Purchase and quality
Batch price and volume are compared with grain quality, supplier terms and the related contract.
AI Grain Trading · management analytics and AI automation
Proposed solution
See the true margin
of every grain trade
A single management layer for grain trading: from the counterparty, contract and batch to logistics, documents, payments and the financial result.
CTRM (Commodity Trading and Risk Management) covers commodity trading and risk. The proposal adds these capabilities and AI tools to existing accounting rather than replacing it by default.
- Planned, forecast and actual margin
- Profit per tonne and cost drill-down
- AI with sources and action approval
The management task
The purchase–sale spread does not show the full result
The spread is only the starting point. The proposed solution links every material cost, deadline and obligation to the trade, creating an end-to-end management view rather than another revenue report.
Logistics
Routing, transport and transloading affect cost and can change between the initial plan and actual execution.
Storage
Elevator charges, storage time and handling are assigned to a specific batch and trade rather than a general overhead total.
Funding
Funding source and cost, credit lines and the payment calendar reveal the financing cost of the operation.
Capital turnaround
Delivery, acceptance and payment times connect margin with turnover and overdue receivables.
Revenue − purchase − logistics − storage and transloading − funding − other agreed costs = trade result
End-to-end trade lifecycle
One trade instead of disconnected traces across systems and spreadsheets
The information model links commercial, operational and financial objects. Reference data and mandatory fields follow the company's actual processes.
- 01
Counterparty and ownership
Registration details, dossier, relationship history, payment discipline, the responsible employee and their area of accountability.
- 02
Contracts and trade
Purchase and sales contracts, volume, prices, deadlines, obligations, specifications and the history of changing terms.
- 03
Batch and quality
Product type, batch, physical volume and grain quality characteristics, storage location and movements.
- 04
Logistics and documents
Route, transport operation, transloading, shipment, source documents, their versions and links to business events.
- 05
Payments and capital
Planned and actual payments, debt, funding source and cost, and the use of agreed limits.
- 06
Result and exceptions
Planned, forecast and actual margin, profit per tonne, missed deadlines, management decisions and their history.
Management dashboard
From a company metric to the calculation and source document
The proposed interface combines financial and operational indicators, filtered by period, product, counterparty, employee and trade status. A manager can move from an aggregate to a trade and its calculation.
Exception indicators cover margin, turnover, contractual deadlines and overdue debt. Thresholds follow company rules rather than generic template values.
Initial commercial terms
Expected prices, costs, timing, margin and profit per tonne under the agreed trade parameters.
Result based on execution
Expected margin updated as logistics, storage, funding and other conditions change.
Confirmed outcome
Actual income and costs, variance from plan, cost structure and capital turnaround time.
- Company indicators and critical events
- Product, counterparty, period or responsible employee
- Trade record and the structure of its result
- Method, source records and related documents
AI analyst for management
AI explains the calculation instead of inventing it
Software calculation modules produce financial indicators from validated data. The language model receives results through authorised tools, explains changes and points to the trade, period, method and sources.
The project includes natural-language queries, scheduled reviews and data extracts. If data is missing or unreliable, the AI analyst must state the limitation instead of producing an unsupported conclusion.
Anomaly detection uses formal rules and statistics: unusually low margin, rising logistics costs, longer turnover, atypical costs or inconsistent indicators. AI helps interpret the signal and identify what to check.
- Which trades have the lowest forecast margin, and why?
- What changed in profit per tonne for a selected product?
- Where did logistics or funding deviate from plan?
- Which delivery and payment deadlines need attention?
An anomaly is a prompt to investigate, not an automatic accusation or action.
The conclusion relies on data and rules; any corrective decision remains within established management authority.
Documents and daily operations
AI supports preparation and control, not only search
The core project combines several operational scenarios. Sources, rules and automation levels are defined after discovery and quality checks on company data.
Document intake, OCR and RAG
Documents from agreed folders, email, document exchange or accounting systems are classified, recognised where needed and linked to the counterparty and trade. Completeness, versions and contradictions are checked.
RAG searches the authorised dossier and returns the source. When confidence is insufficient, an employee confirms the classification and extracted details.
Counterparty record
The AI agent extracts details from a document pack, checks completeness, identifies possible duplicates and inconsistencies, and prepares a structured record.
After employee review, data can be sent to the existing ERP/CRM through an agreed API or prepared as an import file. Operations are logged.
Controlled trade checking
Checks cover mandatory details, margin, purchase, logistics, funding, deadlines and limits. Document terms are compared with internal rules.
The conclusion includes calculations and sources: compliant, needs correction or requires approval. Registration and writes occur only within an authorised workflow.
Reporting from agreed templates
On request or on a schedule, the system prepares management reviews, analytical notes, tables and extracts covering trades, counterparties, products and debt.
Recipients see only authorised data; external distribution requires approval.
XLSXPPTXDOCXPDFCSV
Proposed architecture
Integrate with current accounting first, then extend
The baseline scenario minimises intervention in the current accounting environment. The exchange method depends on the actual 1C configuration or other system in use, its available interfaces and data quality.
- 01 · SOURCES
1C and other current accounting
Financial and operational records, reference data, contracts, documents and agreed file sources.
- 02 · INTEGRATION
APIs and completeness checks
Agreed exchange, identifier matching, consistency controls and error handling.
- 03 · DATA AND CALCULATION
PostgreSQL and calculation services
Management database, financial models, document and metadata storage, and event history.
- 04 · APPLICATIONS
Dashboards and AI tools
Trade records, analytics, OCR, RAG, reports and bounded agent workflows.
Model deployment and integrations are selected after discovery, taking corporate data requirements into account. Access controls, logs, backups and recovery checks are included from the first stage.
Staged implementation
A working management foundation without launching every agent at once
Stages allow data, calculations and practical workflows to be checked before automation expands according to agreed priorities.
Start with analytics if that meets the immediate need. A narrower option combines the dashboard, AI analyst, trade monitoring, exception detection and basic extracts. Automated counterparty and trade registration, extended document workflows and report generation belong to the core project.
Dashboard, AI analyst and operational workflows
The two tracks develop together as components become ready:
- integration, indicators, trade records and exceptions;
- AI analysis, document processing, and counterparty and trade controls;
- management reporting, notifications and agreed integration actions.
A dedicated ERP/CRM/CTRM only if current systems constrain the project
If existing systems cannot support the required processes and integrations, a dedicated layer for counterparties, trades, batches, quality, logistics and costs can be considered.
The platform is selected after comparing alternatives. Replacing statutory accounting or building a corporate MDM platform is not automatically included.
Digital trace, events and copilots for selected roles
A later layer formalises processes, resources, obligations and events as a digital twin of operating activity.
Commercial, finance, logistics, legal and control assistants are then introduced by priority. Their tools and authority are defined separately rather than launching the full agent set at once.
Scope and timing are refined after reviewing systems, data and processes.
Roles and boundaries
Autonomy matches the consequences of the action
Employee and AI-agent permissions are separated. Automated processes use least-necessary access, dedicated accounts, activity logs and controls over permitted tools.
Analysis and search
AI accesses agreed indicators and documents, answers with sources, identifies exceptions and prepares management commentary.
Draft and recommendation
The system creates records, reports, checks and integration data. Ambiguous details and missing information are returned to an employee.
Writes and external actions
Writes to critical systems, financial and legally significant decisions, and external document distribution require confirmation by an authorised employee.
First step
Discovery before fixing the technical specification
An initial conversation only needs to cover the current process, systems in use and the main management question. Access and working materials are provided later through an agreed secure process.
- Systems and data
Configurations, interfaces, reference data, record quality and available history.
- Processes and rules
Trade lifecycle, margin methods, limits, roles and approval points.
- Priority scenario
A bounded implementation scope and control examples using representative data.
- Acceptance criteria
Agreed checks of calculations, drill-down, documents, reports, permissions and error handling.
AI Grain Trading
Start with a management question and discovery
Describe which trade and margin data currently needs to be gathered and reconciled manually.
Write about the project →admin@akonnov.ru