Profile Creation and Standardization
AI can automatically extract and standardize information from unstructured documents – contracts, specifications, test reports, photos – that buyers and sellers already have. Instead of requiring users to fill out complex forms or learn specialized terminologies, they can simply upload what they have. LLMs can parse these documents, extract key attributes, and create searchable profiles that capture nuanced requirements and offerings.
For grain trading, this might mean uploading a grain elevator’s test report and having AI automatically extract protein content, moisture levels, falling number, and other quality metrics into a standardized profile.
Cosolvent will shortly do this out of the box. As we populate and build out GrainPlaza, you will be able to play and experience its potential.
Intelligent Matching Beyond Keywords
Traditional databases rely on exact matches or simple filters. AI can understand semantic relationships and make intelligent trade-offs. It can match a buyer looking for “high-protein spring wheat suitable for bread making” with a seller offering “14.5% protein hard red spring wheat, excellent baking qualities” even if the exact terminology doesn’t match.
The AI can also weight multiple factors – price, quality, timing, location, past transaction history – to surface matches that might not be obvious but could work well for both parties.
For grain trading, this might mean uploading a grain elevator’s test report and having AI automatically extract protein content, moisture levels, falling number, and other quality metrics into a standardized profile.
Cosolvent will shortly do this out of the box. As we populate and build out GrainPlaza, you will be able to play and experience its potential using our synthetic test data.
Dynamic Market Intelligence
AI can continuously monitor market conditions, pricing trends, regulations, and logistics constraints to provide real-time guidance. It can alert users when conditions favor their transactions or suggest timing adjustments based on seasonal patterns, shipping schedules, or regulatory changes.
For grain trading, this might mean uploading a grain elevator’s test report and having AI automatically extract protein content, moisture levels, falling number, and other quality metrics into a standardized profile.
We hope that Cosolvent will grow to do this. As we populate and build out GrainPlaza with synthetic data, we may be able to simulate some examples. However, the true power won’t be known until a real-world system is prototyped and tested.
Reducing Information Asymmetries
In thin markets, participants often lack complete information about alternatives, fair pricing, or standard practices. AI can aggregate market intelligence and provide benchmarking data, helping both sides understand what constitutes a fair deal. This transparency can increase market participation by reducing the fear of being taken advantage of.
Cosolvent will shortly do this out of the box.
Automated Relationship Building
AI can maintain detailed interaction histories and preferences, essentially serving as an institutional memory that builds trust over time. It can remember that Buyer X always needs delivery by harvest season, or that Seller Y consistently delivers above-specification quality, enabling more confident repeat transactions.
This is an obvious extension of the Cosolvent design, but we can’t build it and test it until we assemble a set of realistic market users. Relationships are funny things and pretty hard to simulate in a mechanical way. Deeperpoint has begun a project to use generative LLM+RAG to build sets of synthetic, but realistic users.
Scalable Due Diligence
For each potential match, AI can automatically perform background checks, verify credentials, assess financial stability, and flag potential risks – tasks that would be prohibitively expensive for small transactions in thin markets.
The key insight is that AI doesn’t just automate existing processes – it fundamentally changes the economics of market-making by making it cost-effective to provide sophisticated brokerage services for smaller, more specialized transactions that couldn’t previously justify the overhead.
This is an obvious extension of the Cosolvent design, but it’s structure and function will depend a lot on the specific market that is being served. We may be able to play and simulate in GrainPlaza, but we won’t be able to build it out and test it until we have a live market to apply it to.
Future Cosolvent & GrainPlaza Extensions
The potential for AI applications can range far beyond basic market making.Here are some possibilities that we are thinking about, but not yet addressing. The examples are consistent with Grainplaza but that is only for illustration. Either we will get to them when we can, or someone will take Cosolvent and build these out on their forked version. Alternatively, some of the existing data services for these factors could fairly easily feed into a Cosolvent system via the microservices structure.
Real-Time Cost Modeling
The system can continuously model total delivered costs for both shipping methods by integrating:
- Live bulk shipping rates from major routes (US Gulf to Asia, Black Sea to Middle East)
- Container shipping rates and availability from freight forwarders
- Port handling charges, demurrage, and storage costs
- Inland transportation costs from origin to different port options
- Currency fluctuations affecting international transactions
- Fuel surcharges and seasonal rate variations
For a Manitoba wheat producer, the system could show that while bulk shipping from Vancouver might normally be cheaper, current container rates plus rail transport costs actually favor containerized shipping for their specific volume and timing.
Quality Preservation Analysis
The AI can analyze how different shipping methods affect grain quality over various routes and timeframes:
- Moisture migration patterns in containers vs. bulk holds
- Temperature control capabilities and costs
- Contamination risks and insurance implications
- Quality degradation models based on journey length and conditions
- Buyer requirements for specific quality maintenance standards
This helps producers understand when the quality preservation benefits of containerized shipping justify higher costs, particularly for premium specialty grains.
Market Timing Intelligence
The system can identify optimal shipping windows by analyzing:
- Seasonal shipping rate patterns (harvest surges, weather delays)
- Port congestion forecasts and their impact on different shipping methods
- Buyer seasonal demand patterns and inventory cycles
- Historical data on rate spreads between bulk and container shipping
A producer might learn that delaying shipment by 3 weeks could save 15% on container costs, while bulk rates remain stable.
Supply Chain Flexibility Modeling
The AI can quantify the value of flexibility that containerized shipping provides:
- Alternative port options and their comparative advantages
- Ability to split shipments to multiple buyers
- Reduced minimum volume requirements enabling more frequent sales
- Speed advantages for time-sensitive premium markets
- Reduced dependency on bulk terminal availability and scheduling
Risk Assessment and Mitigation
The system can evaluate and compare risks across shipping methods:
- Weather-related delays and their probability by route and season
- Political and trade policy impacts on different shipping corridors
- Insurance costs and coverage differences
- Counterparty risk assessment based on shipping method choice
- Force majeure event modeling (port strikes, canal closures)
Buyer-Specific Optimization
For each potential transaction, the AI can analyze buyer preferences and constraints:
- Destination port capabilities and preferences
- Buyer’s inventory management and just-in-time requirements
- Regional preferences for shipping methods
- Buyer’s past transaction patterns and satisfaction levels
- Regulatory requirements at destination that favor certain shipping methods
Small-Volume Economics
The system can identify when containerized shipping becomes advantageous for specialty grains:
- Break-even analysis for different volume thresholds
- Premium pricing opportunities that justify higher shipping costs
- Niche market access that’s only practical via container shipping
- Blending opportunities with other producers to optimize container loads
Documentation and Compliance Intelligence
AI can streamline the complex documentation requirements that often favor one shipping method over another:
- Automated generation of bills of lading, certificates of origin, and quality certificates
- Compliance checking for import/export regulations by destination
- Phytosanitary certificate requirements and processing times
- Banking and letter of credit requirements that vary by shipping method