Exception investigation
Bring records from different systems into a structured investigation. Identify discrepancies, examine supported causes, and prepare a case for review.
Operational intelligence for enterprise teams
Intelligence that understands the work.
IntelligenceOS designs and deploys AI systems around your operations—connecting company knowledge, business rules, and existing systems so teams can investigate exceptions, assess the evidence, and act with clear oversight.
Start with one important workflow · Establish its value · Build from there
- 01 · EvidenceTMSMilestone missedCarrier portalLast scan 31h agoOrder systemPromised dateContractLate-delivery clause
- 02 · DomainShipmentCarrierMilestoneOrderPolicy · late delivery v3
- 03 · Investigation
- Milestone gap 31h calculated
- Carrier scan conflicts with TMS 2 records
- Customs hold unconfirmed
- 04 · Review
Recommend: notify customer, open carrier claim
Requires approval · Operations lead - 05 · ActionCase updated in service desk Read-back verified
The operational problem
The knowledge is there. Bringing it together takes work.
An issue appears in one system. The explanation sits in another. The rule is in a document, and the person who understands the exception is already busy.
IntelligenceOS brings that context into the workflow. We help you build systems that gather the relevant evidence, apply business rules, and prepare a clear next step for the people responsible.
01 · Source system
The issue appears
An exception raised in one system
02 · Another system
The explanation sits
Records that never meet the first
03 · A document
The rule is written
Policy, contract or procedure
04 · A person
The exception is understood
Who is already busy
What we build
AI systems for operational decisions and actions.
Each system connects your knowledge, business rules and existing tools to help teams investigate problems, review evidence, and take the next step.
Decision support
Connect recommendations to current evidence and business policies. Make assumptions, missing information, and escalation needs visible.
Controlled execution
Connect approved decisions to your task and case systems. Define who can authorize an action and verify the result in the destination system.
Workflow examples
A shared method. A system shaped to your domain.
The approach can be applied to different operational workflows. Each engagement is scoped around the process, evidence, and decision rights involved.
01 · Logistics
Shipment exceptions and delivery investigations
ShipmentMilestoneCarrierOrder
Initial boundaryRecommend a response; prepare a review case
02 · Manufacturing
Quality deviations and supplier issues
BatchInspectionMachineSupplier
Initial boundaryAssemble evidence; route for quality review
03 · Construction
Procurement delays and project issue triage
PackageSupplierMilestoneContract
Initial boundaryRecommend escalation; draft the issue record
04 · Finance operations
Reconciliation and settlement discrepancies
TransactionLedger entrySettlementAccount
Initial boundaryCalculate the variance; prepare evidence for a reviewer
05 · Service operations
SLA breaches and complex case investigations
CustomerCaseAgreementEvent
Initial boundaryFind the cause; propose the next action
06 · Your operation
The workflow that keeps slowing your team down
Wherever a team investigates exceptions across systems, checks the rules and decides what to do next, the same method applies.
Discuss your workflowCandidate applications of one workflow family — exception investigation and resolution support.
How we work
From a defined problem to a working capability.
Four stages, each ending in evidence your team can review before the next one begins.
01
Understand the workflow
We work with your team to understand the decisions, systems, and exceptions involved. Together, we establish the baseline and what a useful result looks like.
Exit evidenceAn agreed workflow map and baseline
02
Connect the domain
We map the relevant data, terminology, policies, and tools. Access and action boundaries are defined around the people and systems responsible.
Exit evidenceA versioned domain package and validated data access
03
Build and evaluate
We develop a focused workflow and test it against representative cases. Your reviewers help assess the findings, evidence, and failure behavior.
Exit evidenceAn acceptance report, including unresolved failures
04
Introduce and improve
We support rollout, monitor agreed measures, and refine the system through controlled changes. Further workflows are scoped as the value and requirements become clear.
Exit evidenceAgreed go-live, an operating report and controlled releases
Our approach
Built on Domain Intelligence.
Domain Intelligence is our engineering philosophy and strategy for making AI useful in a specific operating environment.
It brings together domain knowledge, business context, deterministic tools, workflow design, and evaluation. The model is one part of the system. The surrounding engineering determines what it can access, how it reasons with evidence, and what it is allowed to do.
IntelligenceOS applies this philosophy to the work inside your business.
Domain knowledgeWhat things mean in your operation
Business contextEntities, relationships, rules and policies
Deterministic toolsCalculations and checks that must be exact
Workflow designWho reviews, and what may change
EvaluationWhether it works on your own cases
ModelOne part of the system
Oversight and evidence
Clear evidence. Defined authority.
Our approach links material findings to supporting records, uses software to enforce permissions, and routes decisions for human review where the workflow requires it.
We make unresolved questions visible and verify actions in the systems where they occur. Deployment requirements—including hosting, model access, data handling, and support—are agreed for each engagement.
- Material findings linked to supporting records
- Permissions enforced by software
- Human review where the workflow requires it
- Unresolved questions made visible
- Actions verified in the systems where they occur
Every deployment answers
Five questions, agreed before anything goes live.
- 01Which work does it perform?
- 02Which systems and evidence does it use?
- 03What can it decide or change?
- 04When does a person take over?
- 05What measurable improvement justifies the cost?
Engagement
Begin with one workflow worth improving.
The first step is a focused assessment of the process, data, and business case. From there, we define a deployment scope and a clear way to evaluate it.
For accepted deployments, ongoing support can cover monitoring, evaluation, maintenance, and agreed improvements.
Workflow assessment
One process, its users, source systems and value case.
- Baseline
- Workflow map
- Data and access assessment
- Acceptance criteria
- Deployment proposal
Initial deployment
One workflow, one team, agreed source integrations and one review destination.
- Configured application
- Domain package
- Evaluation report
- User training
- Operating runbook
Operate and improve
Agreed volumes, support hours, monitoring, releases and review cadence.
- Monitoring
- Evaluation updates
- Incident handling
- Approved releases
- Monthly operating report
Each stage has its own scope and acceptance boundary. New integrations, workflows or business units are scoped separately.
A good first workflow has
Large-company ambition is compatible with a small starting point.
- Recurring work with enough volume or consequence
- A named process owner and budget sponsor
- Accessible, permitted data
- Operators available to explain the process and review cases
- A measurable baseline and an agreed definition of success
FAQ
Questions teams ask first.
Do we need to replace our existing systems?
We assess how the workflow can connect to your existing systems. The integration approach depends on the interfaces, access, and infrastructure available.
Is this a chatbot?
An interface may include conversation, but the engagement is built around an operational workflow: gathering evidence, checking rules, preparing recommendations, and carrying out permitted actions.
Will the system act autonomously?
Authority is defined for each workflow. We begin with clear review and approval boundaries, then evaluate whether specific actions are suitable for greater automation.
Do you train a custom model for every company?
We select the approach from the problem and the evidence. Retrieval, tools, rules, and workflow design may be sufficient. Model specialization is considered when it addresses a measured need.
How long does deployment take?
We provide a scoped schedule after assessing the workflow, integrations, access, and validation requirements.
How do you measure value?
We agree a baseline and measures such as handling time, rework, evidence quality, completion rates, and cost. Results are assessed against the workflow your team uses today.
Founded by
Ugo Chukwu — ai & financial systems engineer. The engineering notes behind Domain Intelligence are published on Substack ↗; educational builds are on ethercess.com/work.
Educational engineering work — not client deployment results.
Discuss your workflow
Bring us the workflow that keeps slowing your team down.
Tell us what the team is trying to do, where the process breaks down, and which systems are involved. We will explore whether a focused IntelligenceOS deployment is a good fit.