An honest comparison.
Most of the tools people mention alongside GRADE do a different job. This page says which job each one is for, where GRADE is genuinely the wrong choice, and how to check the difference yourself. No logos, no all-green checkmark table — just what is true.
Three approaches, one honest table
Not named companies — categories of approach. Where another approach genuinely wins, it says so.
The distinction that carries the whole table: retrieval finds the passages most similar to a question; enumeration checks every product and reports the count. On a catalogue of 300 products, “we found some good matches” and “we evaluated all 300, here are the 4 that qualify and why the other 296 do not” are different claims. See how we verify the guarantee →
Different tools, different jobs
The fastest way to compare is not feature by feature — it is by what the tool is for. Here is where the ones we hear about most sit.
Marketplaces
SpecialChem, Plastics.com
Serve the buyer, across many suppliers. They help a formulator shop the market. That is the other side of the transaction from us. If you are looking for products to buy, use them.
Knowledge search
Nesh, Allchemist
Find documents and answer questions across enterprise content. Broader than GRADE and useful for general search over everything a company knows. If you need enterprise knowledge search, we are not it.
Outbound prospecting
Corvus
Find accounts and cross-sell opportunities from CRM and sales data. That is a different moment in the sale — reaching out — than answering an inquiry that already arrived.
General assistants
Microsoft Copilot, ChatGPT
Excellent at summarising a document you have already found, and at drafting. If your catalogue is small enough that a person can hold it in their head, a general assistant may be all you need.
GRADE
Screens your catalogue against one inquiry's stated constraints, and puts the exact source line behind every decision — including every product it ruled out, and why.
The architectural difference
This is the part that actually matters, and it has nothing to do with who has more features. It is about how the two approaches work.
Retrieval — how a general assistant answers over your documents — finds the chunks of text most similar to a question and answers from those. It is very good at that. But it does not enumerate a catalogue: it fetches what looks relevant, not every record, so "did you check all of them?" is not a question it can answer.
That gap is sharper for data sheets, because data sheets are mostly tables. Published research on retrieval over structured enterprise data notes that conventional pipelines struggle with tabular content and have to work to preserve the row-column integrity that flattening a table into linear text loses — and those row-column relationships are exactly what a spec screen depends on (Cheerla, 2025).
Missing information is a recognised, measured failure mode, not a hypothetical: Microsoft's own Azure AI evaluation SDK ships a "Response Completeness" evaluator specifically to score how much required information a response leaves out (Microsoft Learn).
GRADE takes the other path. We extract each data sheet into typed fields once — pH as two numbers, a VOC ceiling as a number, a certification as a boolean — and then filter those fields deterministically with a database query. Every product in your catalogue is evaluated against every hard constraint, and we report the count: evaluated 312 of 312. It is not more intelligent than retrieval; it is a different operation, and it is the right one for "which of my products meet these requirements."
We are describing a difference in approach, not claiming these tools are bad — for the jobs above, they are good. We are also not claiming completeness is the only thing that matters, only that it is the thing a spec screen must get right.
When not to use GRADE
If any of these is you, GRADE is the wrong tool, and we would rather say so now.
- Your specifications are not published in data sheets. If the numbers only exist in someone's head or an internal system, we cannot screen on them.
- You need field service, formulation development, or lab work. We answer inbound inquiries. We do not visit plants.
- Your inquiries are relationship-led rather than specification-led. If customers call and ask "what should I use," with no stated requirements, there is nothing to screen against.
- A general assistant already handles your catalogue well enough. Smaller catalogues sometimes do not need this.
This is not about team size. A small team gets real value from GRADE too.
Run the comparison yourself
Do not take our word for any of this. Take one real inquiry you have answered before. Ask a general assistant to answer it against your own data sheets. Then run the same inquiry through GRADE. Compare which products each one found — and whether either can tell you it checked all of them.