AI Tender & Quotation Engineering
Turn a 200-page customer specification into a compliance matrix and a costed base design — in days, not weeks.
Transformer and switchgear manufacturers are living through a strange paradox: order books are the fullest they have ever been, lead times for power transformers stretch to three to five years — and yet the first battle of every project is still fought by a handful of quotation engineers reading utility specifications line by line.
Every enquiry arrives with 50 to 200 pages of customer and grid-operator requirements layered on top of IEC 60076. Someone has to find the deviations from your works standards, decide what is quotable, and build a base design with a price the sales team can defend. While that happens, the next three enquiries are waiting — and some never get answered at all.
We build AI-assisted tender engineering into your existing Siemens NX and Teamcenter landscape: large-language-model extraction of technical requirements, automated comparison against your design rules and reference projects, and a deviation list your engineers verify instead of write.
// The quotation bottleneck, in numbers
Your bottleneck is not the factory. It is the quotation desk.
Engineering-to-order is the defining trait of transformer and switchgear manufacturing: no two units are alike, because no two grid operators write the same specification. That makes the tender phase the narrowest point of the whole order pipeline — and the least automated.
The work itself follows the same pattern on every enquiry. Read the specification. Mark everything that deviates from IEC 60076 and from your works standards. Check whether a similar unit has been built before. Derive a base design. Estimate cost. Write the deviation list for the offer. It is skilled work — and 80 percent of it is reading, searching and transcribing rather than engineering judgement.
That is exactly the split AI is good at. The reading, structuring and cross-referencing moves to the machine. The judgement — what to accept, what to flag, what to price — stays with your engineers, who now start from a prepared deviation list instead of a blank page.
Requirement extraction from any tender document
PDFs, scanned annexes, utility standard sheets, Excel requirement lists — the AI reads them all and returns one structured requirements register: parameter, required value, source clause, page. Nothing is paraphrased; every entry links back to the exact passage in the original document, so your engineers verify in seconds instead of re-reading chapters.
LLM extraction · clause-level traceabilityAutomated compliance matrix
Every requirement is checked against your works standards, design rules and IEC 60076 baseline. Output: comply / deviate / clarify — with the reason attached.
IEC 60076 · works standardsReference design & pre-costing
The AI finds the closest units you have already built — in Teamcenter, order archives and post-calculations — and proposes a base design with a defensible cost frame.
Teamcenter · similarity searchEngineers decide. AI prepares.
No black box quotes anything on its own. The system prepares the deviation list, the evidence and the reference candidates — your quotation engineers review, correct and release. Every AI suggestion is traceable to a source clause, which is precisely what makes the output usable in a contractual offer. AI takes the routine; your engineers keep the judgement.
Human-in-the-loop · audit-readyFrom inbox to offer-ready: how the pipeline works
AI tender engineering is not one magic model — it is a pipeline of well-defined steps, each of which your team can inspect. We build it on your infrastructure (on-premises or EU cloud, your choice), connect it to the systems you already run, and tune it to the way your works standards are actually written.
Drop the tender package in, as it arrived
Customer specification, utility standard annexes, single-line diagrams, requirement spreadsheets — original formats, including scans. The pipeline handles layout, tables and multi-column PDFs; no manual pre-sorting.
Every technical requirement, isolated and sourced
The LLM extracts ratings, impedances, losses, temperature rises, test requirements, accessories and documentation obligations into one requirements register — each entry with clause and page reference back to the source document.
Compliance matrix against your rules, not generic ones
Requirements are checked against your works standards, design guidelines and the IEC 60076 baseline. The result is the deviation list your engineers used to build by hand: comply, deviate with reason, or clarify with the customer.
Closest reference units from your own history
Similarity search across Teamcenter and order archives surfaces the nearest units you have already built — including post-calculation data. Engineers start from a base design and a cost frame, not from zero.
Your engineers verify — then the offer goes out
Everything lands in a review view: accept, correct, annotate. The released result exports to your offer template, Teamcenter and, if wanted, SAP. The engineer signs off; the AI never quotes on its own.
When does AI tender engineering pay off?
This is not a tool for everyone. It pays off where enquiry volume meets specification complexity — which is precisely the situation of mid-sized transformer, switchgear and generator manufacturers right now. You will recognise yourself in most of these:
- Your quotation engineers spend days per enquiry reading and transcribing specifications before any engineering starts.
- You decline or delay enquiries because the quotation desk is at capacity — while the market is willing to buy everything you can build.
- Deviations from customer specs are found late — sometimes at design review, sometimes at acceptance testing, always expensively.
- Your most experienced engineers carry the works-standard knowledge in their heads, and several of them retire within the next five years.
- You run Siemens NX and Teamcenter (or a comparable PLM) and want AI that plugs into that landscape instead of replacing it.
What the pipeline plugs into
Built around the systems and standards a transformer works already runs on — not around a new silo.
Before and after: the same enquiry, two ways of working
The point of the system is not to produce nicer documents — it is to change where your engineers’ hours go. Here is what the same 160-page utility enquiry looks like on both sides of the change:
Before — manual
- Spec read & requirements transcribed by hand — 2–4 days
- Deviation hunt across works standards from memory — 1–2 days
- "Have we built this before?" — asking colleagues, browsing folders — ½–1 day
- Missed deviation surfaces at design review — rework, margin loss
- First response to customer after 2–3 weeks
After — AI-assisted
- Requirements register generated with clause references — < 1 hour
- Compliance matrix pre-filled; engineer verifies — ½ day
- Closest reference units ranked from Teamcenter & archive — minutes
- Deviations visible on day one — priced in, not discovered later
- First qualified response in days — and capacity for the next enquiry
Why inteliScience
Generic AI consultancies can build you a document chatbot. What they cannot do is read a transformer specification the way your engineers do — because that requires knowing what an impedance tolerance or a temperature-rise clause means for the design downstream.
We come from the Siemens NX and Teamcenter world. Our engineers build NXOpen automation, Teamcenter customization and engineering AI for manufacturers every day. That means the tender pipeline does not end at a nice PDF summary: extracted requirements become Teamcenter attributes, reference matches point to real items in your PLM, and the base design proposal is something your NX constructors can actually open and continue.
And because energy and industrial clients often cannot ship engineering data to US clouds, every component of the pipeline runs on-premises or in an EU cloud — your specifications, your design rules and your cost data never leave your infrastructure.
The same evidence-first approach works beyond transformers: for any engineer-to-order enquiry, our AI similar project search finds the technically closest past projects, explains why each one matches and links the source passages.
Fixed price — no day rates, no surprises
Tender AI Pilot
- Your last 3 real tender packages, processed end-to-end
- Requirements register + compliance matrix against your works standards
- Reference matching demo on your Teamcenter / order archive data
- Accuracy report reviewed with your quotation engineers
- Deployment blueprint (on-prem / EU cloud) + business case for rollout
4 weeks · runs on your data · results belong to you, whatever you decide afterwards.
For comparison
Doing it in-house
- Hire or divert AI engineers who have never read a transformer spec
- Build document parsing, extraction and PLM integration from scratch
- Learn the failure modes on your own tenders, in production
Frequently asked questions
Does our specification and cost data leave our infrastructure?
How accurate is the extraction — can we trust a compliance matrix from an LLM?
What document formats and languages can it handle?
How does it connect to Teamcenter, NX and SAP?
Our works standards are partly tribal knowledge. Does this still work?
Can we start small before committing to a rollout?
The market wants to buy everything you can build. Stop losing orders at the quotation desk.
Send us one anonymized tender package — we'll show you what the extraction and compliance matrix look like on your own specification.
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