Build or Buy: Why OEM Leaders Choose Productized AI Assistants

Build or Buy: Why OEM Leaders Choose Productized AI Assistants over In-house Projects

When a machine stops, whether it’s limited operator knowledge or a failure, both the operator and service centers start scrambling through binders of manuals, chase down specialists, and make urgent calls to anyone that could help. Modern machinery manuals are easily thousands of pages long, and critical instructions are often buried across PDFs, printouts and portals. When critical equipment goes down, every minute is measured in monetary losses, and missteps can erode customer loyalty.

OEMs know that improving operator support, after-sales efficiency and consistency is essential to maintaining uptime and trust. It’s a core part of the brand promise. Yet as machines become more complex and global fleets grow, internal resources are stretched thin. The conversation has shifted from “should we consider an AI assistant?” to “how do we make it happen, build it ourselves or adopt a platform?”

What an Assistant Needs to Do and Why It’s Not Just a Chatbot

Complex equipment demands faster answers

Modern machines are more capable than ever. Complexity keeps increasing, but documentation is scattered and guidance often hard to access during work. Ramp-up time for new operators and technicians takes longer and longer, costing both time and money. An AI assistant that is equipped with OEM-approved sources including manuals, maintenance instructions, spare parts information, images, safety warnings and error codes can give operators a safe fix in seconds rather than hours of digging through physical guides or searching countless PDFs.

Role-based workflows across the ecosystem 

OEMs aren’t looking for a chatbot. They need an industrial-grade assistant that:

• Serves different user groups with the right depth of information and language: operators, dealers, technicians, after-sales teams and product managers.
• Supports multilingual interaction and hands-free use across phones, tablets and desktops.
• Retrieves the right images, tables, diagrams, safety notices and step-by-step procedures, not just plain text.
• Integrates with machine telemetry and external APIs to provide context-aware answers

spogen.ai’s Smart Assistant, for example, guides operators through safe startup, maintenance, troubleshooting steps and telemetry-based tips with voice prompts and on-screen diagrams, a daily use case that reduces the need to call support and keeps machines running through optimal use. For after-sales and fleet managers, the same platform offers analytics on common questions and gaps in documentation, helping prioritize continuous improvement. These capabilities illustrate what a purpose-built assistant must offer, but the question remains: how is this best built and maintained?

Productized AI Assistants vs. Building In-house

Ready platforms: fast and scalable

Productized AI assistants designed for machinery provide several advantages for OEMs evaluating time-to-value and total cost of ownership: 

• Fast deployment and proof of concept. Off-the-shelf solutions can be rolled out in weeks rather than months or years, enabling a production-ready PoC that delivers immediate usage data, user feedback without tying up R&D and IT resources.
• Predictable costs and vendor support. A ready-to-use software platform lowers upfront investment and includes regular updates and maintenance, reducing internal IT overhead and financial risk.
• Multimodal capabilities. A good assistant handles text, images and voice. Reliable presentation of visual and structured information is expected, but costly to develop internally.
• Built-in analytics and feedback loops. A well-built platform tracks, in an anonymous format, what users ask and where documentation is lacking, giving actionable insight without building custom backends.

Building internally: underestimating the complexity

Many OEMs have tried to or are building their own assistants. Initial prototypes using generic large language models often look promising but digging deeper into data while keeping high accuracy and real-world deployments reveal persistent challenges. spogen.ai’s own experience working with OEMs highlights the key hurdles:  

• Data pipelines and retrieval. Reliable answers depend on retrieval-augmented generation (RAG) pipelines that can fetch relevant chunks from massive manuals and similar documentation. This goes far beyond uploading PDFs to a chatbot.
• Multilingual and multimodal logic. Supporting multiple languages and speech requires translation, vision and speech systems working together. It’s more than an LLM.
• Safety and guardrails. Preventing incorrect answers and ensuring reliable, safety-critical guidance relies on structured data, controlled retrieval and the safeguards built into the architecture of the solution.
• User experience design. Operators expect hands-free, natural communication and clear guidance. Designing and testing such interfaces is a major project.
• Maintenance and scaling. Internal teams must curate content, ensure accuracy, integrate new machine families and keep up with the pace of AI so they don’t fall behind. Ongoing development often competes with new product launches, causing prototypes to stall.

The result? Many projects that started as simple chatbots evolve into multi-year undertakings. Observations show that off-the-shelf solutions deliver quick wins but custom solutions require significant planning and resources. For teams under pressure to deliver measurable improvements, the timeline and uncertainty of in-house development are hard to justify.

A recent research from MIT underscores the point. The study found that 95% of generative AI pilots fail to deliver meaningful business impact. The problem isn’t the models themselves, but the gap between generic implementation and real-world workflows they’re meant to support. The report notes that companies partnering with specialized vendors succeed about twice as often as those building internally. These findings mirror our own experience: success comes from focus, execution and choosing a partner who understands your domain.

Why OEMs Partner with spogen.ai

spogen.ai focuses exclusively on machinery and has distilled the lessons from multiple OEM engagements into Smart Assistant and Tech Assistant. By partnering with spogen.ai, OEMs can:

• Gain a platform built for machinery. Both assistants are designed to ingest complex information, surface the right images, and guide users through use, maintenance and diagnostics, in virtually any language.
• Accelerate time to market and control costs.ai handles data ingestion, multilingual and multimodal logic, enabling OEMs to deploy even within a much shorter time and operate under a predictable SaaS model.
• Focus on core competencies. After-sales teams can focus on improving service operations and commercial teams on positioning and sales, while spogen.ai manages the assistants and their continuous updates.
• Benefit from continuous improvement. Because spogen.ai works with multiple clients from OEMs to dealerships and field service companies, its models and workflows evolve based on broad real-world usage, delivering improvements that a single in-house project could rarely achieve. 

Choosing a specialized platform mitigates risk. Manual documentation and lack of real-time support make troubleshooting slower and more expensive. By turning scattered manuals and data into a unified assistant, spogen.ai helps OEMs deliver faster answers and smarter operation without drowning after-sales teams and trainers in additional tasks.

Next Steps

The question isn’t whether AI assistants will improve customer experience; that debate is resolved. It’s about the best path to implementation and results. Building your own assistant requires specialized AI expertise, sustained investment and long timelines without guarantees of success. Productized solutions like spogen.ai’s Smart Assistant and Tech Assistant offer speed, reliability and industry expertise that are difficult to replicate.

To decide, assess your documentation, after-sales workflows, and integration needs. To decide, assess your documentation, after-sales workflows, and integration needs. Start with a pilot on a ready platform, measure response usefulness and user satisfaction, and determine whether building an internal solution aligns with your capabilities and timelines. In most cases, working with a product partner like spogen.ai gets you there faster and with less risk than build-centric consulting, enabling your teams to focus on what they do best: designing machines that empower, not overwhelm.

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