AI Knowledge Search Going Enterprise: A Practical Guide for Retail Chains in Switzerland

Direct answer: AI-powered knowledge search platforms like DeepJudge are moving from niche legal and compliance tools into mainstream enterprise use, which means the pattern behind them — instantly retrieving the right document, policy, or product answer from a mountain of internal data — is becoming a baseline expectation, not a luxury feature. For Swiss retail chains, the practical implication is that this same retrieval intelligence needs to live inside your staff-facing and customer-facing mobile apps, not just in a back-office dashboard. If your store associates and your shoppers still have to dig through PDFs, static FAQ pages, or a call to head office to get an answer, you are already behind where the market is heading.

DeepJudge, a Swiss AI-powered knowledge search and document retrieval platform, was named in FintechNews.ch's AI Fintech list in 2026, a signal that Switzerland-built enterprise search and retrieval tools are gaining real commercial traction rather than staying in pilot mode. The trend itself is not about DeepJudge as a product for retailers — it started in legal and financial services, where large volumes of dense documents make manual search painfully slow. But the underlying capability it represents — AI that understands a natural-language question and pulls the precise answer out of thousands of internal documents, contracts, or product records — is exactly the kind of infrastructure retail chains are starting to need for their own internal knowledge bases, store operations manuals, supplier catalogs, and customer support content. A precise adoption figure for retail specifically is not publicly available from this source; what is clear is that enterprise buyers in Switzerland are now comfortable trusting AI search tools with sensitive, high-stakes information, which lowers the barrier for retail to follow. This matters because retail chains sit on exactly the kind of fragmented, fast-changing information — pricing, stock levels, return policies, supplier terms, loyalty program rules — that this class of tool is built to organize and surface on demand.

What "AI Knowledge Search Going Enterprise" Actually Means

The core idea behind platforms like DeepJudge is retrieval-augmented search: instead of a keyword search that returns a list of documents you then have to read, an AI layer reads the question, understands intent, searches across your actual internal content, and returns a direct, sourced answer. This is a meaningfully different experience from the search boxes retailers have used for the past decade. It is also different from a generic chatbot, because the answers are grounded in your own documents rather than general internet knowledge, which matters enormously when the answer needs to be correct — a return policy exception, a supplier contract clause, a product recall notice.

The reason this is "going enterprise" rather than staying a research curiosity is trust and integration maturity. Early AI search tools struggled with accuracy and were confined to sandboxes. What FintechNews.ch's 2026 list reflects is that a Swiss-built platform in this category has reached the point where financial and legal enterprises — sectors known for conservative technology adoption — are willing to put it into production. That threshold crossing is the real trend. Once a technology pattern proves itself in a highly regulated, accuracy-sensitive sector like Swiss finance, it typically becomes available and expected across adjacent enterprise verticals within a couple of years, and retail is one of the most natural next stops because retail operations generate enormous, constantly-updating internal knowledge that is currently scattered across intranets, shared drives, PDFs, and individual employees' heads.

Why Retail Generates the Same Problem DeepJudge Solves

Think about what a multi-location retail chain actually has stored: store operating procedures, seasonal promotion rules, supplier agreements, product specification sheets, staff training material, warranty and returns policy variants by product category, and loyalty program terms that change quarterly. Any associate on the floor, and increasingly any shopper using your app, may need an answer drawn from that pile in seconds. That is structurally the same problem DeepJudge was built to solve for legal teams buried in contracts — just wearing a retail uniform instead of a suit.

Why This Matters Specifically for Retail Chains in Switzerland

Switzerland's retail environment has particular characteristics that make this trend more urgent, not less. Swiss retail chains typically operate across multiple language regions — German, French, Italian, sometimes English for international brands — which multiplies the volume and complexity of internal documentation that needs to stay synchronized and searchable. A staff manual, a return policy, or a product safety notice often needs to exist correctly in at least two or three languages, and keeping frontline staff able to find the right, current version in the right language is a genuine operational headache that manual document management handles badly.

Swiss consumers and Swiss enterprise buyers also have a well-documented cultural expectation of precision, reliability, and low tolerance for wrong answers — whether that is a customer being told an incorrect return window or a store associate quoting the wrong loyalty discount. That expectation is precisely what makes AI knowledge search appealing here: the value proposition is not "answers faster" so much as "answers correctly, sourced, and traceable," which fits Swiss enterprise standards for accountability far better than a generic chatbot that might hallucinate. When a Swiss-built platform like DeepJudge earns enterprise trust in finance and law, it sets a local benchmark that Swiss retail leadership will be aware of and will start asking their own technology partners about, even if no retail-specific product exists yet.

There is also a competitive angle. Retail chains operating physical stores across Switzerland face constant pressure from margin compression and rising staffing costs. Anything that reduces the time a store associate spends hunting for information, or reduces the volume of support calls to head office, has a direct and measurable operational payoff. A chain that equips its staff with instant, accurate answers inside a mobile tool will simply run its stores more efficiently than one that still relies on binders, PDFs, or Slack messages to a manager.

Staff turnover is another factor that makes this more relevant in Switzerland's retail sector than it might first appear. Seasonal hiring around holidays, tourist-heavy periods in cities like Zurich, Geneva, and Lucerne, and the general churn in frontline retail roles mean a meaningful share of your workforce at any given time is relatively new. New staff are exactly the group most likely to give a customer a wrong answer because they have not yet memorized every policy exception, every product detail, or every regional variation in terms. A knowledge search tool does not require memorization at all — it shortens the time between "I don't know" and "here is the correct, sourced answer" regardless of how long someone has been on the job. That has a direct, measurable effect on training costs and on the consistency of the customer experience across a chain's locations, which matters more in a market where shoppers expect the same quality of service whether they are in a small-town branch or a flagship city store.

How This Differs From Past "Digital Transformation" Efforts

It is worth being precise about why this trend is different from earlier waves of retail digitization, because retail leadership has seen plenty of over-promised technology projects before. Earlier intranet search tools and document management systems promised similar benefits but typically failed because they still required someone to read through a returned document to extract the actual answer, and their search quality degraded quickly as content volume grew. What has changed with the class of tool DeepJudge represents is that the AI layer itself does the reading and synthesis step, not just the retrieval step, and does so with source attribution that lets a user verify the answer rather than blindly trust it. That combination — accurate retrieval plus synthesized, sourced answers — is the specific technical maturity that allowed a Swiss platform in this category to earn enterprise trust in a sector as risk-averse as finance, and it is the same combination that makes the pattern worth adopting in retail rather than dismissing as another over-hyped tool.

What Changes in Practice for Your App and Website

This is where the trend stops being an abstract industry signal and becomes a product decision. If AI knowledge search is becoming the baseline for how enterprises expect information to be retrieved, then the retail chains that win are the ones who put that retrieval layer directly into the tools their people and customers already use every day — which for most retail operations means a mobile app, not a browser tab on a back-office desktop.

For Store Staff

A staff-facing mobile app with an AI-search layer means an associate on the shop floor can ask, in plain language, "what's the return policy for opened electronics" or "do we have this SKU in the Geneva warehouse" and get a direct, sourced answer without leaving the customer's side. This requires the app to be built with proper offline resilience for spotty in-store connectivity, role-based access so sensitive supplier or pricing data is only visible to the right staff tier, and a search layer connected to your actual internal document repository rather than a static FAQ screen bolted onto an existing app. Our guide on Native vs Cross-Platform Mobile Development: A 2026 Decision Guide is directly relevant here, because a staff tool that needs reliable offline behavior, camera-based barcode scanning, and tight device integration often benefits from different technical choices than a pure customer-facing app.

For Shoppers

On the customer side, the same underlying pattern shows up as smarter in-app product search and support. Instead of a shopper scrolling through a static FAQ page or a rigid category filter, an AI-grounded search can answer "which of these jackets are machine washable" or "what's your exchange policy for sale items" directly, pulling from your actual product data and policies. That only works, though, if the product pages and underlying data feeding that search are structured well in the first place — which is why the way you present product information matters as much as the search technology sitting on top of it. Our piece on Product Page Design That Converts: What the Data Shows covers the structural groundwork that any AI search layer depends on: clean, complete, well-organized product data is what a retrieval system searches over, so weak product pages produce weak AI answers regardless of how good the underlying model is.

One Codebase, Multiple Surfaces

Retail chains rarely need just one app. You typically need a customer shopping app, an internal staff tool, and often a web presence that all need to stay in sync with the same underlying product and policy data. Building each of these as a separate, disconnected project multiplies the cost of keeping an AI knowledge layer current across all of them. This is exactly the scenario our article on Multi-Platform Software Strategy: Web, Mobile, and Desktop From One Codebase addresses — a shared architecture strategy means that when your return policy changes, it updates once and is instantly reflected correctly across the staff app, the shopper app, and the website, rather than requiring three separate manual updates that inevitably drift out of sync.

What to Do About It Now

The realistic starting point for most Swiss retail chains is not building a full AI search platform from scratch — that is a significant undertaking best left to specialized vendors like DeepJudge in the sectors where it already operates. The realistic starting point is auditing where your internal knowledge currently lives, how fragmented and inconsistent it is across languages and locations, and where a mobile app with a well-structured, AI-searchable knowledge layer would remove the most daily friction for staff and customers.

A sensible sequence looks like this: first, consolidate your policy and product documentation into a single, structured source rather than scattered PDFs and shared drives. Second, decide whether your priority is a staff-facing efficiency tool, a customer-facing support improvement, or both — this shapes the technical approach significantly. Third, build (or extend) a mobile app that connects to that structured knowledge base with a natural-language search layer, tested carefully in each of your operating languages. Fourth, measure the reduction in support calls, staff onboarding time, or customer support ticket volume before expanding scope further.

Where Mobile App Development Fits

This is squarely a mobile app development project, not a website tweak. The reason mobile matters so much here is that the moments where knowledge search is most valuable — a store associate helping a customer, a shopper standing in an aisle deciding on a purchase — happen on a phone or tablet, not at a desktop. An AI-grounded search feature bolted onto a slow, clunky, or unreliable app will underperform a well-built static FAQ, so the underlying app quality and architecture decisions matter just as much as the AI layer itself.

It is also worth being honest about sequencing, because chains sometimes want to jump straight to the most ambitious version of this idea. Trying to launch a fully multilingual, staff-and-customer, AI-searchable knowledge system in one release is a common way for these projects to stall, because it requires simultaneously solving data structuring, translation consistency, access control, and app architecture all at once. A staged rollout — start with one language, one user group, and one high-value use case such as returns and warranty questions — lets you validate that the underlying data and search quality actually hold up in daily store conditions before expanding scope. This is not a compromise so much as good project discipline: a knowledge search feature that gives confidently wrong answers in month one does more reputational damage, internally and with customers, than a narrower feature that works reliably from day one.

How This Plays Out Across a Typical Rollout

A realistic first quarter for a mid-sized Swiss retail chain looks something like this. In the first few weeks, someone on your team works with a development partner to inventory where policy and product knowledge actually lives today — which is usually a mix of a shared drive, a few PDFs that get emailed around, and information that only exists in a regional manager's head. That inventory step alone often surfaces inconsistencies that were already causing problems before any AI layer gets involved, such as two store regions operating on slightly different return windows without anyone having formally reconciled the policies.

Once the knowledge base is consolidated into a single structured source, the next phase is building the search experience into a pilot app or an existing app's new module, scoped to one staff group in one or two locations. This pilot phase is where you learn the real edge cases: what happens when a question falls outside the knowledge base entirely, how the interface should indicate uncertainty rather than guessing, and how staff actually phrase questions in practice versus how you assumed they would. Only after this pilot proves out in daily conditions does it make sense to expand to additional languages, additional store locations, or a customer-facing version of the same underlying search layer. Chains that skip the pilot and go straight to a full multi-language, multi-surface rollout tend to spend more time firefighting data quality issues after launch than they would have spent validating the approach beforehand.

Pricing Context: What This Kind of Work Typically Falls Under

Costs vary with scope, but retail chains evaluating this kind of AI-search-enabled mobile work typically map to one of Scult's standard engagement tiers:

Tier	Typical Scope	Fit for This Trend
Essential ($1,000)	Single-purpose mobile feature or focused app update	A first staff-facing FAQ/search module added to an existing app
Growth ($2,000)	Full mobile app build or major feature set across staff and customer surfaces	A staff knowledge-search tool or an upgraded customer support search experience
Enterprise ($4,000+)	Multi-surface, multi-language app ecosystem with deep data integration	A synchronized staff app, customer app, and web presence sharing one AI-searchable knowledge base across German, French, and Italian

Most Swiss retail chains with multiple store locations and multilingual operations will find their real need sits at Growth or Enterprise once staff and customer needs, plus multi-language support, are properly scoped.

Key Takeaways

• DeepJudge's enterprise traction, as reported by FintechNews.ch in 2026, signals that AI-grounded knowledge search has crossed from experimental to trusted in conservative Swiss sectors, and retail is a natural next adopter of the same pattern.
• Retail chains sit on exactly the kind of fragmented, multilingual, constantly-changing internal knowledge — policies, product data, supplier terms — that this technology is designed to organize and surface instantly.
• The practical entry point for most chains is a mobile app, not a desktop dashboard, because the moments staff and shoppers need answers happen on the floor, not at a desk.
• Staff-facing and customer-facing search have different technical requirements around offline reliability, access control, and language handling, and should be scoped separately even if built on shared infrastructure.
• Weak or poorly structured product and policy data will produce weak AI search results regardless of the technology layered on top, so data and content structure work has to happen alongside any AI feature build.
• A multi-platform strategy that shares one codebase and one data source across staff, customer, and web surfaces avoids the drift and duplicated cost of maintaining several disconnected apps.

Swiss retail chains that treat this as a wait-and-see trend risk falling behind competitors who quietly reduce staff friction and customer support load first. If you want help figuring out where a knowledge-search-enabled mobile app fits into your specific store operations, book a meeting with our team.

Frequently Asked Questions

What is AI knowledge search, in plain terms?

AI knowledge search is a system that reads a natural-language question, searches across your actual internal documents and data, and returns a direct, sourced answer instead of a list of files to read through yourself. It differs from a keyword search box because it understands meaning and intent, not just matching words.

Is DeepJudge a retail product?

No, DeepJudge is a Swiss AI-powered knowledge search and document retrieval platform that gained traction primarily in legal and financial enterprise use cases, as noted in FintechNews.ch's 2026 AI Fintech list. The relevance to retail is the underlying pattern it represents, not the product itself being retail-specific.

Why should a retail chain care about a legal-tech trend?

Because the core problem DeepJudge solves — finding the right answer instantly inside a large volume of dense, frequently updated documents — is structurally the same problem retail chains face with policies, product data, and supplier information. Technology patterns that prove themselves in conservative sectors like Swiss finance typically spread to adjacent industries within a short window.

Do I need a custom AI model to add knowledge search to my app?

No, most implementations use retrieval-augmented approaches layered on top of existing AI models, connected to your own structured data. The engineering effort is mostly in organizing your data properly and building the app experience around it, not training a model from scratch.

What's the difference between this and a chatbot?

A generic chatbot answers from general internet knowledge and can produce inaccurate or invented answers. AI knowledge search is grounded specifically in your own documents and data, so answers are traceable back to a real source, which matters far more when a wrong answer has real consequences like an incorrect return policy.

Why does this matter more for multilingual Swiss retailers specifically?

Swiss retail chains often need policies, training material, and product information to exist correctly across German, French, and Italian simultaneously. Keeping that content synchronized manually is error-prone, and an AI search layer needs well-structured multilingual data to work reliably across all regions.

Should this be a staff tool, a customer tool, or both?

It depends on where your current friction is worst. Many chains start with a staff-facing tool because the return on investment is easier to measure in reduced onboarding time and fewer escalations to head office, then extend to customer-facing search once the underlying data structure is proven.

What kind of app is best suited for this: native or cross-platform?

It depends on the specific requirements around offline use, device integration, and update frequency, which is exactly what our Native vs Cross-Platform Mobile Development: A 2026 Decision Guide walks through. A staff tool with barcode scanning and offline needs may lean differently than a customer shopping app.

Can we add this to our existing app instead of building a new one?

Often yes, if the existing app's architecture allows for a new search module and your underlying data can be structured for retrieval. A full rebuild is only necessary when the existing app's technical foundation can't reasonably support the new feature set.

How long does a project like this typically take?

A focused single-feature addition can take a few weeks, while a full staff-and-customer knowledge search ecosystem across multiple languages is a multi-month engagement. Scope and existing data readiness are the biggest timeline factors.

What does "structured data" mean in this context?

It means your policies, product details, and supplier information are stored in a consistent, machine-readable format rather than scattered across PDFs, printed binders, and individual staff knowledge. AI search quality depends directly on how well-organized this underlying data is.

Will this replace our customer support staff?

No, it's better understood as reducing the volume of repetitive, answerable-from-documentation questions so support staff can focus on complex or judgment-based issues. The goal is efficiency, not headcount replacement.

What happens if the AI gives a wrong answer to a customer?

Well-implemented retrieval-grounded search systems cite their source and are far less prone to invented answers than general chatbots, but no system is perfect. This is why staged rollout, clear escalation paths, and human review of edge cases matter during implementation.

Does this require us to digitize all our paper documentation first?

Any paper-only policies or manuals do need to be digitized and structured before they can be searched by an AI system, since it can only retrieve from data it has access to. This is often the most time-consuming part of a project, more so than the app development itself.

How does this affect our loyalty program and promotions?

Loyalty and promotion rules change frequently and are exactly the kind of information that benefits from AI search, since staff and customers often struggle to keep track of current terms. A well-built system surfaces the current, correct version instead of an outdated cached one.

What's a realistic first project if we're just starting out?

A focused staff-facing FAQ and policy search module added to an existing internal app is a common, lower-risk starting point that typically falls under our Essential tier. It proves the concept before expanding to customer-facing features.

How does this connect to product page design?

AI search over product information is only as good as the underlying product data, which is why our article on Product Page Design That Converts: What the Data Shows matters here — clean, complete product data structured well for humans is generally also structured well for AI retrieval.

Do we need separate apps for staff and customers?

Not necessarily separate apps, but they typically need separate access levels, permissions, and interface designs even if built on shared underlying infrastructure. A shared codebase strategy, as described in our Multi-Platform Software Strategy piece, can reduce the cost of maintaining both.

What data privacy considerations apply in Switzerland?

Any system handling customer or staff data needs to comply with Swiss data protection requirements, including where data is stored and processed. This should be addressed explicitly during technical planning, particularly if any cloud-based AI service is involved.

Can offline stores without reliable Wi-Fi still use this?

Store-floor connectivity is often inconsistent, so any staff-facing implementation should be designed with offline resilience and local caching in mind rather than assuming constant connectivity. This is a core architecture decision, not an afterthought.

How do we measure if this is actually working?

Practical metrics include reduction in support calls to head office, faster staff onboarding time, reduced time-to-answer for common customer questions, and fewer policy-related errors at checkout or returns. These should be tracked before and after rollout.

Is this only relevant for large retail chains?

Smaller chains with even two or three locations across different Swiss language regions face the same fragmentation problem, just at a smaller scale, so the underlying need applies broadly rather than only to large enterprises.

What's the risk of doing nothing?

The risk is largely operational drag: slower staff onboarding, more escalated customer questions, and inconsistent policy application across locations, all of which compound as a chain grows or adds locations. Competitors who solve this first gain a quiet efficiency edge.

Does this trend affect e-commerce-only retailers too?

Yes, e-commerce retailers face the same customer support search problem even without physical stores, and the mobile app considerations apply equally to shopping apps without a staff-facing floor component.

What's the role of search in reducing cart abandonment?

Confusing or incomplete product information is a common driver of abandoned purchases, and AI-grounded search that can answer specific product questions directly in-app can reduce the friction that leads to a shopper leaving without buying.

How does multilingual support actually get implemented technically?

Typically the underlying knowledge base is maintained with parallel structured content per language, and the search layer is configured to retrieve and respond in the shopper's or staff member's selected language rather than translating on the fly, which improves accuracy.

What if our product catalog changes constantly?

Frequent catalog changes are common in retail and require the underlying data feeding AI search to update automatically rather than through manual re-indexing, which should be a specific technical requirement discussed early in planning.

Should this be built in-house or with an external partner?

Most retail chains don't have in-house teams with both mobile development and AI retrieval expertise, so partnering with a team experienced in both is usually faster and more reliable than building the capability internally from scratch.

How does this relate to the Essential, Growth, and Enterprise pricing tiers?

Essential tends to fit a single focused feature, Growth fits a fuller staff or customer app build, and Enterprise fits a multi-surface, multi-language ecosystem — the right tier depends on how many surfaces and languages need to be covered.

What technical skills does our team need to maintain this after launch?

Ongoing maintenance mostly involves keeping the underlying content and data current rather than deep AI expertise, since the retrieval infrastructure itself is typically maintained by the development partner or a managed service.

Can this integrate with our existing inventory or POS systems?

Integration with existing inventory and point-of-sale systems is common and often valuable, since real-time stock and pricing data makes the search results far more useful than static documentation alone. This should be scoped explicitly during technical planning.

What happens to accuracy as our documentation grows?

Well-architected retrieval systems are designed to scale with growing document volume without a proportional accuracy loss, provided the underlying data stays well-structured and de-duplicated. Accuracy issues usually stem from messy source data, not from retrieval technology reaching a limit.

How does this affect staff training time?

New staff can get accurate answers immediately through search rather than needing extensive upfront memorization of policies, which can meaningfully shorten onboarding time, particularly for seasonal or high-turnover retail roles.

What's the biggest implementation mistake to avoid?

The most common mistake is building the AI search layer before properly structuring the underlying data, which results in a fast, confident-sounding tool giving inconsistent or wrong answers. Data structure work should come first.

Does this require ongoing subscription costs beyond initial development?

Depending on the AI services and infrastructure used, there are typically ongoing hosting and API costs beyond the initial build, which should be discussed and budgeted for during project planning rather than treated as a one-time expense.

How do we handle sensitive supplier or pricing information in a searchable system?

Role-based access control should be built into the app from the start so that sensitive supplier terms or cost pricing are only retrievable by staff with appropriate permissions, not exposed broadly across the organization.

Will this work well for a chain with only a handful of stores?

Yes, the value scales down reasonably well even for smaller chains, since the core problem of fragmented policy and product knowledge exists regardless of store count, though the investment level should scale accordingly.

What's the connection between this trend and mobile app development generally?

Because the moments where this technology delivers value happen on the shop floor or in a shopper's hand, mobile app development is the natural delivery surface, rather than a desktop-only tool that staff and customers rarely use in the moment they need an answer.

How do seasonal promotions get handled in an AI search system?

Seasonal and time-limited promotions need to be added to and removed from the structured knowledge base on a schedule so the AI search always reflects current, not expired, offers, which requires a content management process alongside the technical build.

Can this help with compliance and audit requirements?

A well-built system that logs what was asked and what source document answered it can actually improve audit traceability compared to relying on staff memory or inconsistent verbal guidance, though this should be explicitly designed into the system rather than assumed.

What if we operate across Switzerland and other European markets?

Multi-market retailers face an even larger fragmentation problem across different regulatory and language requirements, making a shared, well-architected knowledge system more valuable, not less, though scoping should account for market-specific policy differences.

How does this affect our website, not just our app?

The same structured knowledge base that powers app search can also power an improved website FAQ or support search experience, meaning the data investment pays off across both surfaces rather than being app-exclusive.

What's a realistic timeline to see measurable results?

Most chains can measure early signals like reduced support call volume within a few weeks of launch, though fuller operational impact, like faster staff onboarding, becomes clearer over a full quarter of use.

Should we wait for a retail-specific version of tools like DeepJudge?

Waiting risks falling behind competitors who apply the same underlying pattern to their own operations now rather than waiting for a packaged retail product, since the core capability is already implementable today with the right development partner.

How do we keep the AI search system's answers current as policies change?

The system should be connected to a content update workflow where policy changes are reflected in the structured knowledge base immediately, rather than requiring a technical rebuild each time a policy updates.

What's the relationship between this and customer self-service more broadly?

AI-grounded search is one part of a broader shift toward better customer self-service, alongside clearer product pages and simpler support flows, all of which reduce the burden on live support staff.

What if our current app is outdated or poorly built?

An AI search feature added to a poorly performing app foundation will underperform, so it's often worth addressing core app quality and architecture issues as part of the same project rather than layering a new feature onto a weak base.

How does this affect return and warranty handling specifically?

Return and warranty policies are often the most-asked and most product-specific questions in retail, making them a strong initial use case for AI search since getting them right directly reduces friction and staff time per interaction.

What should we ask a development partner before starting this kind of project?

Ask specifically how they handle multilingual data structuring, offline reliability for staff tools, data privacy compliance, and how they plan to keep the underlying knowledge base current after launch, since these determine long-term success more than the initial build quality alone.

Where should we start if we're not sure this applies to us yet?

Start with a straightforward audit of where your staff and customers currently struggle to find accurate information, then book a meeting with our team to discuss whether a focused first step makes sense for your specific operation.