Financial Services

AI Infrastructure & MLOps for Financial Services

AI Infrastructure & MLOps for financial services, built around the constraint that defines the sector: every automated decision must be explainable and reproducible months after the fact.

Regulations in scope
5
Systems we integrate
5
Typical first release
6 weeks

What changes when it is financial services

Orqent Labs builds AI infrastructure sized to the workload you actually have, with the rollback paths you will eventually need.

In financial services, every automated decision must be explainable and reproducible months after the fact. That single fact reshapes how ai infrastructure & mlops has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is credit memo drafting, usually integrated against regulatory reporting platforms. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. Six weeks to something running in production, not six quarters to a strategy document.

The sector constraints we design around

Defining constraint
every automated decision must be explainable and reproducible months after the fact
Regulations in scope
RBI guidelines · SEBI regulations · DPDP Act 2023 · PMLA and AML rules · IRDAI where insurance applies
Systems of record
core banking · trading and OMS · loan origination · SAP and Oracle financials · regulatory reporting platforms
Where we usually start
credit memo drafting

AI Infrastructure & MLOps workloads in financial services

  • credit memo drafting
  • KYC and onboarding checks
  • regulatory report assembly
  • reconciliation
  • client communication review

What is included

  • Workload sizing based on measured throughput, not guesses
  • Model registry and versioned deployments
  • Autoscaling and cost-per-inference monitoring
  • Canary and rollback deployment paths
  • On-premise or air-gapped options where required
  • Runbooks and on-call documentation

Questions from this sector

Can we use AI in credit decisions?

With explainability, documented model governance and human review on adverse outcomes, yes. RBI expects you to be able to explain any decision that affects a customer.

How do you handle data residency?

Deployment inside Indian regions or on your own infrastructure, which is the usual requirement for regulated financial data.

Cloud or on-premise?

We model both against your real volume. On-premise typically wins at sustained high throughput or where data residency is non-negotiable; cloud wins on variable and early-stage workloads.

Can you deploy air-gapped?

Yes, with open-weight models and a fully offline inference stack, the usual pattern for defence, and for some healthcare and government work.

Do you support our existing Kubernetes setup?

Yes, and we would rather extend it than introduce a parallel platform your team has to learn.

AI Infrastructure & MLOps for financial services, worth a conversation?

Tell us the workload and the regulation it sits under. We will tell you what is realistic.

Or email bd@dtrasglobal.com · call +91 74118 77878