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Materials Prep · add-on
Metallogic AI logoMetallogic AI

Recipes, etchants, and defect diagnosis from inside every sample.

Mai is an AI prep assistant for metallography labs. It draws on the PACE Metallographic Handbook, your lab's own recipe library, and a 2,700-entry etchant catalog to answer the questions a metallographer would otherwise ask a senior colleague.

  • $49/month per organization
  • Available on every plan, including Free
  • Cancel from the customer portal anytime
materialsprep.com / sample / SAMPLE-417
Mai responding inside Materials Prep with a recommended preparation recipe for AA 6061-T6, including a step-by-step procedure table and natural-language explanation.
What Mai handles

The questions a metallographer asks every day.

What's the right prep for AISI 4140 Q&T at HRC 42?

Recipe recommendations

Mai drafts a complete ladder — section, mount, grind, polish, etch — sized to the sample, the analysis goal, and what your lab already stocks. Saves to your recipe library and links to the active sample in one click.

Best etchant to reveal grain boundaries in 2024-T3?

Etchant lookups

Mai searches the PACE catalog of about 2,700 etchants by material, alloy, scale, and method. Returns composition, procedure, and what each etch reveals.

Why does my edge look like this? [photo attached]

Defect diagnosis

Attach a photo of edge rounding, comet tails, pull-out, or smearing. Mai identifies the failure mode and proposes a corrected ladder grounded in the handbook's defect catalog.

Built on PACE knowledge

Twenty-five years of metallography knowledge, distilled.

Mai is built on the PACE Metallographic Handbook, written by Donald Zipperian, Ph.D., PACE's Chief Technical Officer. Plus standard metallography practice across material families from carbon steels to refractory alloys to magnesium.

  • Recommendations bias toward gentler, lower-damage prep — PACE's philosophy.
  • Honest about uncertainty: hedges when answering outside the handbook's coverage rather than fabricating parameters.
  • Vendor-neutral: recommends prep characteristics (pad class, abrasive size, force, time) so the recipe works with whatever consumables your lab stocks.
  • Reads your sample, your inventory, and your recipe library on every conversation. Recommendations align to what your lab can actually run today.
What Mai knows
Etchants in the catalog
~2,700
Material families covered
80+
Years of PACE bench work
25+
Defect modes catalogued
20+

Steels, cast irons, stainless, aluminum, copper alloys, titanium, magnesium, superalloys, refractory metals, coatings, weld zones, ceramics, composites, electronic materials — covered in detail with prep ladders and etchant matches.

Inside your workflow

Mai opens from the sample you're already on.

No separate app, no separate window. The drawer slides in over the sample you're looking at, with the sample's material, hardness, mount, and goal already loaded.

01

Pre-loaded with context

Your sample's material, alloy, hardness, mount, goal — plus your lab's owned consumables and existing recipes for similar materials. Mai never asks for what's already in the database.

02

Answers, not preambles

Direct, technical, on-brand. Specific parameter ranges (force, time, RPM, grit), not vague generalities. Tables for procedural data. Defect-watch callouts on every recipe.

03

Save and apply, in one click

When Mai drafts a recipe, save it straight to your library — and optionally link it to the current sample so the next prep step pre-fills from the recipe ladder.

One price. Any plan.

Add Metallogic AI to any Materials Prep plan, including Free. One flat rate per organization with a generous monthly conversation budget.

$49/month
Per organization · USD · Cancel anytime
  • Recipe recommendations on every sample
  • PACE etchant catalog lookup
  • Photo-based defect troubleshooting
  • Save and apply recipes in one click
  • Available on every plan including Free
Get started free

How your data is handled

When you use Mai, the content of your messages, attached images, and your sample's metadata (material, hardness, mount, recipe history) are sent to Anthropic for processing and returned. We do not use your prompts to train models, and Anthropic's data processing terms apply to the request-response cycle. Mai's recommendations are advisory; validate any recipe before using it for QA-graded work.

Read the full privacy disclosure