Case Studies
Real engagements, measurable outcomes. Here is how FuturAIQ has helped operations-driven companies move from manual processes to intelligent systems.
Building a Predictive Maintenance System for Industrial Equipment: Our Reference Architecture
Industrial operations teams sit on two things at once: equipment that's expensive to let fail, and technical documentation that's hard to search when something goes wrong. A maintenance engineer facing a fault code at 2am doesn't have time to read 400 pages of manuals. The symptoms they're seeing need to match against specifications, fault-code tables, and historical maintenance logs — fast, and with reasoning they can verify. Most predictive maintenance tools either oversimplify ("this pump will fail in 14 days") or dump raw retrieval results on the user. Neither is how engineers actually work. They need a system that retrieves the right context, reasons through it like a senior technician would, and flags the severity clearly enough that a work order gets created automatically when it matters.
Extracting Ducts from HVAC Drawings: A Vision-LLM Reference Build
Facilities teams, MEP consultants, and industrial project managers spend hours reading mechanical drawings. Before any retrofit, compliance review, or equipment change, someone has to go through the drawing and manually count ducts, classify them by pressure, and flag the high-pressure runs that need special attention. On a complex floor plan, this is a full afternoon of work, and it has to happen before anything else moves. The obvious solution is "throw a vision model at it." The reason this doesn't just work is that general-purpose vision LLMs don't know what matters on a technical drawing. They see lines and text, but they don't know that a 14" round duct running from a kitchen hood is a HIGH pressure classification that triggers different fire-code requirements, or that a branch runout under 10" is a LOW pressure concern you can batch-review. Without domain encoding, the output is either too generic to trust or too verbose to use.
Agentic AI for Operational Tools: A Reference Pattern with Human-in-the-Loop Safety
Operations teams spend their days inside tools — CRMs, ERPs, ticketing systems, document review apps, internal dashboards. Most of that time is spent doing things the tool already supports, just through too many clicks. "Find all open tickets from last week assigned to Priya." "Compose a reply to this vendor saying we need another week." "Pull the three highest-value deals that closed this quarter." Each of those is a single sentence in English and a five-minute journey through the UI. The obvious fix is "put a chatbot on top of it." The reason this doesn't work in practice is that most AI integrations stop at answering questions. They can tell the user what the ticketing system says. They cannot change what the ticketing system says. And the moment an agent can take real actions — send an email, update a record, close a ticket — the safety question gets serious. A hallucinated send in a support inbox is worse than a hallucinated answer in a chat window. What operations teams actually need is an agent that can take real actions on real tools, with a confirmation layer that stops it before anything destructive goes through. Not "AI that describes your work," but "AI that does your work, with your approval at the points that matter."
Resume Intelligence: A Reference Pipeline for High-Stakes Document AI
Most AI applications are one LLM call dressed up in a UI. For straightforward tasks, that's fine. For anything consequential — where the output gets trusted, acted on, or submitted somewhere it matters — a single-call design fails in three predictable ways. It hallucinates details that look plausible but aren't in the source document. It produces inconsistent outputs that vary between runs even when the input is identical. And it has no way to catch its own mistakes, because there's no validation step after generation — the first version is also the final version. These failure modes are tolerable in a chatbot. They are not tolerable when AI is analyzing a contract, generating a compliance document, scoring a proposal, or producing any output that someone will act on without double-checking. High-stakes document AI needs a pipeline, not a prompt. The question we set out to answer with this reference build: what does a production-grade document AI pipeline actually look like, and can we prove the architecture end-to-end?
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