Turn decades of materials data into decisions your engineers can ship.
Lattice Graph ingests your lab notebooks, characterization files, and supplier specs — and returns the one composition that meets your constraints. No more searching through PDFs.
σy = 503 MPa
Built for teams who ship atoms, not just apps.
What our platform is designed to deliver — from aerospace alloys to battery cathodes to next-generation ceramics.
Three steps from fragmented data to engineering-grade answers.
No data-science team required. Lattice learns your naming conventions, infers missing metadata, and surfaces provenance for every recommendation.
Ingest messy data as-is
Drop in 40+ formats — lab notebook PDFs, instrument exports, supplier CSVs. No schema mapping required.
Auto-unify into a graph
Lattice links compositions, treatments, and outcomes into a living knowledge graph — with every edge traceable to source.
Query in plain language
Ask "which Mg alloys survive 400°C creep at under $9/kg?" — get a ranked shortlist with confidence intervals.
Problems we hear about in every first call.
Lattice is purpose-built for the decisions materials engineers actually make in the week before a design review.
Shortlist candidate alloys against multi-objective constraints.
Trade off yield strength, density, supply risk, and unit cost on the same plot. Pareto fronts update live.
Find the closest prior failure in seconds.
Paste a micrograph or spec — Lattice returns similar prior cases from your own archive and the open literature.
Dual-source before procurement asks.
Automated spec matching across 28 supplier catalogs.
RoHS & REACH sanity checks.
Flag restricted substances before BOM lock.
Heat-treat optimization.
Recommend cycles from prior runs that hit your spec window.
Inverse design with targeted properties.
Specify the property profile you need — Lattice proposes compositions with uncertainty bounds grounded in your own data.
Export to your existing PLM and ELN.
One-click sync to Teamcenter, Windchill, LabArchives, and whatever CSV lives in your shared drive.
Familiar tools, purpose-built for materials.
Built for engineers, not data scientists. Use it in the browser, or pipe it into your existing notebooks via the SDK.
Strength / density trade-off
One line from notebook to knowledge graph.
A batteries-included Python SDK with typed responses, streaming results, and optimistic caching. Use it in a Jupyter cell the same way you'd query a database.
from lattice import Client, spec client = Client(api_key="sk_live_…") # natural-language composition search results = client.search( "Mg alloys stable above 400°C " "with yield strength > 220 MPa " "and cost below $9/kg", filter=spec.source(tier="internal"), limit=12, ) for m in results.shortlist: print(m.composition, m.confidence) # → WE43-T6 0.94 # → ZK60A-T5 0.91 # → Elektron 21 0.87
We're building the platform we wished existed when we were materials engineers ourselves.
Ship the material, not the search for it.
We're onboarding early teams now. Sign up for early access and help shape the platform.