What's Under the Slab: How AI Trust Actually Gets Built in AEC

We get asked some version of this question on almost every call. Back in 2024 a customer asked us: "This is not an OpenAI, right? This is all just gathering information from what we've already inputted?"

Two years later, the question hasn't gone away, it's just gotten sharper. A pursuit specialist told us recently: "I built a context doc for Claude with everything about my firm including our projects, our people, our boilerplate. Why can't I just use that?" 

Both questions are manifestations of the same worry but in new form. First it was "is this really grounded in our stuff." Now it's "if grounding is all it takes, why do I need you at all."

Fair questions. In this piece, we try to give you real answers to both these questions, with evidence not just theories.

A spec sheet is frozen the day it's issued. Your firm needs an as-built.

That hand-built context doc is genuinely useful. It's also a spec that is only as current as the day you wrote it. When an engineer's PE license renews or a project hits substantial completion, nothing updates until someone remembers to go update that document. It scales to exactly one maintainer. Vacation could break it, and a resignation could retire it for good.

Also, it can't check itself. If your doc says Jon Smith and the 2021 resume says Jonathan Smith, the model takes your word for it and never clocks they're the same person, because it has nothing else to check against.

The gap here is the infrastructure. One approach is a well-organised spec. The other's an as-built: something that stays current, cross-checks its own records, works for your whole team, and shows its sources.

AI still makes things up. How can you keep that out of your proposal?

You've probably watched AI do well for a while and then, in the words of one CIO we work with, "do stupid things." You may also have personally experienced how sometimes AI can get a little “creative” (read: hallucinate), or sometimes just ghosts you.

They're right, and we're not going to pretend otherwise. Every system built on large language models can hallucinate. In most industries that's an inconvenience. But in an SF-330, one invented project citation straight up becomes a disqualification.

What actually reduces the risk? Outputs grounded in your firm's own uploaded content with citations that trace every claim back to its source, to help the human reviewing it before anything goes out the door.

One customer, on a demo call, put it better than we could: "We always say to have the caveat that it is AI, not a human brain." We'll sign our name under that. Kantiv is a first-draft accelerator, not a final-product generator. Anyone promising otherwise is promising something nobody can deliver yet.

That said, the right grounding can actually help you curb disqualifying errors. In fact, it made one of our customers (a large contractor group) very happy when they used Kantiv on a post-DQ bid and it caught a disqualifying issue the team had missed.

Grounding only holds up, though, if the records underneath it are actually reconciled, which is where most AI proposal tools fall apart.

The system has to know what's stale, and what's just spelled differently.

A CIO at a large A/E firm gave us his sharpest complaint about AI tools:

"AI keeps picking great resumes of people who don't work here anymore."

A tool that searches the pile as-is will find something. But it doesn’t necessarily tell you whether it found the right something, found it twice, or missed the version that would've won.

This happens because the same fact exists in multiple, disagreeing copies, and no tool can tell you which copy to trust. Search alone can't fix that. It just returns more copies faster.

Same root problem, different symptom: your firm's history was written by dozens of hands over decades, so the same person is Jonathan Smith on his resume, Jon Smith in a case study, and J. Smith in a 2019 SOQ. The same wastewater project shows up in four documents under three names with two different completion dates.

That's why before Kantiv retrieves anything, it reconciles. When documents come in, the platform extracts roles, timelines, and costs, then reconciles them at the people, project, and client record level. It works out whether records from different documents describe the same real-world thing. Exact matches first. Where the wording differs, similarity search shortlists candidates, then a second AI pass reviews that shortlist and renders a verdict with a confidence score. That match only goes through if the confidence clears a bar we set deliberately, and set differently for people, projects, and clients, because merging two engineers by mistake is a worse error than leaving two project records apart.

It doesn't stop at your own documents, either. Kantiv checks what it finds against your systems of record (which could be your CRM, Deltek or Vantagepoint, Unanet) so you're squaring that pile against the source of truth your firm already trusts, not just deduplicating PDFs. 

What comes out the other side is four connected records — people, projects, opportunities, companies. These are tied together by who worked on what, for which client, and how that maps to what you're chasing next. True institutional knowledge.

That connected record is what actually powers the platform day to day: it's the same data feeding Image Hub, RFP Insights, Smart Drafts, and every draft Kantiv generates.

This runs automatically and nobody has to approve each connection or merge one by one, which is the only way this works at the scale of decades of documents. But your team stays in control where it counts: they can merge, split, or correct any record the moment they spot an issue, and once someone verifies a detail, the automation is never allowed to overwrite it. The machine does the volume. Your people hold the red pen.

The payoff is a moment one proposal coordinator described after a skeptical colleague reviewed her work: "He was like, 'where did you get all of this?'" The answer is: from the firm. It was always there. The Kantiv platform simply helps surface the knowledge at the right time, in the right way.

Your structural PE shouldn't be reformatting the fee table.

The second question you’re likely to ask: which AI is actually doing the work, and what does it cost?

 Interpreting an RFP's requirements takes real reasoning strength. Summarizing 400 pages of supporting material is volume work, where speed matters more than depth. And so Kantiv routes each task to the model suited for it, the way you would staff a pursuit, because different jobs need different specialists.

The platform also keeps fallback paths ready, because your deadline week doesn't get pushed ahead just because AI models chose to have a bad week (remember how Claude made Fable off limits for bit?). And when a stronger model ships next quarter, your content just gets better without a rebuild.

This is also the real answer to that proposal manager's question from the top: why not just build it yourself. Because Kantiv doesn't just route between models, it ships the actual AEC workflows: RFP analysis, SF-330 filling, resume management, compliance checking, letter proposal drafting. You could vibe code a tool around a model this weekend. But someone still has to own prompt governance, permissions, and data freshness six weeks from now. That someone is us, not your already-stretched IT team.

On cost, we understand how confusing AI pricing can be with tokens getting used at record speed and running up unexpectedly high bills. Hence, most of Kantiv’s capabilities, including chat, are packaged within the platform fee you pay. The assurance is that you will be spared any end of month surprises.

Our customers didn't take our word for any of this. Good.

One CIO compared several AI proposal tools before landing here and told us Kantiv was "the best aligned to the industry and workflow." Another team ran their own five-week sandbox with a core team. They measured time savings, and scored  output quality before calling it a go and rolling out. Another one switched to us after having run a general-purpose proposal tool and finding that it lacked AEC-specific project management.

We’ve had customers document roughly 25% time savings per proposal and save 300+ hours per month. And a giant firm famous for building iconic theme parks couldn’t stop recommending us because their win rate ran around 90% using the platform for proposals. 

We won't guarantee you a win rate because that's not ours to promise. But firms using Kantiv are winning in more ways than one, and we don't think that's a coincidence.

Five questions real firms asked us. Your other vendors should hear them too.

Each of these questions came from an actual conversation with a firm sizing us up, and every one is worth asking anyone who wants your proposal data.

1. "Is this pulling from what we've uploaded, or is it generic AI making things up?" The first question we ever got, still the best one. A clear answer names the grounding, shows the citations, and admits where review is still on you. A muddy answer means the grounding is muddy too.
2. "How does it know Jon and Jonathan are the same engineer?" The hardest problem in this category, and the least discussed. A real answer describes the checks, the confidence bar, and how your team corrects a bad call. If the answer is "our AI figures it out," keep asking until someone can say how.
3. "Which model does which job, and what will a pursuit actually cost us?" One model doing everything is a red flag. So is a vague cost estimate or range.
4. "What happens to our work when a better model ships next month?" The right answer: your organized firm knowledge stays put, and the new model simply works from it. If the answer involves migration, retraining, or rebuilding, the vendor built their product on the model, and you'll rebuild every time the model changes.
5. "Show me every project where this person was the PM, for this client type, live." A content library returns text blocks for you to sort through. A connected system returns an answer. Sixty seconds in a demo settles which one you're looking at.

Want to know if your firm's running on a spec or an as-built?

A pile of documents just gets heavier every year nobody touches it. An as-built gets more accurate every time someone updates it.

Bring us your messiest pile — the boilerplate nobody trusts, the resumes with three spellings of the same name, the "FINAL_v2_USE_THIS_ONE" files. We'll show you what it looks like reconciled, live, on your own documents. No generic demo deck. If you've got questions before that — any of the five above, or ones we haven't thought of — send them our way. We'd rather answer them now than have you find out the hard way after you've signed with someone else.

FAQs

How do AI proposal tools for AEC firms actually work?

Most AEC AI proposal tools use retrieval-augmented generation (RAG), the AI pulls from your firm's uploaded documents (resumes, project profiles, SOQs, past proposals) to ground its answers in your content rather than generating from general knowledge. The quality depends on how well the system organizes and reconciles that underlying data before it retrieves anything.

Can AI write an SF-330 or federal proposal?

AI can draft significant portions of an SF-330 such as project descriptions, personnel sections, past performance write-ups, grounded in your firm's uploaded content. But federal proposals have strict compliance requirements. The right tool will generate citations for every claim so a human reviewer can verify against source documents before submission. AI accelerates the first draft; it doesn't replace the compliance check.

What causes AI hallucinations in proposals, and how do you prevent them?

Hallucinations happen when a language model generates text without enough grounding context where it fills gaps with plausible-sounding but incorrect information. Prevention starts with grounding every output in your firm's actual documents, adding citations that trace claims back to sources, and keeping a human in the review loop. The stronger the data reconciliation underneath (clean, deduplicated, current records), the lower the hallucination risk.

Is ChatGPT or Claude enough for AEC proposal writing, or do I need a purpose-built tool?

General-purpose AI like ChatGPT and Claude can help with immediate drafting tasks, but they don't connect to your firm's document library, don't reconcile duplicate records, and don't manage permissions or data freshness. A purpose-built AEC platform handles the data infrastructure, keeping project and resume data current, routing different tasks to the right models, and building in AEC-specific workflows like RFP analysis and compliance checking.

What is retrieval-augmented generation (RAG) and why does it matter for construction firms?

RAG is a method where an AI system searches your own documents for relevant context before generating a response, instead of relying only on what the model learned during training. For AEC firms, this means the AI references your actual projects, people, and clients and not generic construction knowledge. This is what keeps proposal drafts grounded in your firm's real experience rather than invented details.

How does AI handle duplicate or inconsistent records across proposal documents?

Firms accumulate decades of documents written by different people, so the same engineer might appear as Jonathan Smith, Jon Smith, and J. Smith across different files. A strong AI proposal platform reconciles these records before retrieving anything — using exact matching first, then similarity search to shortlist candidates, then a confidence-scored review pass. Human reviewers can merge, split, or correct any automated decision.

How much do AI proposal tools for AEC firms cost?

Pricing varies. General-purpose AI tools run $20–60/month per user but lack AEC-specific workflows. Purpose-built AEC proposal platforms typically range from $300–600/month and include content libraries and proposal generation. Some platforms package most capabilities into a platform fee and charge separately for agent-based tasks via credits, so you know the cost before running a job.

What happens to my firm's data when the AI model updates?

If a vendor built their product directly on top of a specific model, a model change could require migration or retraining. The better architecture separates your organized firm knowledge from the model layer where your reconciled people, project, and client records stay in place, and the vendor swaps in the newer model. Your output improves without any rebuild.