audits are run by people who’ve actually implemented and supported the systems they’re evaluating — certified across Adobe Commerce, BigCommerce, and Shopify Plus, with deep, hands-on ERP expertise spanning Epicor, NetSuite, and Infor. That combination is what lets us start with the ERP instead of the storefront, treat data quality as core scope rather than an add-on, and hand you a roadmap instead of a PDF. It’s also why our recommendations aren’t shaped by which platform we’d rather build on — we get paid the same either way.
Not every business needs DotcomWeavers for their audit. But every business deserves an audit that actually produces outcomes. The five questions above will help you find the right partner — whether that’s us or someone else. If you’d like to put them to us directly, that conversation costs nothing to start.
The best B2B commerce audit you’ll ever have shouldn’t feel like a report. It should feel like a turning point.
Ready to see what a different kind of audit looks like?
Start with our B2B eCommerce Audit Checklist to understand exactly what full-scope coverage should look like before you commission anything. If you want to see how DotcomWeavers approaches B2B commerce end-to-end, from ERP integrations to platform builds, explore our B2B Commerce Services. And if you’re ready to have an honest conversation about where your platform is underperforming and what an audit would actually uncover, get a free assessment conversation with DotcomWeavers. No commitment, no pitch deck, just a straight answer on whether an audit makes sense for you and what it would cover.
For operations leaders
The outcome is measurable: fewer inbound calls to inside sales asking “do you carry this?” or “what’s the part number for X?” Every buyer who self-serves through AI search is a call that didn’t happen. In large distribution operations, the cost reduction from reduced inbound inquiry volume is substantial and compounds directly with catalog growth.
The discovery challenge
As buyers increasingly research via ChatGPT and other AI tools before ever reaching a supplier’s storefront, B2B sellers need product data and content that is indexable and surfaceable by those external AI systems — not just optimized for Google. Distributors with un-enriched, inconsistently structured product data don’t just rank lower in traditional search — they become invisible in AI-mediated discovery entirely.
83% of B2B sellers now prioritize AI when selecting search tools. 45% of B2B organizations plan to implement AI-powered visual search in 2026. — Algolia / Salesforce, 2026
Trend 4: Dynamic Pricing and Sales Intelligence — Static Price Lists Are a Competitive Liability
B2B pricing has always been complex — customer-specific tiers, contract pricing, volume breaks, seasonal demand fluctuations, and increasingly, tariff exposure that shifts quarterly. What’s new in 2026 is that AI can now manage that complexity dynamically: adjusting margins in response to real-time demand signals, automatically generating accurate quotes in minutes rather than hours, and giving sales representatives the per-account intelligence they need to have materially better conversations.
On pricing
AI-powered dynamic pricing engines adjust pricing by customer tier, demand signals, competitor data, and margin targets in real time — not at the next quarterly pricing review. For distributors navigating the tariff complexity introduced by post-2025 trade policy changes, AI that can automatically factor in tariff exposure and adjust margins accordingly has moved from a competitive advantage to an operational necessity. Companies still managing this via quarterly spreadsheet updates are making pricing decisions on data that’s already stale.
On quoting
AI generates accurate, margin-optimized quotes in minutes by pulling live inventory availability, customer-specific pricing rules, and lead time data directly from the ERP. Sales reps stop building quotes manually; AI handles the calculation and the customer gets a faster answer. The accuracy improvement alone changes close rates — buyers who receive a quote with an error lose confidence in the supplier’s operational competence.
On sales intelligence
AI rep assistants surface next-best-action recommendations, reorder windows, cross-sell opportunities, and meeting prep — all per customer, per rep. Institutional knowledge that previously lived in the heads of senior account managers becomes a scalable system available to every rep from day one.
The framing that captures the competitive urgency: your competitor’s AI already knows their margin floor and their customer’s reorder window. The question is whether yours does.
AI sales forecasting achieves 79% accuracy vs. 51% with traditional methods. High-performing sales teams using AI are 10.5x more likely to see major improvements in forecast accuracy. 60% of B2B organizations now use data-driven selling. — Futurism / Salesforce, 2026
Trend 5: AI Is Automating the B2B Operations Layer
While much of the AI conversation focuses on the customer-facing layer — search, personalization, pricing — the highest-volume AI adoption in 2026 is happening in the back office. Order processing, demand forecasting, inventory management, and supplier communications are being transformed by AI that eliminates the manual work that has long defined distribution operations. The result isn’t just cost reduction — it’s a structural operational advantage that compounds over time.
Order automation
AI reads and processes orders arriving from emails, PDFs, and EDI files automatically — extracting the relevant data, validating it against ERP records, and routing it without a human touching a keyboard. This capability now has approximately 60% adoption in distribution, making it the single most widely deployed AI use case in the sector. The impact: inside sales teams stop re-keying orders from PDFs and start spending their time on relationships and exceptions.
Demand forecasting
AI reduces forecasting errors by up to 50% by analyzing sales velocity, seasonality, external demand signals, and supplier lead times simultaneously. That accuracy translates directly into lower inventory carrying costs, fewer emergency purchase orders, and reduced stockouts on high-velocity items.
Inventory and warehouse operations
AI-powered slotting recommendations, pick path optimization, and automated reorder triggers are reducing the labor cost per order fulfilled and improving throughput without additional headcount.
Supplier communication and customer service
Workflow automation tools handle purchase order generation, delivery confirmation, and exception management without human intervention for standard scenarios. On the customer service side, 60% of B2B companies now use AI-powered solutions for handling order status inquiries, return requests, reorder confirmation, and invoice queries — reducing inbound support volume without requiring headcount increases to match revenue growth.
DotcomWeavers uses n8n and Make for B2B workflow automation — building order intake pipelines, supplier communication flows, and customer service automation that connect directly to your ERP. This is one of the highest-ROI AI investments available to distributors today.
Trend 6: The Data Foundation Is the Real Competitive Moat
There is a pattern behind every AI trend covered in this post: they all require the same foundation.
AI personalization needs clean transaction history and customer context. Agentic commerce needs structured product data and real-time pricing APIs. Demand forecasting needs unified, consistent sales and inventory data. Order automation needs reliable data flowing between ERP and storefront. The companies winning with AI in 2026 aren’t just deploying better tools — they’re rebuilding the data infrastructure those tools run on.
Why data readiness is the bottleneck
30% of generative AI projects are expected to be abandoned after pilot stages — and the primary cause is data quality issues, not AI capability limitations. Only 19% of executives report meaningful revenue gains from AI investment. The gap between companies that deploy AI and companies that capture value from it is almost universally a data readiness problem.
What clean data infrastructure actually looks like
- A unified data layer connecting ERP, ecommerce platform, and CRM with consistent, real-time synchronization
- An enriched product catalog managed through a PIM system, with complete attributes, structured specifications, and consistent taxonomy
- Clean, structured customer and order history that AI models can actually learn from
- Real-time inventory availability that’s accessible via API — not updated nightly via batch file
The architecture connection
AI-native platforms built on API-first, headless architecture give AI models the real-time data access they need. Monolithic legacy platforms — including systems still running on SAP ECC — don’t provide the data accessibility that AI requires. This is one reason 60% of B2B commerce organizations are expected to have adopted composable architecture by 2026, up from 25% in 2023. Platform modernization and AI readiness are now the same investment.
45% of CIOs are actively shifting budget toward AI, often by reducing spending on fragmented point solutions. 54% are consolidating vendors toward AI-enabled integrated platforms. The organizations making these moves aren’t just cutting costs — they’re eliminating the data fragmentation that prevents AI from working.
Trend 7: Smart Catalog — Making Your Products Findable in an AI-First World
Most B2B distributors have a product data problem they haven’t fully reckoned with yet. Attributes are missing. Descriptions are inconsistent. Taxonomy varies across categories. None of this was catastrophic when buyers were navigating category trees or running keyword searches. It becomes a serious competitive liability when AI is doing the finding.
Smart Catalog is the capability that closes this gap. It uses AI to automatically enrich product records — filling in missing specifications, standardizing attributes, adding synonyms and related terms, and structuring data in a format that both on-site search and external AI tools like ChatGPT can interpret accurately. The result is a catalog that doesn’t just work better on your storefront — it surfaces correctly when buyers research via AI tools before they ever reach your site.
For distributors managing tens of thousands of SKUs, the manual alternative isn’t realistic. A catalog enrichment effort that would take a team months to complete manually can be executed in a fraction of the time with AI — and stays current as new products are added. The practical outcome: buyers find the right product faster, inbound “do you carry this?” calls drop, and your inventory becomes visible in channels that keyword-optimized content never reached.
The foundation that makes Smart Catalog work is structured data management — typically anchored in a PIM like Akeneo — that gives AI a clean, consistent input to work from and a reliable place to write enriched output back to.
Trend 8: AI Sales Rep Assistant — Turning Every Rep Into Your Best Rep
The institutional knowledge problem in B2B distribution is real and persistent. Senior reps know which accounts are due for reorder. They know which products to cross-sell to which customers. They know the right moment to call and what to lead with. Junior reps don’t — and the gap shows up in conversion rates, average order value, and customer retention.
The AI Sales Rep Assistant is what closes that gap at scale. It surfaces per-account, per-rep intelligence before every interaction: which accounts haven’t reordered on their usual cycle, which customers are likely to need a product they haven’t bought yet, which deals are at risk, and what to prioritize this week. Rather than relying on a rep’s memory or the quality of their CRM notes, the system derives these signals from actual order history, behavioral data, and account patterns.
The impact compounds in both directions. Experienced reps move faster because the prep work is done for them. New reps perform closer to the level of experienced ones because the institutional knowledge is surfaced, not assumed. And sales managers get visibility into account health across the entire book of business — not just the accounts their best reps happen to be watching closely.
This isn’t a forecasting tool or a dashboard. It’s an active intelligence layer that tells reps what to do next and why — specific to each customer, updated continuously as new data comes in.
Trend 9: RFQ Process Automation — From Days to Minutes, Without Sacrificing Accuracy
The request for quote process is one of the highest-friction points in B2B selling — and one of the most automatable. In traditional distribution operations, a rep receives an RFQ, manually checks inventory availability, looks up customer-specific pricing rules, calculates lead times, applies margin targets, and builds a quote — often across multiple systems. The process takes hours. Errors happen. Deals are lost while buyers wait.
RFQ Process Automation replaces that manual chain with an AI-driven workflow that does the same work in minutes. The system reads the incoming request, pulls live inventory from the ERP, applies the right pricing tier for that specific customer, accounts for current lead times and tariff exposure, and generates a margin-optimized quote — ready for rep review or, for straightforward requests, sent directly.
The speed advantage alone changes competitive dynamics. In categories where buyers are getting quotes from multiple suppliers, the first accurate quote has a disproportionate close rate. But the accuracy improvement matters as much as the speed. Quotes generated from live ERP data don’t have the pricing errors that happen when reps work from stale spreadsheets or cached price lists.
For distributors handling high quote volumes — especially those with complex customer-specific pricing structures or significant tariff exposure — this is one of the fastest paths to measurable revenue impact from AI. The manual hours saved per quote, multiplied across hundreds of RFQs per week, translates directly into rep capacity that can be redirected toward relationship-building and larger deals.
Across all nine of these shifts, the pattern is the same: the technology is available, but the value it delivers depends entirely on the infrastructure underneath it. At DotcomWeavers, we’ve spent over two decades building that infrastructure for B2B distributors and manufacturers — and the work we do today across agentic commerce readiness, AI personalization, Smart Catalog, dynamic pricing, RFQ automation, and operations workflows is a direct extension of that foundation. If any of the trends in this post describe a gap you’re feeling, or an opportunity you haven’t been able to move on, that’s the conversation worth having.
What B2B Leaders Should Do Right Now
Return to where we started: 64% of B2B leaders say AI will have a “very significant” impact on digital sales. Only 20% feel prepared. That gap is both the problem and the opportunity — for the companies on the right side of it.
The six trends covered here are not independent. They compound. AI personalization works better when built on clean ERP data. Agentic commerce requires AI-powered search to function. Order automation frees sales teams to act on AI-generated intelligence. You don’t have to start everywhere at once, but you do need to build with the whole system in mind.
A practical starting point framework
- If your biggest pain is operational: Start with order automation or AI demand forecasting. These have the fastest, most measurable ROI and require the least customer-facing change management.
- If your biggest pain is customer experience: Start with AI search or buyer portal personalization. The impact shows up quickly in self-service rates and reduced inbound inquiry volume.
- If your biggest pain is sales performance: Start with dynamic pricing or AI sales intelligence. The combination of better forecasting accuracy and faster quoting changes close rates within a quarter.
- If you’re not sure where to start: Start with a data readiness audit. AI on bad data doesn’t produce intelligence — it produces expensive noise.
Where DotcomWeavers Fits
DotcomWeavers has spent more than two decades building B2B digital commerce infrastructure for distributors and manufacturers. The shift to AI in 2026 isn’t a new chapter for us — it’s the next layer of the same foundation we’ve always built: clean data, connected systems, and commerce experiences that actually work for complex B2B buying environments.
We don’t approach AI as a standalone product layer bolted onto an existing platform. We approach it as the output of getting the underlying architecture right — which means ERP integration that’s actually real-time, product catalogs that are actually structured, and commerce platforms that are actually API-accessible. When those foundations are in place, the AI capabilities this blog describes aren’t aspirational. They’re deployable.
Here’s what that looks like across the six trends:
- Agentic Commerce Readiness: We build the ERP-connected catalog architecture, real-time pricing APIs, and inventory sync that makes your storefront legible and transactable by AI buyer agents.
- AI Personalization: We deploy and configure native AI personalization across Adobe Commerce, BigCommerce, and Shopify Plus — including account-level catalogs, behavioral engines, and predictive reorder surfaces.
- AI Search & Catalog Enrichment: We implement intelligent search and Akeneo PIM integration that structures your product data so AI search — on your storefront and in external AI tools — actually returns the right results.
- Dynamic Pricing & Quote Intelligence: We deliver AI-driven dynamic pricing as a built service, including ERP-connected engines that handle customer tiers, volume breaks, and tariff exposure automatically.
- Workflow & Operations Automation: We use n8n and Make to build the order intake, supplier communication, and customer service automation workflows that eliminate manual back-office work at scale.
- Data Foundation & Platform Architecture: We build on API-first platforms with deep integration expertise across Epicor, NetSuite, and Infor — creating the unified data layer that AI requires to work.
The right next step isn’t a product pitch — it’s a conversation about where your business is, what’s working, and what data and infrastructure gaps are preventing your AI investments from compounding. Every engagement we have starts with understanding the current state before recommending a path forward.
If any of the six trends in this blog describe a gap you’re feeling — or an opportunity you haven’t been able to move on — we’re the right conversation to have.
