Target setup
Capture molecule, host, feedstock, production mode, business objective, constraints, and assay readiness.
Lab-minimal AI decision platform
From target molecule to ranked strain-design plan.
Stra-Forge® helps synthetic biology companies turn scattered literature, pathway evidence, enzyme data, host constraints, and early economics into traceable design decisions.
Evidence engine first
Commercial feasibility built in
Closed-loop learning ready
Decision run · SF-DMO-01
Demo mode
Recommendation
Safest first-pass design + highest-upside alternative
Output
Ranked build plan
CustomerSynbio & precision fermentation teams
ProblemSlow, fragmented strain-design decisions
SolutionEvidence-backed design ranking
Business valueDecide faster, fail less, scale better
Real workflow
Built around how production strains are actually developed.
Instead of behaving like a general chatbot, Stra-Forge® follows a reproducible decision pipeline: define the target, gather evidence, reconstruct biology, screen feasibility, rank designs, and learn from outcomes.
Evidence mining
Search exact and related compounds across literature, patents, databases, and internal reports.
Knowledge layer
Normalize molecules, enzymes, hosts, units, and reported titer / rate / yield into comparable evidence.
Pathway reasoning
Reconstruct candidate routes, required heterologous steps, cofactors, bottlenecks, and host gaps.
Commercial screen
Connect strain metrics to product category, downstream difficulty, and early techno-economic pressure.
Ranked build plan
Recommend safest first pass, highest upside, and most informative experiment set with evidence traceability.
Interactive demo
A product experience built for decision quality.
Select a target molecule and host assumption. The demo shows the intended customer workflow: evidence summary, pathway map, commercial screen, and ranked design recommendation. Outputs are illustrative and not wet-lab protocols.
Recommendation dashboard
Resveratrol: S. cerevisiae route recommended
High-value polyphenol / nutraceutical ingredient.
Fit score88
Host note
Candidate pathway map
Required enzyme logic
Likely bottlenecks
| Evidence type | Signal | Decision use |
|---|
Product surfaces
Designed as a real platform, not just a report generator.
The demo now reflects the practical build sequence: evidence engine first, then recommendation engine, then feedback learning.
Target setup page
Collect molecule, host, feedstock, production mode, lab constraints, commercial priority, and project objective.
Evidence workspace
Show structured studies, enzyme candidates, reported metrics, optimization strategies, and confidence tags.
Pathway map
Visualize precursor flow, heterologous steps, host-native reactions, cofactor needs, and likely bottlenecks.
Recommendation board
Rank safest first-pass, highest-upside, and most informative build strategies with transparent scoring.
Report export
Generate investor, management, or R&D-facing feasibility memos with assumptions and traceable evidence.
Feedback ingestion
Upload experimental outcomes to update confidence in pathways, enzymes, host compatibility, and design rules.
Decision engine
Transparent ranking before machine-learning complexity.
The first commercial MVP should not hide behind a black box. Stra-Forge® can start with a traceable scoring model, then improve as real outcomes flow back into the platform.
25%
Evidence strength
20%
Pathway completeness
15%
Enzyme confidence
15%
Host compatibility
15%
Bottleneck + build risk
10%
Commercial feasibility
Structured knowledge layer
From fragmented sources to reusable decision assets
Target molecule
Pathway
Enzyme
Host
Titer / Yield
TEA screen
Build plan
The practical path to making Stra-Forge® real.
The demo is structured around a realistic implementation sequence: narrow scope, build golden reports, create evidence infrastructure, and expand into a closed-loop platform.
Golden reports
Pick one molecule class and two hosts. Manually produce 3 high-quality feasibility reports to define the ideal product output.
Evidence engine
Build literature ingestion, structured extraction, entity normalization, evidence tables, and human curation workflow.
Recommendation MVP
Add pathway reconstruction, bottleneck templates, scoring logic, commercial screen, and report export.
Learning loop
Ingest experimental outcomes to update enzyme confidence, host compatibility, bottleneck prediction, and ranking weights.
Trust & boundaries
Built for responsible synthetic biology decision support.
Stra-Forge® should be biosecurity-aware from day one. The product experience can be useful without generating risky implementation details.
Traceable recommendations
Every recommendation should carry evidence references, confidence levels, assumptions, and reviewer status. The goal is decision-grade evidence, not unverifiable AI answers.
Biosecurity-aware outputs
The platform should avoid pathogen enhancement, toxin production, evasion mechanisms, and direct DNA-ordering outputs. Sensitive cases should trigger human review.
Customer data isolation
Enterprise data should remain private by default, with tenant-level isolation and explicit permission before any data is used for model or benchmark improvement.
Human-in-the-loop curation
The first version should include expert review for extracted evidence and high-impact recommendations. This builds trust and creates proprietary structured data.
Pilot program
Start with one target molecule.
The right first customer motion is a paid feasibility or strain-design project: one target, one host assumption, one evidence-backed recommendation memo, and a clear path toward a recurring platform workflow.