01
Extract the evidence
Read documents, emails, forms, and connected data into structured facts while retaining links to the source.
Logic-based AI for financial services
The right AI for work that has to be right.
Onteric extracts and reconciles financial data, follows the rules you define, and produces precise, explainable outputs for regulated workflows.
Precision-critical AIEvidence. Rules. Defensible output.
Trusted by leading brands
The decision boundary
Use generative AI for language. Use logic when the answer must hold up.
Generative AI is valuable for drafting, summarising, and exploring ideas. Regulated work often has a different requirement: the answer must be exact, repeatable, traceable to evidence, and constrained by explicit rules.
Generative AI is well suited to | Logic-based AI is designed for |
|---|---|
| Drafting and rewriting | Extracting exact data from financial documents |
| Summarising long material | Reconciling facts across documents and systems |
| Brainstorming and creative variation | Applying calculations, policies, and eligibility rules |
| Conversational exploration | Producing the same output from the same evidence |
| Low-risk assistance with human review | Preserving a complete evidence and reasoning trail |
The question is not which architecture is universally better. It is whether the workflow rewards useful variation or requires controlled, defensible execution.
Before you adopt AI
Five questions that reveal whether a workflow needs logic.
If most answers are yes, begin with an architecture built for precision, control, and auditability.
- 01
Is accuracy legally or operationally required?
If a wrong figure can affect affordability, eligibility, compliance, or customer treatment, approximate extraction is not enough.
- 02
Must the result be explainable?
Teams should be able to show the source evidence, rule, calculation, and reasoning path behind a result.
- 03
Must the same input produce the same output?
Regulated processes need repeatable outcomes so reviewers can reproduce what happened and test policy changes confidently.
- 04
Must the system stay inside defined boundaries?
The architecture should operate only on permitted evidence, within controlled environments, and through approved actions.
- 05
Must the system follow your business rules?
Policies, thresholds, exceptions, and calculations should be represented as inspectable logic—not hidden inside a prompt.
From evidence to answer
A controlled path from documents to defensible action.
Onteric combines language understanding with explicit logic. It can interpret complex financial documents while preserving control over how facts are reconciled and what happens next.
02
Map and reconcile
Map each fact to your definitions and workflow model, then identify conflicts, omissions, and relationships across sources.
03
Apply explicit logic
Run the policies, calculations, thresholds, and exception rules your process requires.
04
Preserve the trace
Keep the evidence, checks, intermediate steps, and final output connected for review and audit.
Auditability by design
Every answer keeps its receipts.
Review the evidence used, the rule that fired, the calculations performed, and the route to the conclusion. When a policy changes, teams can update the logic directly and see exactly what changed.
DECISION_TRACE / 0042 verified
SOURCE
application.documents.income
RULE
affordability_policy.v3 / threshold_check
EVALUATION
verified_income → policy_threshold → satisfied
RESULT
Decision supported
Illustrative trace showing how evidence, policy logic, and outcomes remain connected.
Business understanding in practice.
These financial and operational examples show how Onteric connects documents, data and explicit rules. They illustrate the context behind a workflow, not the full definition of company brain.
Mortgages
Fact find and income and expenditure
The work
Advisers print off bank statements, cross check against payslips, re-key that information, which slows cases down, distracting advisers from building relationships with clients.
With company brain
Understands the whole case file in seconds, flag missing documents, data contradictions, completes fact finds, including income and expenditure, automatically. Even matching against criteria where relevant.
Measured by: adviser time per case, time to close case, I&E accuracy, document-processing cost.
Financial advice
Onboarding and annual reviews
The work
Onboarding a new client and conducting annual reviews can involve hours of pulling data from different places and cross checking it, and that’s before you look at what changed. Time is spent on admin not advice.
With company brain
Onboarding a new client and conducting annual reviews pulls data automatically from all relevant sources including email, CRMs, policies and previous fact finds so all the prep-work is done for the adviser.
Measured by: admin hours per client, adviser capacity released.
Conveyancing
Source-of-wealth validation
The work
Conveyancers cross-check statements, gift letters and sale proceeds across a high-liability AML file.
With company brain
Figures are reconciled across every document type, unexplained gaps are identified, and the evidence and logic behind every check is logged and audited.
Measured by: review time per file, exceptions found before submission, to-and-fro with client.
Insurance
Claims evidence and policy rules
The work
Assessors compare invoices, reports and policy documents by hand, slowing decisions and making consistency hard to demonstrate.
With company brain
Claim evidence is structured, coverage rules applied exactly, and only genuine exceptions go to a human assessor.
Measured by: handling time, referral rate, decision consistency.
Healthcare finance
Invoices against approved spend
The work
High invoice volumes make line-by-line checks against purchase orders, budgets and approved suppliers slow and error-prone.
With company brain
Every invoice line is matched to approved records, with the exact price, quantity, supplier or duplication discrepancy shown.
Measured by: FTE hours saved, overpayment identified.
Finance operations
Bank reconciliation and accounts payable
The work
Finance teams match transactions to invoices and purchase orders by hand, then spend month-end chasing exceptions.
With company brain
Bank and AP data are matched against your rules, with missing evidence, duplicate payments and mismatched amounts escalated automatically.
Measured by: reconciliation hours, unresolved exceptions, time to close.
Validated
Eliminate the jobs you don't want to do
“We partnered with Onteric to free our advisors to focus on what drives revenue. The ROI case was clear from day one.”
1 hoursaved per loan case
Up to 20%more accurate than manually completed/best-guess I&E assessments
Traceablefields linked back to source evidence
These measured outcomes belong to the Mojo Mortgages workflow. Results for other applications should be established through a scoped pilot rather than inferred from this deployment.
Frequently asked questions
Logic-based AI, without the black-box answers.
Clear distinctions for teams deciding where AI can assist—and where the architecture needs stronger control.
What is logic-based AI?
Logic-based AI represents policies, relationships, calculations, and decision criteria as explicit rules. Instead of generating a plausible response, it follows permitted reasoning paths to produce an output that can be inspected and reproduced.
How is logic-based AI different from generative AI?
Generative AI predicts useful language and is well suited to drafting, summarising, and conversational tasks. Logic-based AI applies defined rules to evidence, making it more suitable when outputs must be exact, consistent, explainable, and auditable.
What is deterministic AI?
Deterministic AI produces the same output when it receives the same input under the same rules. That repeatability matters in regulated workflows because teams need to reproduce outcomes, test changes, and investigate exceptions.
What makes an AI output auditable?
An output is auditable when reviewers can inspect the source evidence, rules, calculations, checks, and intermediate reasoning that produced it. A generated explanation after the event is not the same as preserving the actual decision path.
Can generative AI be used safely in financial services?
Yes, when the task and controls match the risk. Drafting, summarisation, and low-risk assistance may suit generative AI. Precision-critical extraction, reconciliation, and rule-based decisions require stronger guarantees around consistency, evidence, and control.
Can AI extract financial data without generating new information?
Yes. Onteric’s document workflow is designed to read, structure, and map evidence rather than generate missing values. Retained source links let teams inspect where extracted facts came from.
Which workflows are best suited to logic-based AI?
Look for document-heavy workflows that repeat the same evidence gathering, reconciliation, calculation, policy, or exception-handling steps. Mortgage processing, financial-advice preparation, insurance claims, source-of-wealth checks, and finance reconciliation are strong examples.
Does logic-based AI replace human review?
Not automatically. The goal is to automate repeatable work and route genuine exceptions to people with the evidence and reasoning already assembled. The appropriate review model depends on the workflow, risk, and organisation’s governance.
How does Onteric integrate with existing systems?
Onteric provides APIs and embeddable components for document extraction and workflow integration. Teams can introduce it into an existing journey rather than replacing every surrounding system.
Put logic-based AI to work
Bring us one difficult workflow.
Tell us where manual work, fragmented evidence, or black-box AI is slowing your team down. We’ll help identify the right place to begin.