A practical buyer’s guide for architects, engineers, and AEC leaders who want speed without risking compliance
AI has officially entered building code research.
And if you’re an architect, MEP engineer, or technical leader in an AEC firm, you’ve probably already tried it. Maybe with high expectations. Maybe with skepticism. Maybe with both.
Some AI tools feel helpful immediately. They pull up relevant sections faster than you can search manually. They summarize dense language. They turn code into something readable.
But if you’ve been burned even once by an AI-generated compliance answer, you know the problem: building codes are not a “text search” problem. They’re a reasoning problem. And in real projects, “mostly right” can still be dangerously wrong.
So if you’re evaluating AI code tools for your team, here’s the uncomfortable truth:
The best AI code tool is not the one that sounds smartest. It’s the one you can trust.
This article lays out a practical framework to evaluate AI code tools in 2026, based on what matters in real projects: defensibility, transparency, and workflow fit.
Step 1: Know what category you’re actually buying
Before comparing vendors, clarify what kind of tool you want. Most products fall into one of two categories.
Category A: AI on top of a code library
These tools are excellent at:
- finding relevant sections
- summarizing code language
- speeding up navigation
They often feel like a “Copilot” layer on top of a digital library.
They can be useful for everyday lookup and faster reading, but they often struggle when:
- the answer depends on multiple codebooks
- exceptions override the base rule
- local amendments change triggers
- calculations or project facts determine compliance
Category B: AI built for code reasoning
These tools aim to behave more like a code expert:
- interpret project context
- follow definitions and triggers
- evaluate exceptions
- connect multiple sections across documents
- show the reasoning chain clearly
This is the category that can actually change workflows, not just speed up search.
Step 2: Use this 10-point evaluation checklist
Here’s the checklist I recommend using when evaluating AI code tools. It’s designed for real AEC environments, not demos.
1) Does it support project context (not just questions)?
Most compliance questions depend on project facts:
- occupancy
- construction type
- sprinkler status
- stories and height
- governing agency and permit date
If a tool doesn’t explicitly use project context, it will give generic answers. Generic answers are where mistakes begin.
Buyer test:
Can you create a project profile and have the system consistently use it?
2) Can it handle multi-document reasoning?
Real answers rarely live in one section.
A corridor question might require:
- IBC egress rules
- fire-resistance requirements
- occupancy separation logic
- referenced standards
- local amendments
Many AI tools cite a section, but fail to connect the dependency chain.
Buyer test:
Ask a question that requires at least 3 sections across different chapters, plus one referenced standard.
3) Does it understand exceptions as first-class logic?
Exceptions are not footnotes. They are governing rules.
A lot of AI systems give the base requirement and stop. In real practice, the exception is often the whole point.
Buyer test:
Ask a question where the correct answer is primarily determined by an exception. See if it finds and applies it.
4) Is it amendment-aware (and not just “mentions amendments”)?
This is a big one.
Many tools “include amendments” by retrieving them as text. But amendments often change triggers, thresholds, and logic.
If the AI does not treat amendments as logic changes, it will still get the answer wrong.
Buyer test:
Pick a local jurisdiction scenario where an amendment changes a threshold or requirement and see if the tool applies the amendment correctly.
5) Can it show the reasoning path step by step?
This is where most tools fail.
A citation is not reasoning.
A real compliance answer needs:
- assumptions stated clearly
- definitions applied
- triggers evaluated
- exceptions checked
- final conclusion derived
Buyer test:
Can you audit how the tool arrived at the answer, and would you feel comfortable defending it in a plan review meeting?
6) Does it provide citation quality that holds up under scrutiny?
“Cites code sections” is not enough.
You want citations that:
- match the exact claim being made
- include relevant subclauses
- don’t cherry-pick partial text
- stay consistent across follow-up questions
Buyer test:
Challenge the tool: “Show me the exact clause that supports this requirement.” See if it can.
7) Does it capture assumptions and edge cases?
Compliance is full of assumptions.
The best tools explicitly state:
- what they assumed
- what conditions would change the answer
- what information is missing
Buyer test:
Ask an ambiguous question. A strong tool should respond with conditional logic, not a single confident answer.
8) Can it generate reusable compliance artifacts?
The biggest waste in code research is repetition.
If your team solves the same question every project, you’re paying the “code tax” repeatedly.
A modern system should help produce:
- reusable checklists
- standardized compliance notes
- shared interpretations
- project-specific decision logs
Buyer test:
Can you convert research into a checklist or documentation artifact that can be reused across teams?
9) Does it build institutional memory over time?
The best firms do not just answer questions. They compound knowledge.
If the tool can store project history, link decisions, and retain context, it becomes a firm asset.
Buyer test:
Can the tool retain prior reasoning and reuse it for future projects with similar conditions?
10) Does it integrate into real workflows?
This is not about fancy integrations.
It’s about whether the tool fits how teams work:
- fast enough for active design cycles
- reliable enough for compliance
- shareable across project teams
- structured enough for training juniors
Buyer test:
Would your team actually use it daily, or would it become another “innovation tool” that sits unused?
Where the market is heading: search tools vs reasoning systems
In 2026, code software is splitting into two lanes:
- Code search and libraries (still essential)
- Code reasoning and compliance intelligence (the next layer)
UpCodes remains one of the strongest products in the library category. It’s excellent for online access, navigation, and search.
But if you’re evaluating AI code tools specifically, the bigger question becomes: are you buying a summarizer, or a reasoning engine?
Why MeltPlan is the strongest contender in AI code research centered around reasoning
From what I’ve seen, MeltPlan’s Melt Code stands out because it is built around the hardest requirement in this category:
defensible, auditable code research and reasoning.
It goes beyond “answers + citations” and focuses on:
- project-aware research
- multi-code and amendment-aware logic
- transparent reasoning paths
- reusable workflows through projects and checklists
- organizational memory (so knowledge compounds)
In other words, it behaves like a code expert workflow in software, rather than a chat overlay on a library.
Final recommendation: run a real-world bakeoff
If you’re buying this for your firm, don’t run a demo evaluation.
Run a bakeoff.
Pick 10 real questions from past projects:
- mixed occupancy edge cases
- egress calculations
- accessibility conflicts
- amendment-driven requirements
- ambiguous scenarios that required senior review
The best tool will not be the one with the nicest UI.
It will be the one that produces answers you can defend.
