See the difference.Measure the impact.
We believe in transparency. Here's exactly how Alchemy Bio compares to traditional AI chatbots and manual research — with methodology you can verify.
Why AI Chatbots Fall Short in Biopharma Research
Generic AI models have fundamental limitations that make them unreliable for mission-critical biopharma workflows.
Temporal Desynchronization
Training cutoff creates data gaps
"Current Phase 3 trial status for [Drug X]"
→Model returns state from training cutoff, not registry reality
Synthetic Data Generation
Confabulation under uncertainty
"Top 5 KOLs in CAR-T with h-index metrics"
→Model generates plausible but non-existent researcher profiles
Prompt Engineering Overhead
Technical barrier to adoption
Workflow Latency
Iterative refinement burden
Purpose-Built Extraction Infrastructure
Domain-specific pipelines engineered for regulatory-grade data extraction from heterogeneous biopharma source systems.
Temporal Synchronization
Direct integration with authoritative registries. No training cutoff boundaries.
Real-time data streamsProvenance Chains
Every data point traced to source. Full audit trail for regulatory compliance.
Complete traceabilityDomain-Specific Pipelines
Purpose-built extraction architectures optimized for biopharma data structures.
Specialized processingMulti-Registry Traversal
General-purpose models surface high-visibility, frequently-cited content. Our architecture systematically indexes across registry depth, capturing low-citation recent publications and regional sources.
High-citation, popular publications only
Deep search across all relevant sources
Critical insight: Breakthrough data often resides in recent low-citation studies or regional publications not indexed by general-purpose search.
Precision vs Approximation
Exact values with provenance, not statistical guesses
- -Budget forecasts with error margins
- -HTA submissions with unverifiable claims
- -Competitive analysis with synthetic data
- +Exact values from source databases
- +Complete provenance chain per data point
- +Explicit "data not available" signaling
Temporal Data Freshness
Pipeline Performance Metrics
Quantified benchmarks across extraction time, output consistency, and source verification accuracy.
HEOR Pipeline
Structured extraction of cost-effectiveness endpoints, budget impact parameters, and real-world evidence from heterogeneous source systems.
Extraction Capabilities
- ICER threshold mapping across jurisdictions
- Budget impact parameter extraction
- RWE synthesis from fragmented registries
- Comparative effectiveness quantification
Data Integrity
Direct registry integration with continuous synchronization
Output: Structured Report
Regulatory-grade output with provenance chains and audit trails
Reducing Cognitive Overhead
Structured interfaces eliminate prompt engineering burden, enabling domain experts to operate without technical intermediation.
Zero Prompt Engineering
Structured interfaces eliminate prompt crafting overhead.
Single-Query Execution
One input, complete output. No iterative refinement.
Built-in Verification
Source citations embedded. No manual fact-checking.
Input Complexity Differential
Comparative query formulation requirements
General LLM Interface
Prompt engineering required
I need to analyze cost-effectiveness data for [drug name] in [indication]. Please search for: 1. Published ICER values from HTA submissions 2. QALY gains reported in clinical trials 3. Comparator treatments and their costs 4. Real-world evidence on healthcare utilization Format the results in a table with source citations. Make sure to check multiple databases including PubMed, Cochrane, and HTA agency websites... [Requires 5-10 follow-up prompts to refine results, verify sources, and correct hallucinations]
Alchemy Bio Interface
Structured input, zero prompt engineering
Analyze HEOR data for [drug name] in [indication]Validation Methodology
Rigorous, reproducible evaluation framework designed for regulatory-adjacent evidentiary standards.
Evaluation Protocol
Data Integrity Audit
Workflow Efficiency Metrics
End-to-end operational efficiency measured from initial query formulation through final validated output:
- Query formulation and prompt engineering overhead
- Iterative refinement cycles to acceptable quality
- Manual verification burden against source databases
- Error correction and re-work time allocation
Result: Domain-specialized systems demonstrated substantially reduced time-to-insight compared to both general-purpose AI and manual research approaches.
Ground Truth Sources
Outputs validated against authoritative databases representing the evidentiary standard for biopharma research:
Ground truth alignment assessed via cross-referencing extracted data points, citations, and statistical claims against primary source records.
Sample Query Templates
Execute these queries in parallel environments to benchmark output quality and extraction fidelity.
Clinical Trials
Registry extraction
Extract active trial registry data with enrollment status, phases, and sponsor information.
Execute in Alchemy BioHEOR
Economics extraction
Generate cost-effectiveness analysis with QALY values and HTA submission data.
Execute in Alchemy BioKOL
Expert mapping
Identify domain experts with verified credentials, publications, and institutional affiliations.
Execute in Alchemy BioExecute identical queries in general LLMs (ChatGPT, Claude) to compare output accuracy, source verification, and iteration requirements.
Execute Your Own Benchmark
Deploy a controlled evaluation using your research queries. Compare extraction fidelity, source verification, and operational efficiency.