Operational AI infrastructure for quoting, supplier operations, market intelligence, field service, and engineering knowledge — built around how your business actually runs.
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We have mapped operations at industrial OEMs, contract manufacturers, and global suppliers. The specifics change. The friction points do not.
A buyer requests a quote at 9pm. The CSR calls at 11am, leaves a voicemail, emails the rep, who emails the engineer, who emails the buyer back two days later asking for specs. The deal cools while the chain finishes its loop.
PDFs, spreadsheets, drawings, hand-marked photos, customer-portal exports — every customer has a different format and every internal team re-keys the same dimensions into ERP, MES, and the quote tool.
Competitor moves, trade journals, regulatory shifts, supplier news — the manager assembles a slide deck once a quarter from whatever Google Alerts coughed up. By the time leadership reads it, the move has already happened.
Lead times, quality issues, OTD, claims — tracked in email threads with whoever responds first. Performance reviews happen annually. The chronically late supplier keeps getting purchase orders.
Field reports, parts pulls, customer signatures, completion notes — captured on a clipboard, transcribed at the truck, lost between truck and office. Warranty claims and parts forecasting get the leftovers.
The senior engineer who knew which valve fits which line, which alternate part the customer accepts, which assembly drawing is current — retires. Nothing is written down in a system the next hire can search.
Before we talk about AI, we map the machine. Every manufacturer we work with starts here — the four operational surfaces every order, unit, and customer touches, and the work that happens on each.
From inquiry to bound quote. Where lead time wins business — or kills it.
Where promises become parts. The supplier and production layer that defines OTD and margin.
What happens after the unit ships — and decides whether the customer is a one-time buyer or a fleet account.
Everything that turns activity into insight — competitors, regulations, customer trends, internal expertise.
We do not believe in an "AI engineer." We believe in an AI operations layer that takes the predictable, repetitive, system-to-system work off your team so your engineers and operators can spend their time on design, quality, and customer relationships.
Sent quotes, issued POs, accepted warranty claims, signed certifications. The AI parses, configures, retrieves, and surfaces — your engineers, buyers, and quality leads decide and sign.
Six systems we have deployed in production at manufacturers. None of them are chatbots in the marketing sense. All of them are operational infrastructure that connects the tools you already use.
Inbound inquiries are qualified, specs are parsed from any format, and a draft quote is on the partner's desk within the hour — not the week.
OTD, quality, NCR, and claim performance tracked per supplier across the portfolio — bad performers get the call, good ones get more business.
Trade journals, competitor sites, regulatory feeds, and community discussions monitored continuously — delivered as a newsletter, an intranet chatbot, or both.
Voice + photo capture from the technician on the truck becomes a structured job report — warranty claims, parts pulls, and customer signatures all linked to the unit.
Sales reps, technicians, and customers retrieve the right part, the right drawing, the right alternate — in seconds, with citations to the source drawing.
Signals from service, sales, distributors, and complaints get clustered into product and operational insights — feeding engineering, marketing, and ops, not just a quarterly slide.
This is what the first thirty minutes of a new RFQ look like once the operational layer is in place. No CSR backlog, no engineer email tag, no three-day lag.
Diagram is illustrative. Production traces include retries, fallbacks, and human-checkpoint pauses not shown here.
We do not ask manufacturers to migrate. We build the operational layer on top of the systems you have already invested in — your ERP stays the system of record, your PLM stays the engineering source of truth, and the AI lives in the seams between them.
We do not replace your ERP, PLM, or service system. They stay systems of record. The operational layer is additive.
Every AI write is timestamped, attributable, and reversible. Quality teams, auditors, and customers see a clean trail.
Legacy ERP, in-house quoting tools, distributor portals — we integrate where your work already lives.
Not a feature, not a chatbot, not a magic button on a marketing page. The work it does is the same work your team has always done — moved into a system where it runs reliably.
Manufacturing is a quality business. If a system is not measurably more accurate than your current process, we do not ship it. We measure tolerance fit, OTD, and quote accuracy continuously.
The best AI system is the one that disappears into the plant's actual workflow. If your team has to change how they work to use it, it is the wrong system.
Design decisions, tolerance disposition, supplier selection, customer escalations — all go through a person. The AI parses, configures, retrieves, and surfaces.
How an industrial OEM deployed an AI market scouting agent that monitors trade journals, competitor sites, and community discussions across 15–30 sources — delivered as a monthly executive newsletter and an SAP-integrated chatbot.
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