B2B Search At Scale: Expert Insights From Takeoff Digital

We spoke with Elvin Ragip, Takeoff founder, about the technical and operational realities of B2B search at scale.

Takeoff Digital is a UK-based e-commerce agency specializing in B2B platforms built on Adobe Commerce and Shopify. Their work centers on procurement integrations and custom automation for enterprise clients. They also maintain their own PunchOut integration module on the Adobe Commerce Marketplace.

Takeoff Digital is a full-service e-commerce agency based in the United Kingdom, specializing in Adobe Commerce (Magento), Shopify, and custom-built solutions using frameworks like Laravel and Symfony. Our sweet spot is B2B e-commerce. We build platforms that handle complex requirements like customer-specific pricing, procurement system integrations, multi-warehouse inventory, and automated order workflows. We also maintain our own PunchOut integration module on the Adobe Commerce Marketplace, which connects e-commerce stores to procurement systems like SAP Ariba, Coupa, etc.

PunchOut is a protocol, typically using cXML or OCI, that allows a buyer’s procurement system to connect directly to a supplier’s e-commerce store. The buyer “punches out” from their procurement platform, lands in the supplier’s catalog, shops as normal, and then sends their basket back into their procurement system for approval and purchase order generation.

Because many enterprise buyers don’t place orders through a traditional checkout. They’re locked into procurement workflows with approval chains, budget codes, and compliance requirements. If your store doesn’t support PunchOut, you’re invisible to those buyers. For suppliers, it opens up access to large-volume, repeat-order customers who would otherwise only buy through EDI or manual processes.

L&S Engineers runs a large B2B catalog with thousands of SKUs and complex pricing structures: customer group pricing and special pricing rules. The tricky part was getting that data into a format that search could actually use. Magento’s EAV architecture spreads pricing across multiple database tables instead of storing it in one flat format, so a standard export wouldn’t work. We built a custom data feed app that queries that structure directly and reliably pulls out the full catalog: products, category hierarchies, stock levels, media, and all the pricing data, including customer group-specific prices. It then transforms everything into a clean format for Luigi’s Box.

First: data integrity on delivery. We use a generation-based commit model, meaning the search index only activates a new catalog version once every batch has been successfully transmitted. There’s no half-updated state where some products are live and others aren’t, which matters when pricing accuracy is non-negotiable.

Second: keeping it current without unnecessary overhead. Rather than re-sending the entire catalog every time, the feed supports incremental delta syncs, pushing only products that have changed since the last run. For a catalog with frequent price updates, stock changes, and new SKUs, that keeps the search index fresh without hammering the system.

Third: resilience. Failed batches are tracked and retried automatically rather than silently dropped, so a temporary network issue doesn’t leave gaps in the index.

Fast results with the correct price surfaced for every customer. In B2B, that’s the baseline expectation. A buyer shouldn’t see a price that doesn’t apply to their account. It comes down to how search handles different types of queries, because B2B buyers don’t all search the same way.

There are two different modes. When a buyer types in an exact SKU or part number, they know precisely what they want. The search should return that single product instantly, with zero noise. Fuzzy matching and synonyms should be dialed right down for those queries. Speed and precision are everything.

Descriptive queries are the opposite. “Heavy-duty safety boots” or “A4 lever arch files” require relevance scoring, synonym handling, attribute weighting, and intelligent filtering. The search needs to understand intent and surface a useful set of results the buyer can narrow down.

It is, because B2B buyers often switch between both modes in the same session. They might search for a part number from a spec sheet, then browse more broadly for alternatives. The search engine needs to detect the query type and adjust its behavior accordingly. That only works when the underlying product data is rich and well-structured. It’s why the data layer matters so much before you even think about search configuration.

The data layer is responsible for completeness and accuracy: every SKU present, every price correct, every attribute populated. The search layer is responsible for interpretation, understanding what the buyer means and surfacing the right result. The mistake we see often is teams trying to compensate for bad data with clever search configuration. It doesn’t work. If the catalog is incomplete or inconsistent, no amount of relevance tuning will save you.

The biggest shift is that B2B buyers now expect the same experience they get as consumers: personalized search and intelligent recommendations, but layered on top of complex pricing, customer-specific catalogs, and procurement workflows. Those expectations are raising the floor for what a B2B search implementation needs to deliver.

Query understanding. A buyer searching for “3M tape for electrical work” expects the right result to come up, even if the catalog lists it as “Scotch Super 33+ Vinyl Electrical Tape.” That gap between how buyers describe things and how suppliers catalog them is a real, persistent problem in B2B. Semantic search closes it in a way that keyword matching can’t.

That’s where we’re seeing growing interest. A maintenance engineer and a procurement manager might be looking for the same product category, but they approach it completely differently. One searches by technical spec, the other by supplier relationship or budget code. A guided selling experience that adapts to role or purchase history is still relatively rare in B2B, but the demand is there.

Yes, and it changes it for the better. When merchants move away from monolithic platforms, they start assembling individual components: a dedicated search provider, a dedicated PIM. Each one has to justify itself on its own terms. That’s healthy. It forces cleaner integrations and makes it easier to upgrade individual parts without rebuilding everything. For search specifically, it means the integration has to be reliable and the value has to be measurable. You can’t hide behind a bundled solution.

Data quality, without question. At mid-size, you can get away with inconsistent product data, manual processes, and a few workarounds. At enterprise scale, those cracks become chasms. When you’ve got 50,000 SKUs, customer-specific pricing across hundreds of accounts, and integrations with ERP and procurement systems, bad data doesn’t just slow you down. It breaks things in ways that are expensive and hard to recover from.

The entire operating model. Enterprise e-commerce isn’t just a bigger website. You need proper staging environments, release management, monitoring, and incident response. The teams that struggle most with scaling are the ones that try to run an enterprise platform the same way they ran their mid-size store: the same informal processes, the same tolerance for manual steps, the same assumption that someone will notice if something breaks. At scale, someone notices because customers tell them.

Our rule of thumb: if it’s repetitive or error-prone, automate it. Product imports from ERP systems, stock level syncs, price updates, order status notifications. These should never depend on someone remembering to run a spreadsheet.

We keep things manual when they require judgment. Merchandising decisions, promotional content, quality checks on new supplier data. These benefit from a human eye. The mistake we see is merchants automating the wrong things, like blindly importing supplier data without validation, while keeping manual the things that should be automated, like checking for out-of-stock products still appearing in search.

The sweet spot is automated execution with manual oversight. Automate the heavy lifting, build in dashboards and alerts, and the team can catch issues before customers do.

Start early – at least 8-12 weeks before peak. The worst time to discover your product feed is broken is Black Friday morning.

On the data side: audit your catalog thoroughly. Fix missing images and out-of-stock products still appearing in search. Run through the full buyer journey for your top-selling categories and remove anything that creates friction.

On the infrastructure side: load test at three to five times your expected peak traffic, set up monitoring so you know the moment something goes wrong, and pre-stage promotional content on staging before it goes live.

One thing that often gets overlooked: test your integrations under volume, not just your front end. If your ERP sync runs every 15 minutes and you’re suddenly processing 10 times the normal order volume, that sync will fall behind. The whole pipeline needs testing, not just what the customer sees.

“How do we make sure our data is ready for this?” Most merchants come in focused on features. They want better search and working recommendations. Those are the right goals. But the first question should always be about the foundation: What does the data look like? How complete is the catalog? How reliable is the pricing export? If we can answer those questions well, everything else becomes much more straightforward.

Wrapping up

Better B2B search starts with the data layer, not the search engine settings. If the catalog is complete, pricing is accurate, and the pipeline is reliable, the search layer has something real to work with.

B2B adds complexity that B2C merchants rarely face: buyers switch between exact part lookups and broad descriptive searches in the same session. Pricing is account-specific, and procurement workflows add another integration layer. The same principle holds whether you’re running 5,000 SKUs or 500,000. If the data is clean and the infrastructure is tested, the hard problems become solvable. If it isn’t, no feature will save you.