Operations for finance, run by AI and people.
AI handles the volume: triage, scoring, drafting. Our people handle the judgment: approvals, exceptions, customers.
Six operations, one model: AI does the volume, people make the decisions.
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Support desks for policyholders, account holders, merchants and agents.
- AI
- Routes intents, answers from your policy documents, drafts replies and scores every call.
- People
- Trained agents approve what customers see and take the hard conversations.
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Alert review and investigations for payments, cards and AML programs.
- AI
- Scores known patterns, flags new anomalies, maps rings and mule networks, drafts case narratives.
- People
- L1 and L2 analysts decide, investigate and sign off every report.
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Underwriting support, in-loan monitoring and collections across the credit life cycle.
- AI
- Extracts KYC and KYB documents, scores with reason codes, segments delinquent accounts.
- People
- Underwriters rule on exceptions. Collectors work the accounts that need a person.
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Reconciliation, document processing and exception handling with an audit trail.
- AI
- Matches statements, classifies and validates documents, routes exceptions.
- People
- Operations staff clear the breaks and own the controls.
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Proactive outreach and suitable recommendations for account holders and investors.
- AI
- Scores churn risk nightly, drafts outreach matched to each segment, filters for suitability.
- People
- Relationship managers approve every message before it goes out.
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Research desks for banks, lenders, wealth and asset managers.
- AI
- Reads news, filings, rate sheets and regulatory notices. Tracks borrower sectors, macro signals and competitor loan terms. Produces sourced signals.
- People
- Analysts write the view: credit and sector memos, rate benchmarks, regulatory alerts. Nothing trades or lends on its own.
- Insurance
- Banking
- Wealth & asset management
- Fintech
- AI-assisted support
- Fraud & AML operations
- Credit & collections
- Back-office automation
- Retention copilots
- Research desks
- Custom software development
- People approve every action
- Audit-ready by default
Custom software development
Software built for regulated finance, from the first line to the audit.
Portals, workflow systems, integrations and data platforms built to your specification by engineers who have shipped inside banks, insurers and fintechs. We can run it afterwards, or hand it over clean.
Scope a build- Customer, agent and broker portals
- Onboarding, self-service, policy and account servicing, claims and document upload, with role-based access and an audit trail on every change.
- Loan and policy workflow systems
- Origination, underwriting queues, approvals and exception handling, with decision engines that give a reason code for every outcome.
- Core-system integrations
- Policy admin, core banking, CRM, carrier portals and ledgers connected through APIs and event pipelines. No rip and replace.
- Reconciliation and reporting engines
- Commissions, settlements, fee billing and regulatory reports that match automatically, route exceptions and export for finance and auditors.
- Data platforms and feature stores
- Governed data marts with lineage, access control and quality monitoring that models, dashboards and regulators can trust.
- AI copilots and agents
- Retrieval over your documents, drafting assistants and agent workflows with the approval queue and audit log built in from day one.
- 01
Discovery sprint
Two weeks with your operations, risk and IT teams: scope, data sources, controls and a fixed price.
- 02
Fixed-scope build
Milestones, weekly demos and a security review before every release. You see working software from week three.
- 03
Run or hand over
We operate what we built with service levels, or hand it over with documentation, training and a warranty period.
- You own the code and the data
- Works inside your stack and your cloud
- SSO, role-based access and encryption as standard
- Documentation and handover included
Where AI and people work the same queue. Six operations we run for insurers, banks, wealth managers and fintechs.
- Support · Insurance
AI-assisted policyholder desk
- AI drafts replies from policy and billing records
- Agents approve or edit before anything is sent
- Every interaction scored, not a two percent sample
- Escalations stay human end to end
- Risk · Fintech
Fraud and AML alert operations
- Supervised scores for known patterns, anomaly scores for new ones
- Graph analysis surfaces rings and mule networks
- Case narratives drafted for analyst sign-off
- False-positive rate reported weekly
- Operations · Insurance
Commission reconciliation with an exception desk
- Carrier statements ingested and matched automatically
- Exceptions routed to a named analyst
- Agent hierarchy and split logic applied
- Audit export for finance
- Risk · Banking
Credit early-warning and collections pod
- Monthly early-warning scores with reason codes
- Delinquent accounts segmented: likely to self-cure or serious risk
- Collectors focus on the accounts that need a person
- Fair-lending checks on every model change
- Experience · Wealth
Retention copilot
- Churn model screens every account overnight
- Copilot drafts outreach matched to the segment
- Relationship managers approve at nine in the morning
- Measured as retention lift, not messages sent
- Research · Banking & wealth
Market and credit research desk
- News, filings and regulatory notices retrieved with auditable metadata
- Borrower sectors and macro indicators tracked for early warning
- Competitor loan products, rates and fees benchmarked weekly
- Analysts write the memo. Nothing trades or lends on its own
AI drafts. People approve. Everything is logged.
Every engagement runs on one pattern. AI systems filter and draft at scale. A named person authorises anything that reaches a customer or moves money. Every decision carries a reviewer, a timestamp and the reasoning behind it.
This is not a training wheel we remove once the models are trusted. In regulated finance it is the architecture. It also keeps AI cost predictable: cheap models screen the volume, and expensive reasoning only runs where it matters.
- Reply to policyholder · #4821 AI draft · reviewer M.K. approved 09:12
- Outreach · account 7731 AI draft · reviewer R.O. edited 09:15
- Case narrative · ALRT-1029 AI draft · L2 queue pending
- Limit increase · account 2210 AI recommendation · reviewer J.R. rejected 09:21
Every row carries a reviewer, a timestamp and the reasoning behind it.
How we build
One architecture for every engagement, from the data to the queue your team works in.
Every system we build and run has the same four layers: governed data, models with reason codes, the strategy and monitoring that turn scores into decisions, and the application where people act. It is how we scope, build and report.
- 01
Data assets
Governed marts and feature stores with lineage, disclaimers and access control.
- 02
Models
Scorecards, classifiers, anomaly and graph models, language models for drafting.
- 03
Strategy & monitoring
Thresholds, guardrails, drift checks, champion-challenger and the approval queue.
- 04
Application
Portals, queues and chat integrations where your people actually work.
What the industry has already shown
Publicly reported results from the institutions named. Industry evidence, not Platorix claims.
- 60%
- fewer false positives in AML screening
- Danske Bank
- 80%
- of loan decisions automated at the same risk threshold
- Zest AI clients
- 20–25%
- faster client proposals with a generative AI assistant
- Morgan Stanley
Built the way regulated finance expects.
Each control maps to an obligation your regulator already has: model-risk guidance, fair lending, GDPR Article 22, FINRA 2210, AML programs and DORA.
- People approve money
- Nothing customer-facing or money-moving ships without a named approver. The draft-approve queue is part of the system, not a policy document.
- Every action audited
- Inputs, outputs, overrides, reviewer and timestamp are logged immutably, so an examiner can replay any decision.
- Explainable by design
- Scorecards and reason codes drive decisions. Language models only translate a model's output into plain language. They never decide.
- Drift watched, challengers ready
- Population stability and performance are monitored with escalation thresholds, and a challenger model is tested before any change.
- Fairness tested in the gate
- Disparate-impact tests sit in the validation gate, not in a yearly review. The perception of bias is treated as a material risk.
- Third parties governed
- Model providers and APIs are managed as governed dependencies, with resilience testing and data minimisation before anything enters a retrieval corpus.
Build. Staff. Govern.
Three engagement shapes, one operating model. We map the work first, prove it on one line of business, then scale when you say so.
- 01
Build
Custom software and AI systems on the four-layer stack: portals, workflow systems, integrations, data platforms, models with reason codes, and the approval queues and chat integrations your teams use every day. Fixed scope, weekly demos, you own the code.
- 02
Staff
Managed AI-and-human pods: trained agents, analysts and engineers working inside your processes with the copilots we build, plus cross-training for your own staff so the capability stays with you.
- 03
Govern
Model risk and AI governance: fairness audits, drift monitoring, documentation, and a mapping from each regulation you face to the control that satisfies it, with a pre-production checklist before anything goes live.
Put AI and a team behind your operation.
Tell us which queue hurts most. We'll come back with a team, the AI it needs, the controls around it and a number.