Inspiration
Scientific research often starts with a simple idea: “I have a hypothesis I want to test.” But turning that hypothesis into a real experiment is much harder. A researcher has to search through papers, identify existing work, design an experiment, find protocols, determine materials and equipment, estimate costs, plan timelines, consider validation and safety, and get expert feedback. These tasks are usually spread across many disconnected tools.
The problem is not that researchers lack information. The problem is that the information, reasoning, and planning are fragmented. Researchers still have to manually connect literature, experimental methods, procurement, budgeting, scheduling, verification, and expert judgment.
That led us to a simple question:
What if a researcher could start with a hypothesis and move through the entire planning process in one structured, evidence-grounded workflow?
That is the idea behind SciForge — From Hypothesis to Experiment-Ready Research.
What it does
SciForge is a research-planning engine, not just an AI chatbot.
A researcher provides a hypothesis or research question, and SciForge guides it through a structured pipeline:
Hypothesis → Literature & Evidence → Novelty Assessment → Experimental Design → Protocol → Materials & Procurement → Budget → Timeline → Validation & Statistics → Safety & Governance → Evidence Verification → Scientist Review → Feedback → Experiment-Ready Plan
Instead of returning a paragraph of AI-generated suggestions, SciForge produces a structured Research Planning Package containing:
- Research objective and hypothesis
- Supporting and contradicting literature
- Novelty signals
- Experimental design, controls, variables, and measurements
- Step-by-step protocol
- Required materials, equipment, quantities, and sources
- Budget estimation
- Timeline and dependencies
- Validation and statistical planning
- Failure modes and quality checks
- Safety and governance considerations
- Evidence and source traceability
- Scientist review and structured feedback
The important part is that AI does not make the final scientific decision. SciForge is designed as a decision-support system where researchers can inspect evidence, challenge assumptions, modify the plan, and remain responsible for the final decision.
How we built it
We designed SciForge as a modular research-planning architecture with seven conceptual layers:
- Input Layer — captures the hypothesis, research question, constraints, and domain information.
- Evidence Layer — retrieves relevant scientific literature and supporting sources.
- Reasoning Layer — analyzes the evidence and constructs a proposed research plan.
- Planning Layer — converts the plan into protocols, resources, budgets, timelines, validation, and safety considerations.
- Verification Layer — checks claims and procurement information against available sources.
- Quality Layer — applies structured quality gates to identify incomplete or problematic plans.
- Expert Layer — allows researchers to review, correct, and improve the generated plan.
Our implementation builds on an existing LabMind AI prototype, which already demonstrates the core workflow:
Hypothesis → Literature QC → Novelty Signal → Experiment Plan → Operational Details → Verification → Scientist Feedback
SciForge expands this foundation into a broader research-process platform focused on connecting the scientific and operational sides of experiment planning.
Challenges we ran into
Building an AI system for scientific planning creates challenges that a normal chatbot does not have to deal with.
First, hallucinations and incomplete evidence. A scientifically plausible answer is not enough. Important claims need supporting evidence and traceability.
Second, operational accuracy. It is not enough to suggest an experiment. The system needs to consider materials, quantities, procurement, cost, dependencies, timelines, validation, and safety.
Third, scientific complexity. Experimental requirements differ significantly between domains, so the system cannot assume that one generic planning approach works everywhere.
Fourth, safety and governance. Biological, chemical, human-subject, animal, and institutional requirements can vary, meaning safety has to be considered during planning rather than added at the end.
Finally, human judgment. We did not want to build a system that presents an AI-generated plan as automatically correct. Expert review and structured feedback are therefore part of the workflow itself.
Accomplishments that we're proud of
We are proud that SciForge goes beyond the common idea of “AI generates a research protocol.”
Our key accomplishment is designing a complete orchestration layer that connects:
Scientific Evidence + Reasoning + Experimental Design + Operations + Verification + Expert Review
into one workflow.
We also built the concept around measurable quality rather than simply claiming that AI makes research better. Our evaluation framework proposes measuring:
- Planning time
- Plan completeness
- Evidence coverage
- Procurement verification
- Issues identified before execution
- Expert acceptance
- Feedback effects
- Time to an expert-approved plan
Most importantly, SciForge is supported by an existing prototype foundation rather than being only a conceptual idea. The LabMind AI prototype already demonstrates the core hypothesis-to-feedback workflow that SciForge expands upon.
What we learned
We learned that scientific research planning is an orchestration problem, not simply an information-retrieval problem.
Researchers already have access to excellent individual tools. The real difficulty is connecting the outputs of those tools into one coherent, defensible research plan.
We also learned that in scientific applications, confidence should come from evidence and verification rather than fluent AI-generated answers. A useful system should make assumptions, sources, gaps, risks, and uncertainties visible.
Most importantly, we learned that human expertise should remain part of the system. AI can help structure information, identify possibilities, and accelerate planning, but scientists should remain responsible for evaluating the plan and making the final scientific decisions.
What's next for SciForge — From Hypothesis to Experiment-Ready Research
Our next step is to move SciForge from a strong prototype concept toward real-world research environments.
Phase 1 — Academic Research Labs
Pilot SciForge in university laboratories and supervised research environments to measure whether it reduces planning effort and improves preparation.
Phase 2 — Biomedical Research Centers
Expand into environments where evidence, safety, and governance requirements are more demanding.
Phase 3 — Biotechnology & Scientific Organizations
Support teams that conduct repeated experiments and need standardized research-planning workflows.
Phase 4 — Institutional Research Platform
Integrate institutional templates, approved material catalogs, lab-specific constraints, expert networks, and laboratory information systems.
Our long-term vision is simple:
A researcher should be able to start with “I have a hypothesis” and reach “I have a defensible plan for testing it” without manually stitching together dozens of disconnected tools.
SciForge — From Hypothesis to Experiment-Ready Research.
Built With
- ai
- ai-agents
- evidence-retrieval
- experiment-planning
- generative-ai
- llm
- natural-language-processing
- postgresql
- python
- research-automation
- scientific-research
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