Intelligent automation for the workflows that keep businesses running.
Operations, finance, and back-office teams spend enormous time on repetitive data work - reconciliation, report generation, document processing, exception handling. AI agents that reason over structured and unstructured data can transform throughput without scaling headcount, while maintaining full audit trails and human oversight on every resolution.
The challenge
Why AI for Operations & Back-office?
Operations teams have tried automation before. Rules engines. RPA tools. Spreadsheet macros. They all hit the same wall: real-world operational data is messier than any rule set can encode reliably. Transactions come in with non-standard counterparty formats. Documents arrive with slightly different layouts than the template expected. Exception rates creep up. The automation that was supposed to free up time creates a new category of work: managing the automation. The reason AI reasoning works where rules fail isn't magic - it's that it can handle ambiguity. A reconciliation agent doesn't need an exact rule for every counterparty format variation; it reasons over the evidence the same way a skilled analyst would. The key design insight: you don't need to automate the decision. You need to automate the investigation. Let AI do the data gathering, cross-referencing, and evidence assembly that consumes 90% of each case's time. Keep the human in the loop for the 10% that is actual judgment.
Common pain points
Manual reconciliation consuming senior analyst time
40+ hours per week of skilled ops analyst time spent matching transactions, tracing discrepancies, and preparing resolution paperwork - work that is 90% pattern recognition and data assembly, 10% genuine judgment. A resource cost that grows with transaction volume.
Rule-based automation that breaks on edge cases
Every ops team has tried rules engines. They work for 80% of cases, then break on the 20% that don't fit the exact pattern. Exceptions pile up. The team spends more time managing edge cases than they did before automation. The project gets quietly abandoned.
Reports that take all day and arrive stale
Weekly ops reports require pulling data from 3–5 systems, combining in Excel, formatting for leadership, and sending. By the time the report is ready and reviewed, the underlying data has changed. Decisions are made on information that is already hours or days out of date.
Document processing backlogs that never shrink
Incoming documents - invoices, purchase orders, contracts, claims - require manual extraction of key fields before they can be processed in downstream systems. Volume spikes and backlogs form. Approval cycles slow. Supplier relationships suffer.
Audit trails that don't hold up to scrutiny
Manual processes leave documentation gaps. When an auditor asks how a specific decision was made last quarter, the answer is reconstructed from memory and email chains. That's a compliance risk and a governance failure waiting for an audit to expose it.
Use cases
Agent workflows for Operations & Back-office
Real workflows we design, build, and deploy - not theoretical concepts.
Transaction Reconciliation Agent
Trigger
Daily reconciliation job completion or real-time discrepancy threshold breach
Workflow
Pull discrepancy reports from all source systems → normalise data into a canonical schema → classify discrepancy type (timing mismatch, partial settlement, counterparty format error, currency delta) → retrieve relevant transaction records from each system → assemble evidence package with root cause analysis → generate draft resolution recommendation with compliance documentation → route to human reviewer for one-click approval
Outcomes
Reconciliation processing time reduced 60–80%. Human effort focused on genuine exceptions requiring judgment, not routine data gathering.
35+ hours per week returned to ops team. Escalation rate under 5% maintained across 200,000+ cases.
Systems involved
- ERP / accounting system
- Banking APIs
- Internal transaction database
- Compliance documentation system
Human oversight
Every resolution requires human approval before execution. Audit log records every agent decision, evidence retrieved, and human override.
Automated Report Generation Agent
Trigger
Scheduled (daily, weekly, monthly) or on-demand request
Workflow
Query configured data sources (data warehouse, BI tools, CRM, ERP) → aggregate and validate data → generate narrative commentary with AI-written analysis of trends, anomalies, and highlights → format to the defined report template → validate figures against prior period → deliver to configured recipients via email, Slack, or portal
Outcomes
Reports delivered automatically with accurate figures and AI-written narrative commentary. Human reviewer checks and approves before distribution.
Report compilation time reduced from half-day to minutes. Data freshness improves because reports can run on shorter cycles.
Systems involved
- Data warehouse
- BI tools (Power BI, Tableau)
- CRM
- ERP
- Email / collaboration platform
Human oversight
Figures validated against source data before delivery. Significant anomalies flagged for human review before the report is distributed.
Document Processing & Field Extraction Agent
Trigger
Document received via email, upload portal, or document management system
Workflow
Classify document type (invoice, PO, contract, claim) → extract structured fields using OCR and LLM-based extraction → validate extracted data against expected formats and business rules → flag low-confidence extractions for human review → push validated data to downstream system (ERP, approval workflow, database) → archive original document with extraction metadata
Outcomes
Document processing time from hours to minutes. Extraction accuracy of 95%+ on standard document types. Human review focused on exceptions and ambiguous cases.
Processing backlogs eliminated. Invoice approval cycles shortened. Data entry errors reduced significantly.
Systems involved
- Document intake (email, portal, DMS)
- OCR engine
- ERP / accounting system
- Approval workflow platform
Human oversight
Low-confidence extractions always routed to human review before downstream processing. Original documents archived for audit with full extraction metadata.
Exception Management Workflow
Trigger
Exception flagged by any upstream process (reconciliation, document processing, order management)
Workflow
Receive exception record with context → classify exception type and severity → retrieve relevant records and policies → assess resolution options against business rules → draft resolution recommendation with supporting evidence → route to appropriate approver based on exception type and value → track through to resolution and log outcome for model improvement
Outcomes
Exception resolution time reduced 50–70%. Standard exceptions resolved in minutes. Complex exceptions arrive at the right human reviewer with all context pre-assembled.
Exception backlog eliminated. Resolution quality improves from consistency. Approvers spend time deciding, not gathering.
Systems involved
- Exception queue management system
- Source system APIs
- Policy library
- Approval workflow
- Audit log
Human oversight
All exceptions above defined thresholds require named human approver. Escalation path defined for each exception category.
Compliance Documentation Agent
Trigger
Regulatory reporting deadline or internal audit requirement
Workflow
Retrieve all relevant transactions, decisions, and activity records for the reporting period → validate completeness against regulatory reporting requirements → generate structured compliance report with required fields, supporting evidence, and audit references → flag any gaps or anomalies requiring manual investigation → produce submission-ready documentation package
Outcomes
Compliance report preparation time reduced 70%+. Completeness validation automated. Evidence chain generated automatically rather than assembled manually.
Regulatory submissions produced faster with higher completeness scores. Audit queries answered with readily available evidence packages.
Systems involved
- Transaction records
- Internal audit log
- Regulatory reporting templates
- Document generation system
Human oversight
Compliance officer reviews and approves all regulatory submissions. Gaps flagged for investigation rather than silently filled.
Our approach
How we work in Operations & Back-office
Operations teams have tried automation before. Rules engines, RPA, and spreadsheet macros all hit the same wall: real-world data doesn't fit the exact patterns rules were written for. Our approach is different: we use AI reasoning where rules fail - not to replace human judgment on complex decisions, but to automate the investigation and evidence assembly that consumes 90% of each case's time. The reframing that unlocks these projects: stop trying to automate the decision. Automate the investigation. Keep the human in the loop for the 10% that is actual judgment. Compliance teams approve it. Ops teams trust it. And because humans stay in the loop, accuracy is higher from day one than any fully automated system.
Automate the investigation, preserve the decision
The key insight in ops automation: most cases aren't complex decisions - they're time-consuming investigations. Automate the data gathering and evidence assembly; let humans approve the resolution in one click. This keeps compliance teams on board and humans where they add most value.
Evaluation harness before production code
We build test suites from historical case records - including every case that previously defeated automation - before writing production code. This gives ops teams a concrete, auditable quality baseline to validate against from day one.
Escalation rate as a weekly health signal
A rising escalation rate means data drift or new case types the agent wasn't designed for. We track it as a weekly leading indicator, not just a deployment metric - so problems are caught and addressed before accuracy degrades in production.
Framing for compliance teams matters
'Automated decision-making' gets rejected. 'Automated investigation with human-approved resolution' gets approved. We've learned that the framing of a proposal to a compliance team is a design decision, not just a communication choice - the same architecture described differently determines whether the project proceeds.
Our non-negotiables
What we never do in Operations & Back-office AI
Trust is built by constraints as much as capabilities. These are ours.
We never fully automate decisions that require compliance sign-off without an explicit named-human approval step
We never skip the evaluation harness - every deployment has a defined test suite built from historical cases before production
We never hide the escalation path - every agent output includes a clear human override mechanism
We never deploy without immutable audit logging - every agent decision, data retrieval, and human override is permanently recorded
We never go live without an accuracy SLO defined and agreed with the ops team
Proven results
What we've delivered in this space
Numbers from real engagements - not estimates or benchmarks from someone else's project.
Reduction in processing time
FinTech ops reconciliation agent: 40+ hours/week manual work reduced to under 10, across 200,000+ cases processed.
Escalation rate
Maintained over 200,000 cases - indicating stable agent performance and appropriate handling of edge cases.
Returned to the ops team
Time previously spent on manual investigation and evidence assembly, redirected to judgment and exception review.
Recommended services
What we typically build for Operations & Back-office teams
Questions we always get
Common questions from Operations & Back-office teams
Proven results
What we've delivered in Operations & Back-office
Real outcomes from real projects. See how we've helped operations & back-office teams automate workflows, ship faster, and scale with AI.
Ready to scope a Operations & Back-office AI project?
Book a 30-minute discovery call. We'll tell you what's feasible, what's realistic, and what to build first — with a clear timeline and cost estimate.