<\/p>\n
If you are weighing Flikt.AI against simply uploading your plan set to ChatGPT, the honest framing is this: ChatGPT is a genuinely capable document reader, and for a lot of construction paperwork it is the right tool. Plan review is not one of those things. Not because the underlying model is weak, but because a coordination conflict is not a fact stored on a page — it is a contradiction between two pages that were drawn by different consultants, at different scales, weeks apart. Finding it is a cross-referencing problem, and a chat window is not built to be a cross-referencing engine.<\/p>\n
Everything below applies equally to ChatGPT, Claude, Gemini, Copilot<\/strong>, and any other general-purpose AI assistant. None of the limitations here are about a particular vendor’s model quality — they are about what a conversational assistant is shaped to do versus what reviewing a construction document set requires. Where we say “a chatbot” below, read it as the category.<\/p>\n Ask a general-purpose assistant a question about a drawing and it will answer from what is in front of it. That works well when the answer lives on the sheet: what is the specified glazing, what does note 7 say, summarize this spec section.<\/p>\n A coordination conflict does not live on the sheet. A 26×10 supply duct on M-2 is perfectly correct. The concrete beam dropping below the slab soffit on S-1.1 is perfectly correct. The conflict only exists when you hold both against each other and notice that nobody drew a section showing the duct clearing the beam. Neither sheet is wrong on its own. There is no sentence anywhere in the set that says “these collide.”<\/p>\n That is the job Flikt.AI does: it tags every sheet by discipline, builds a cross-reference of the whole set, and checks each discipline against every other — architectural, structural, mechanical, electrical, plumbing, fire protection, fire alarm, low-voltage, telecom, civil, landscape, interior design, food service, signage, zoning, and specifications — in a single pass. It is looking for contradictions between documents, not answers inside one.<\/p>\n A modest commercial set is 60 to 200 sheets of large-format, graphically dense drawings. A chat session takes them in as a conversation, and the practical result is that the earliest sheets stop being available with full fidelity by the time you reach the later ones. You can work around it by feeding sheets in batches, but then you have re-created the exact problem you were trying to solve: nothing is being compared across the batch boundary, which is precisely where coordination conflicts hide.<\/p>\n A general assistant asked to “find problems” will return a list. It has no opinion about which of those items would stop a pour and which is a dimensioning nit, because it has no model of what a field conflict costs. A reviewer handed 200 undifferentiated observations reads none of them. Ranking is not cosmetic — deciding what does not<\/em> matter is most of the work.<\/p>\n Fluent prose about a possible clash is worthless if you cannot check it. Without an enforced citation format you get plausible-sounding findings with no sheet number attached, and the burden of verification lands back on your PM — who now has to search the set to find out whether the AI was right. That is slower than not asking.<\/p>\n Run the same prompt against the same set on Monday and Thursday and you will get different lists. That is fine for drafting an email. It is not fine for a deliverable you are going to circulate to the design team, and it makes it impossible to tell whether a finding disappeared because it was resolved or because the model simply did not surface it this time.<\/p>\n Every plan-review system produces false positives; the question is whether anyone is systematically hunting them. Flikt.AI treats false-positive suppression as an engineering discipline — findings are checked against the drawing they cite, and patterns that produce noise get gated. A general-purpose chatbot has no feedback loop from your industry at all.<\/p>\nThe core difference: reading a document vs. cross-referencing a set<\/h2>\n
What actually breaks when you upload a plan set to a chatbot<\/h2>\n
1. The set is too big to hold at once<\/h3>\n
2. There is no severity model<\/h3>\n
3. Nothing forces a citation<\/h3>\n
4. Ask twice, get two different answers<\/h3>\n
5. Nobody is tuning the false positives<\/h3>\n
Side by side<\/h2>\n