ChronexaFINANCIAL SERVICES · FINTECH · WEALTH
Onboarding, reconciliation, reporting and research still run on people re-keying numbers between systems. We build the AI that does that work inside your perimeter, cites the source of every figure, and stops for a person wherever a regulator would ask who decided.
Built in your cloud tenant. Every action logged.Sensitive data never trains a public model, and every model call can be traced back to its source document.
Choose an action to see what it writes back across your systems.
01 / WHERE THE HOURS GO
These are the four places the work leaks most often. Most firms have at least two of them running right now.
Documents arrive in pieces, get checked by hand, and the missing one is discovered on day five. Every day the account is not open is revenue you are not booking.
Matching bank, ledger and processor data is mostly mechanical. The judgement is in the handful of breaks, but finding them takes days.
Reports are rebuilt every period from exports and pasted tables. When an auditor asks for the lineage of one figure, it takes a week to reconstruct.
Filings, transcripts and data feeds are read line by line before any analysis starts. That is the part a well-built copilot does in minutes, with citations.
02 / WHERE AI PAYS BACK
Ranked by how fast they usually pay back. The free audit puts your own numbers in the last column.
| Use case | Today | With Chronexa | Typical impact |
|---|---|---|---|
| KYC & client onboarding | Documents checked by hand, gaps found late, 7–10 days to open | Documents read and screened on arrival, the exact gap requested the same hour | 60–70%faster time to open |
| Invoice & AP processing | Invoices keyed into the ERP, three-way match by hand | Read in any format, matched automatically, only exceptions reach a person | 80%less manual handling |
| Bank & ledger reconciliation | Days of spreadsheet matching every month end | Continuous matching, people review only true breaks | 50%+less reconciliation effort |
| Regulatory & management reporting | Rebuilt each period from exports | Generated from governed data with lineage on every figure | Days → hoursper reporting cycle |
| Research & due diligence | Analysts read filings and data rooms line by line | A private copilot answers with cited passages | 70%less review time |
Typical results from systems we have delivered and comparable deployments. Your audit replaces them with your own numbers.
03 / SYSTEMS WE CONNECT
We connect what you already run first. APIs where they exist, MCP servers and careful automation where they do not.
04 / THE CONTROLS
The controls are designed in from the first workflow, not added after something goes wrong.
Deployed in your own cloud tenant or a dedicated instance. Client data never trains a public model, and access follows the permissions you already enforce.
Every extracted field and every research answer links to the document and page it came from, so an auditor can check it in seconds.
Agents prepare, match and draft. Anything that moves money, opens an account or files a report waits for a named approver, and that approval is logged.
05 / THE ENGAGEMENT
Scope, what counts as working, and the price. Built in your own accounts, documented and exportable. The free audit comes first, and so does the first workflow.
RELEVANT WORK
For a mid-market fintech SaaS we rebuilt invoice ingestion that had become a manual backlog: invoices read in any format, a three-way match, and only true exceptions routed to a person. Manual handling fell by about 80% and approvals moved from days to hours.
We have also built a private diligence copilot for a private equity firm, cutting data-room review time by about 70%, and an ML trading-signal platform with a mobile app for a SEBI-regulated advisor, with a compliance playbook written for a regulated domain.
Read the invoice pipeline case study06 / START WITH ONE WORKFLOW
A strategy call covers the process, the systems behind it and where the data is allowed to go. You leave knowing whether AI pays back here, and what it would cost to find out.
You will be talking to the engineers who would build it, not an account manager.info@chronexa.io
Rather write it down first?
A FEW GOOD QUESTIONS
Yes, if it is designed for it from the start. We deploy inside your own cloud tenant or a dedicated instance, keep client data out of any public model training, log every model call and approval, and keep a named person approving anything that moves money, opens an account or files a report. The controls are part of the architecture, which is what a regulator or auditor will ask about first.
It depends on where your data is allowed to go. For many firms that means Claude or GPT through an enterprise agreement with zero data retention, or models hosted in your own Azure or AWS tenant. Where data cannot leave your environment at all, we use open-weight models deployed privately. We choose per workflow, on accuracy and cost per output, and we document the decision.
Both. Most engagements start in operations, onboarding, reconciliation and reporting, because the payback is fastest. We also build research copilots and machine-learning systems, including a trading-signal platform with a mobile app for a SEBI-regulated advisor. That work starts from method and governance, not from a promise about returns.
Every extracted value is grounded in the source document and carries a confidence score. Low-confidence values go to a person with the reason attached, rather than flowing into the ledger. Research answers cite the exact passage they rely on. If something cannot be traced to a source, it is not presented as fact.
There are no packages. The audit is free and so is the first workflow. After that, each build is quoted against the hours, cycle time and risk it removes, and you approve the scope and price before work starts.