Financial services
Files you can question, answers with a source, decisions with a trail
The problem
A credit file is a stack of loose documents in formats that do not match, and somebody opens them one at a time. Onboarding stalls because a missing certificate went unnoticed until the file had already passed through several hands, and a claim moves at the speed of whoever has time to open the PDF. When the auditor asks why that case was approved in March, the answer lives in the memory of an analyst who has since left.
What we do
The stack extracts what the file documents carry and connects to the transaction history, and Neuro Intelligence lets you ask questions of that data in plain language: it profiles the sources, builds a semantic layer over your fields and runs the query against the data rather than answering from the model's intuition, read-only throughout. Neuro Desk handles the customer queue across WhatsApp, email and web, resolves what the knowledge base covers while citing the document each answer came from, and escalates to a person when the case decides money. Neuro Ops records every run with its input, output, cost and errors, so reviewing a decision does not start with asking engineering for logs. The model reads language; your core stays the system of record.
- Questions about files and transactions translated into verifiable queries, with every metric definition on show
- Customer answers cited back to the knowledge base rather than improvised
- Human escalation designed in from day one for anything that moves money
- A per-run log with input, output, latency and cost, readable without an engineer
- An owner and a status per agent, so it is clear who answers when something breaks
The modules behind it
Nothing is built from zero. Each solution is a combination of modules that already exist, fitted to your operation.
Neuro Intelligence
This is the module that carries the sector: it profiles the sources you connect, builds a semantic layer over your fields and lets risk, finance and audit ask questions without queueing behind the one person who knows the spreadsheets. It reads and does not write, so the core is left alone.
Neuro Desk
The customer queue in banking and insurance is made of repeat questions about balances, coverage and requirements, and Desk resolves the ones the knowledge base covers, citing the document instead of sounding confident with nothing behind it. Anything that decides money leaves the queue for a person, with the context already summarised.
Neuro Ops
It is the module that turns “trust the agent” into a record somebody can open run by run when a review lands.
Neuro Intelligence
Questions about your data
Dataset
ventas_q3.csv
18 rows · 6 columns
Active filters
Result
$128,200
Revenue · Grouped by Region · 15 rows counted
Rows
Showing 6 of 15 rows
Ask
Text matching against sample rules. No model is connected.
What is only true in this industry
No decision gets signed without evidence
In lending, claims and financial crime work, the question that arrives later is not what you decided but what you decided it on. So every answer ships with its source cited, every agent action is logged, and human review stays in the loop on anything that decides money. That gives you the trail you defend the decision with, not a regulatory opinion.
The data arrives in fragments, owned by different systems
The file is a scanned PDF, an ID document, proof of address and bank statements; the history sits in the core banking or policy administration system; bureau data and screening lists come in from somewhere else again. The stack connects to those sources and makes them queryable together, without becoming the new place where the truth lives.
Residency and access are design inputs, not afterthoughts
Where data is stored, who can see what, and what is allowed to leave your network are decided up front rather than bolted on at the end. We agree with you on each agent's scope, which fields never cross the perimeter and what is retained from each run, before the first source is connected.
What to expect
What to expect is that analysts stop reading whole files to locate a handful of fields, and that the conversation with audit starts from a log instead of from somebody's recollection. How much you get out of it depends on how tidy your sources are and on whether your team keeps the knowledge base current: if the approval criteria only exist in a few people's heads, the system cannot cite them either. We will not promise you a percentage, we do not publish client results, and anyone who quotes you a figure without seeing your data is guessing.
Further reading
AI process automation: 10 processes your company can automate today
AI process automation: 10 concrete processes, what each one gains and what it does not fix. With cited sources from NBER, Science, the OECD and the U.S. Census.
How to implement AI in a company: a step-by-step guide
How to implement AI in a company, step by step: pick the process, audit data, prototype, production, governance. Cited sources: MIT, Gartner, BCG, McKinsey.
AI Agents for Business: What They Are, How They Work and Real Use Cases
What AI agents for business are, how they differ from chatbots and RPA, how they work and which use cases already run today. With sources cited from Gartner.
Does this look like your case?
Tell us how your operation runs today and we will tell you which modules apply, what would need adjusting and how long until it runs. If your industry is not on this list, the conversation is still worth having.
Six building blocks, agents, knowledge, systems, vision, apps, voice, combined into whatever your business needs next