01
Re-answering questions you already answered.
Routine questions get a cited answer drawn from approved sources. Novel and high-risk questions route to a person, flagged as such.
01 / Engine
Answer engines
Your engineers answer the same questions all year. We can prove which ones a machine can answer safely.
The answers exist in your product manuals, spec sheets, and past support emails. Turning that into an answer engine is easy. Knowing whether to trust it is the hard part.
We test answer quality against your own experts' standards so you see the gaps before your customers do.
02 / After the demo
Everyone will show you a demo. Ask what happens after.
Every vendor in this category can build something that answers questions about your products. Most of them can demo it in a week on questions they chose from documents they picked.
Almost none of them will tell you where it fails.
Our first deliverable is an evaluation, not software. Your experts label real questions pass or fail. We score retrieval and answer quality against those labels, broken out by document type, and we hand you the list of what it still gets wrong.
In our evaluation work on a construction-products corpus, a conventional extraction approach transcribed 0 of 21 design-code cells from a decision flowchart correctly. The pipeline we built transcribed 21 of 21. Table lookups on the same corpus improved just as sharply. We could quote you a percentage for that one and we won't. The set was small enough that a percentage would flatter it. These are results from our evaluated extraction pipeline, not yet the live ingest path. We report them as evaluation figures.
We also found a 19-point retrieval gap in our own first build, and engineered it to zero. We tell prospects this on purpose. A vendor who has never found a gap that size in their own system has not looked.
The eight questions we would ask any vendor, including us, are on the evaluation page.
03 / The desk
What your team stops doing
02
Hunting for the current revision.
Answers cite the specific document and passage, so the person receiving it can verify without calling you.
03
Rebuilding a retired expert's reasoning.
Years of your team's own technical correspondence becomes searchable, attributable source material instead of an archive nobody can use.
04
Guessing whether the system is right.
Answer quality is scored continuously against your experts' standard, not assumed.
Every answer traces to a document you approved. When the sources do not cover a question, the system says so rather than reaching.
Every architecture for this ends with "engineer reviews the answer." Ours is the one that measures how often the engineer had to.
04 / Fit
Who this is for
Manufacturers between roughly $75M and $750M in revenue with an internal team that answers high-stakes technical questions from contractors, engineers, specifiers, distributors, and regulators. The answer has to be traceable to an approved source.
That team is usually called Technical Services, Applications Engineering, Product Support, or Technical Sales Support.
We work in building products and construction materials first, industrial and specialty materials second. We have delivered a measured answer engine for a manufacturer of passive fire protection materials, on branded product lines that carry strict trademark and naming rules.
If your technical library is mostly uncontrolled prose, or a wrong answer is cheap, this is not worth your money and we will say so.
05 / Start
How engagements start
Every engagement starts with a document review. Tell us about the questions, the approved sources, and how answers get out the door today. We identify what your library can support, where generic tools will fail, and what needs attention before anything is built.
The standard advice for this category is a 90-day pilot that ends in a working demo. Ours is six weeks. It ends in working software plus a measured evaluation against your experts' labels. That evaluation is a document listing what the system still gets wrong.
Most clients start here because it costs nothing and tells them whether to bother.
06 / FAQ
FAQ
What can AI do for a manufacturer's technical services or applications engineering team?
It can take the routine share of incoming technical questions and answer them with a citation to the approved source. Those questions are already answered in your manuals, submittals, test reports, and past correspondence. Every answer becomes traceable, and the novel questions get your experts' full attention.
How is this different from a general AI assistant or copilot?
A general assistant answers from what it learned during training. An answer engine answers only from your approved documents and cites which one. When your sources do not cover a question, it says so instead of reaching for something plausible.
How long does it take to get a working technical answer engine?
A six-week pilot produces working software plus a measured evaluation against your experts' pass/fail labels. Speed is the easy part of this category. Any vendor can promise it. Ask instead what the pilot will tell you about where the system fails.
What do we need to have in place before starting?
An identifiable set of approved documents and at least one technical expert willing to label a sample of real questions pass or fail. If your technical content is mostly uncontrolled prose, or a wrong answer is cheap, this is not worth the spend and we will tell you that.