Manufacturing Chatbot Development Company

Overview

The plant floor generates more data than anyone can use, and reaching it usually means walking to a terminal and navigating an ERP interface designed in 2004 by someone who never had to use it wearing gloves.

FoundrySoft is a manufacturing chatbot development company. We build conversational interfaces that connect operators, maintenance techs, floor managers, and executives to the systems that already hold the answers, in the language they actually use.

Why manufacturing chatbots fail, and what we do differently

Manufacturing is one of the least forgiving environments for a chatbot, for three reasons that generic vendors consistently underestimate.

A confident wrong answer costs real money here. If a bot tells a technician the torque spec is 40 Nm when it is 55, you have a warranty claim or an injury, not a bad user experience. So we build for refusal: every answer is grounded in a retrieved source document, the bot cites the manual and revision it drew from, and when retrieval returns nothing relevant it says it does not know instead of generating something plausible. For safety-critical procedures we hard-gate the bot to verbatim citation only.

Your data is in systems that resist integration. SAP, Oracle, an MES from a vendor who was acquired twice, a historian database, and a spreadsheet on someone's desktop that is load-bearing. The chatbot is the easy part. The work is the middleware: read-only service accounts, query allowlists so the model cannot construct an arbitrary database call, row-level permissions so a contractor sees only their line, and caching so a chatty bot does not put load on a production ERP.

The floor is not an office. Gloves, noise, poor lighting, spotty Wi-Fi in the far bay, and shared devices. We design for voice input where hands are busy, oversized touch targets, offline-tolerant behaviour that queues and syncs, and a device handoff model that does not leak one operator's session to the next.

What we build

Machine maintenance and troubleshooting assistants

A retrieval system over your machine manuals, maintenance logs, SOPs, and past work orders. A technician gives an error code and gets the diagnostic tree, the parts likely needed, and the last three times this fault occurred on this asset with what fixed it. This is usually the highest-return first project, because the knowledge is already written down, the value of finding it faster is easy to measure, and the failure mode is bounded.

ERP and inventory queries in natural language

"How many units of part 4471-B are in the west warehouse, and when does the next shipment land?" The bot resolves that to a parameterised query against an allowlist, runs it under a read-only account with the asking user's permissions, and returns the answer with a timestamp and a link to the underlying record so a manager can verify it.

Supply chain and production analytics briefings

Scheduled agentic workflows that examine shipment delays, material shortages, scrap rates, and line throughput, then push a short written briefing to the people who need it. This is where agents genuinely earn their cost, because the work is reading a lot of boring data on a schedule and noticing what changed.

Quality and compliance documentation support

Drafting deviation reports, retrieving the applicable revision of a work instruction, and checking a batch record against its specification, with a human approving every output that touches a regulated record.

On-premise and air-gapped deployment

Process IP is often the most valuable thing a manufacturer owns, and for many plants sending it to a third-party API is not a policy question that has a yes available.

We deploy the full stack on your own hardware when that is the requirement, running open-weight models locally so no prompt, document, or query leaves your network. The tradeoff is real and we will be straight about it: a locally hosted open-weight model is meaningfully less capable than a current frontier model, which usually means narrowing the bot's scope and leaning harder on retrieval quality. For plants that can use a hosted API, we deploy against current frontier models such as Claude Opus 5 or Claude Sonnet 5 with a zero-retention configuration, which is often an acceptable middle path. We help you work out which side of that line your data sits on. Our engineering team has written up the tradeoffs in self-hosting a coding LLM with vLLM and choosing among open-weight models for agents.

Start narrow

The manufacturing chatbot projects that succeed start with one line, one document set, and one measurable question, then expand once the retrieval quality is proven. The ones that fail start with "an assistant for the whole plant." We scope the first engagement deliberately small, because the fastest route to a plant-wide rollout is a first deployment that maintenance staff actually trust.

Let's talk about which line to start on.