Reconcilia Energy
AI-powered settlement and reconciliation for energy trading that matches unstructured trading communications with structured transaction records to improve traceability, reduce input errors, and minimize financial loss risk.
The Problem
“Reconcile unstructured energy trading communications with structured transaction records”
Organizations face these key challenges:
High volume of emails, PDFs, broker messages, and confirmations with inconsistent formats
Manual matching of communications to trades is time-consuming and difficult to scale
Rules-based matching fails when references are missing, abbreviated, or ambiguous
Input errors in price, quantity, delivery dates, or counterparty details can cause losses
Impact When Solved
The Shift
Human Does
- •Review emails, PDFs, broker messages, and confirmations to identify trade details
- •Compare counterparty, volume, price, delivery window, product, and settlement terms against booked records
- •Investigate mismatches, missing references, and possible amendments across communications and spreadsheets
- •Decide how to resolve reconciliation breaks and update records or escalate issues
Automation
- •Apply basic rules to flag obvious field mismatches
- •Search records using exact or partial reference matches
- •Highlight missing required fields in structured entries
Human Does
- •Review low-confidence matches, ambiguous communications, and high-risk exceptions
- •Approve recommended resolutions for booking, amendment, or settlement discrepancies
- •Handle counterparty disputes and unresolved reconciliation breaks
AI Handles
- •Extract normalized trade attributes from unstructured communications
- •Match communications to candidate transaction and settlement records with confidence scores
- •Detect anomalies such as missing amendments, duplicate bookings, and mismatched settlement terms
- •Prioritize exceptions and route cases with supporting evidence and recommended actions
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve a final resolution for low-confidence matches, ambiguous communications, or high-risk exceptions without review by a trade operations or settlements analyst. [S1]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Reconcilia Energy implementations:
Key Players
Companies actively working on Reconcilia Energy solutions: