Modeltrain
Life sciences data readiness
what
A data strategy and engineering consultancy that acquires and cleans clinical, medical, and commercial data for analytics and AI.
who
biopharma, devices, CROs and clinops, consultancies, agencies, startups
symptoms
“We don't have our own data team.”
“Nobody really believes the data is accurate.”
“We stitch data together in Excel.”
problem
Business teams need the data right now to do their work but can't get immediate support.
solution
We help business teams get the data they need fast.
use cases
CRM, analytics, reports, dashboards, data apps, data pipelines, AI and agent enablement
services
3-month engagements in 2-week sprints.
01
acquire new data
Collect and structure data from public sources: government registries, regulatory databases, professional directories, clinical trial repositories, congresses, the Census, facility listings, hospital websites. Delivered as files or as a database table.
02
clean up data
Deduplicate records, standardize formats, resolve inconsistencies, and fill in missing values to produce data your team can actually trust.
03
link data together
Link multiple data sets that describe the same entities (physicians, sites, products, trials, etc) to create richer, more complete records.
04
make data ai-ready
Structure data for AI agent consumption. Create clean schemas, descriptive metadata, knowledge graphs, evaluation rubrics, and agent skills.
data types
We specialize in key life sciences domains.
person
Resolve duplicate records for physicians, investigators, and KOLs across internal systems, licensed data, and public sources into a single authoritative entity.
org
Standardize HCO and trial site records across systems.
sponsor
Standardize sponsor company names so "Novartis Pharmaceuticals Corporation," "Novartis Pharma AG," and "Novartis" all resolve to one entity.
product
Standardize drug names, molecular IDs, and brand/generic linkages so “Keytruda,” “pembrolizumab,” and “MK-3475” all resolve to one record.
indication
Map inconsistent disease names and abbreviations to MeSH, MedDRA, and ICD-10 hierarchies, and roll them up to therapeutic areas for cross-functional analysis.
procedure
Standardize trial interventions and procedures using CPT codes and medical terminology.
protocol
Standardize protocol elements across trials to derive computed scores for complexity, site burden, and patient burden.
why us
We are data professionals with years of experience in clin ops, medical affairs, and commercialization who had to design our own solutions to deal with data issues. We will use these solutions to help you.
difference
Long implementation timelines before value is delivered
Usable data assets in weeks, iterated from there
One sprawling data model that fails to account for nuances
Built around the specific use cases your team actually needs
External consultants without life sciences domain knowledge
Deep domain expertise in commercial, medical, and clinical use cases
Rigid architecture intended only for reporting
Structured for AI agents, text-to-SQL, and LLM-powered tools
sample projects
Compile a list of staff at clinical trial sites listed on their websites
Link prescriber records between a CRM and a claims dataset to build enriched HCP profiles
Link sponsor names across a global trial portfolio so "Pfizer Inc," "Pfizer Global R&D," and "Pfizer Oncology" map to one record
Score trial protocols on patient burden based on visit frequency, procedures, and eligibility criteria
Tag all products in a pipeline with therapeutic area hierarchies for cross-functional reporting
Prepare the data for a text-to-SQL interface over a commercial data warehouse so business users can ask questions in plain English
Clean up an organization list with inconsistent spellings across non-English alphabets
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