DiscoveryFlow
Research Accelerator Platform by CirkaFlow

Transforming Months of Manual Work Into Days of Systematic Discovery

A four-layer intelligent architecture that automates hypothesis generation, validation, and decision-making

Request a Demo View Platform Architecture

What is It?

Research acceleration platform that automates hypothesis generation, validation, and scientific decision-making. It transforms complex scientific workflows into
a transparent, reproducible, and self-improving system.

How it works

Four-Layer Intelligent Architecture:

Digital Twin simulation
Hypothesis evolution (500–1500 variants)
Validation across 200M+ papers
Hybrid Decision Platform

Who it’s for

Research Teams & Laboratories
Universities & Scientific Institutes
Corporate R&D Departments
Sustainability & ESG Projects
Grant & Funding Organizations

What you gain

10–20× faster analysis

70–90% less manual work

Breakthrough discoveries in days

6–8 weeks spent analyzing a single technological pathway

Even a small change in assumptions often forces researchers to start over.
50–100 sources manually reviewed for every topic

Papers, reports, patents — all processed by hand, with no automated synthesis.
High dependency on intuition and prior experience

Critical decisions are often subjective and difficult to reproduce.
No systematic way to explore all alternatives

Combinatorial explosion makes exhaustive evaluation unworkable.
30–70% of modern workflows remain manual

Researchers spend most of their time searching, checking, reconciling information — not generating hypotheses.

Why Systematic Analysis Is Nearly Impossible Manually

Even with experienced researchers, manually assessing all possible technological pathways is physically unworkable. The complexity grows exponentially, far beyond what human intuition or manual review can handle.

Combinatorial explosion makes full evaluation impossible

Even small systems generate tens of thousands of possible pathways, none of which can be exhaustively reviewed by hand.
Researchers rely heavily on intuition and prior experience

Critical decisions become subjective, inconsistent and hard to reproduce.
High-value solutions often remain undiscovered

Most promising cross-domain combinations never get explored.
No transparency in decision-making

Manual workflows leave no audit trail, making it impossible to justify choices in grants or peer review.
No mechanism for comparing alternative pathways

Changing assumptions often forces researchers to restart the entire process.

The Four-Layer Intelligent Architecture

A unified system that transforms manual research workflows into a transparent, reproducible,
and self-improving discovery process.

Digital Twin

A simulation-ready model that enables rapid testing of parameters, technologies, and pathway configurations.
AlphaEvolve

Generates and evolves thousands of hypotheses, identifying unexpected combinations and optimizing them inside the Digital Twin.
Semantic Scholar Intelligence

Validates hypotheses against 200M+ scientific papers, checks novelty, identifies trends, and uncovers interdisciplinary links.
Hybrid Decision Platform

Combines rules, machine learning, and LLM synthesis to produce transparent, reproducible decisions — never a black box.

How Each Layer Works

A coordinated architecture where every layer amplifies the next — from modeling to hypothesis evolution to scientific decision-making.

1. Digital Twin

A simulation-ready model of the entire process.
• 40–80 parameters per model
• 200–500 simulations per cycle
• Enables objective testing of thousands of configurations
• 10–20× faster analysis compared to manual modeling
2. AlphaEvolve

An evolutionary engine that generates and improves hypotheses.
• 500–1500 hypotheses per cycle
• 50–100 evolution generations
• Finds unexpected high-value combinations
• Produces 3–5 optimal solution types in 1–2 days
3. Semantic Scholar Intelligence

Connects hypotheses to global scientific evidence.
• 200M+ publications analyzed
• 5,000–20,000 relevant documents per topic
• Novelty and trend detection
• Reveals interdisciplinary scientific links
4. Hybrid Decision Platform

Produces transparent, reproducible scientific decisions.
• Integrates BRMS, ML models, and LLM synthesis
• 20–50 rule-based decision modules
• 5–12 ML predictors (risk, feasibility, forecasting)
• 100% audit trail and fully explainable outcomes

What Researchers Gain

Clear, measurable advantages that transform how scientific research is done.

Radical Time Reduction

10–20× faster analysis
Months of manual work are reduced to days of systematic discovery — without sacrificing scientific rigor.
Less Manual Work, More Thinking

70–90% reduction in routine tasks
Researchers spend less time searching, checking, and reconciling information — and more time on analysis and hypothesis generation.
Higher-Quality Hypotheses

30–50% improvement in hypothesis quality
Thousands of alternatives are systematically explored, revealing high-value solutions that intuition alone would miss.
Full Transparency and Reproducibility

100% explainable decisions
Every assumption, comparison, and outcome is traceable — essential for grants, peer review, and long-term learning.

A Fully Automated 24-Hour Research Cycle

An autonomous system that continuously updates knowledge, evolves hypotheses, and improves research outcomes — day after day.

00.00

Information Ingestion

Automatic synchronization with Semantic Scholar, arXiv, PubMed
Integration of new papers on oceanology, materials science, chemistry
Citation Network Analysis updates to detect emerging trends

02.00

Digital Twin Updates