Neurologik — straight answers.

That's exactly who we build for. We only work with manufacturers where product expertise is genuinely difficult — multiple variables, application-specific decisions, engineering involvement in almost every deal. If your products are simple to quote, we're not the right fit. If your engineers are the bottleneck on most technical questions, we are.

No. We build from what already exists — product documentation, spec sheets, configuration guides, application notes, past proposals, website content, technical manuals. Your engineers don't need to sit through interviews or extraction sessions upfront. They do need to review outputs and validate accuracy — that's not optional, and it's what makes the system accurate. But the time commitment is manageable, not a second job.

No. Most manufacturers we work with have exactly what you'd expect: PDFs scattered across a server, Excel pricing lists, product specs in varying states of update, and a lot of knowledge that lives in someone's head. We work with messy reality, not ideal conditions.

We're an early-phase company, and we're transparent about that. The technology foundation is built and working. We have paying customers in production. What we're still building is scale — more deployments, more product categories, more edge cases handled. If you need a vendor with a 10-year track record, we're not that. If you want a system that works now and a team that treats your deployment as a core project, we're worth the conversation.

We start with your existing materials — product catalogs, data sheets, application notes, old RFP responses, configuration guides. We structure that into a knowledge base and test it against real questions. Where there are gaps, we identify them and fill them from documentation first. Your team reviews outputs and flags anything that needs correction — that feedback loop is what makes the system accurate over time.

A first working version — limited in scope but real — is live in 3–4 weeks. From there you build on top of it: adding product areas, refining responses based on feedback, expanding to additional use cases. The system improves as your team uses it and gives feedback.

One knowledge base, distributed across internal teams, customers, and partners — each with their own access level and data permissions. When products change or pricing updates, you update the knowledge base once and every deployment reflects it immediately.

The AI runs alongside your existing systems, not in place of them. In the initial phase, it operates independently and outputs are passed into the tools your teams already use. Deeper integrations with ERP, CRM, or quoting systems are possible in later phases, but they're not a prerequisite.

Many companies try. AI is maybe 30% of the problem — the other 70% is structuring your specific product knowledge, building the review and approval workflows, and maintaining accuracy as products change. That's where most in-house attempts fail or stall. It takes significant time and money, and most never reach production quality.

CPQ tools are built for structured, rule-based catalogs. If your quoting requires genuine engineering judgment — application knowledge, exception handling, customer-specific constraints — CPQ either fails or requires years of configuration to get right. We're faster to implement, cheaper, and the system stays live — meaning it changes when your products change. Add a new product line, update a configuration, change a price: it's done immediately. CPQ is built around fixed rules. Every time your products evolve, you're back to reconfiguration.

Implementation starts at $10K–30K for a single use case. Most manufacturers start with one — partner enablement, RFP automation, or an internal knowledge base — and expand from there. Full product coverage across multiple use cases is a different scope and priced accordingly.

Engineering time freed from routine technical questions, faster response to opportunities, and reduced partner escalations. Most manufacturers see payback in 3–6 months.

Hiring solves capacity temporarily. A new sales engineer takes 12–18 months to reach full productivity, costs $150–200K per year, and eventually leaves — taking their knowledge with them. The constraint reappears. An AI knowledge base is operational in weeks, scales to unlimited parallel requests, and the knowledge is permanent. You're not replacing engineers — you're removing the ceiling their capacity creates.

Your product knowledge stays in your environment. We don't train shared models on your data. Each customer's knowledge base is isolated. We can work within your existing infrastructure requirements — we'd rather solve a real security constraint than paper over it.

Yes. We work with companies that have specific data residency requirements. Deployment can be scoped to meet those requirements depending on your region and compliance needs.

No — and this is usually the assumption that stops manufacturers from moving forward. You don't need everything written down first. We start with what exists and use the process of building the knowledge base to surface and structure the logic that currently lives in people's heads. Your engineers validate outputs, flag gaps, and correct mistakes. That's how the logic gets captured — not through documentation sessions before we start, but through a working system that gets refined over time.

Before any commitment to a full implementation, we run a scoped first phase: we review your existing product documentation and past proposals, map where the knowledge gaps are, and identify which use case has the clearest path to a working AI — whether that's internal Q&A, partner enablement, or RFP support. You get a concrete picture of what's feasible with your specific products, not a generic readiness report.

A consultancy will assess your situation, write a report, and leave. You own the recommendations but not a working system. We build the knowledge infrastructure specific to your products and make it live. You end up with something that works, not a document about what could work.

We don't hand over a finished product and disappear. The knowledge base needs to evolve as your products change, and outputs need continuous review and improvement. We stay involved — managing updates, monitoring accuracy, and expanding coverage as you add use cases.

Manufacturers of complex, technical products — where every order involves engineering judgment, application-specific configuration, or deep product knowledge to close. If they sell through a partner or distributor network, that's a strong fit. If they also sell directly to other businesses, equally so. The common thread: product expertise is both the competitive advantage and the biggest bottleneck.

Neurologik, Inc. is registered in Delaware, USA. Neurologik OU is registered in Estonia, EU. Our team works remotely across Europe and North America.