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nexeai
AI for finance, banking and insurance

Prepare the controls. Keep the decision.

In a sector where data is sensitive and compliance requirements are strict, we can design AI solutions whose architecture accounts for your security and compliance constraints.

Tell us about your constraints
case · document reviewExample
Case 29401 · onboardingReview required

received this morning · 7 documents expected

Checks passed

  • vérifié : Identity matched against the documents supplied
  • vérifié : Required documents present
  • vérifié : Data consistent across the documents
  • à vérifier : Proof of address missing
AgentDocument request prepared

letter drafted from your template · not sent

Banks, insurers, asset managers and finance departments spend a considerable amount of time collecting, formatting and checking information. These are tasks AI can support, provided a strict framework is set for the data and the controls.

In practice, AI can read a contract and extract the useful information, prepare a report, flag a reconciliation anomaly or answer a business question from your procedures. Your teams read and approve: the decision stays human.

We build these systems end to end for French financial players, with security and compliance as the starting constraints: local, hybrid or cloud architecture, documentation of the flows, the permissions and the controls. Tell us about your constraints in a first conversation.

Where AI steps in

Inside the processing chain, never beside it

An agent does not replace a step: it prepares the next one and hands back. Here is where this is worth it, and where it is not.

Client or file

  1. Onboarding

    expected documents, checks

    Agent

    Checks that documents are present and consistent

    list of missing documents

    Humain

    The account manager follows up with the client

  2. Controls

    internal rules, consistency

    Agent

    Applies your rules and flags what stands out

    ambiguous cases raised, never decided

    Humain

    The middle office decides

  3. Analysis and reporting

    notes, summaries, tables

    Agent

    Gathers the elements and prepares a first draft

    a summary that links back to its sources

    Humain

    The analyst concludes and signs off the note

  4. Decision and commitment

    approval, refusal, execution

    No agent here, and that is not a gap: committing the institution is decided and signed.

What it changes, role by role

Executive or finance director
You want to modernise operations without creating a compliance risk: we frame the scope, the data involved and the controls with you before any rollout.
Head of operations or middle office
You organise file processing and the controls: AI can prepare the work and flag anomalies, while approval stays human.
Analyst or account manager
You spend time collecting and formatting information: AI gathers, structures and summarises, while you keep the analysis and the decision.
Compliance officer
You need to be able to explain and audit what the systems do: every project comes with documentation of the flows, the permissions and the controls, verifiable at any time.
Three scenarios

Named the way you name them

Nobody goes looking for “document analysis”. They go looking for a file whose status is already established.

Scenario 01

Preparing a documentary review

  1. A file arrives with its documents, more or less complete
  2. The agent checks that every expected document is presentthe agent takes over
  3. It cross-checks the data from one document to another
  4. It prepares the request for more documents and flags the gaps
  5. The account manager approves, rejects, or requests a reviewyou take back the reins
Today
The review starts by listing what is missing, which takes as long as examining the file itself.
With an agent
A file whose status is established before it is even opened, and a request for documents already drafted.

Scenario 02

Preparing a report

  1. The reporting deadline arrives
  2. The agent gathers the data from the approved sourcesthe agent takes over
  3. It compares against the previous period and spots the gaps
  4. It drafts a first version that links back to every source
  5. The analyst checks, comments and publishesyou take back the reins
Today
Formatting eats up the time that should go to the commentary.
With an agent
A sourced first draft to correct, and the time given back to analysis.

Scenario 03

Answering a procedural question

  1. A team is looking for the rule that applies to a case
  2. The agent queries your procedures and your internal documentationthe agent takes over
  3. It reports the exact passage and its reference
  4. It flags the cases where the procedure gives no clear answer
  5. The manager decides and, if needed, updates the ruleyou take back the reins
Today
The same question comes up every month and gets handled from memory.
With an agent
An answer that cites the paragraph, and a record of what the rule did not cover.

The other tasks we keep seeing

Targeted automations, under human control, defined by your requirements and the tests carried out.

Document analysis
Reading and extracting information from contracts, reports and files, with structured, sourced output.
Reporting & summaries
Generating first drafts of reports, notes and summaries from your data, reviewed by your analysts.
Back-office processing
Automating the recurring administrative tasks that weigh on operational teams.
Internal assistants
Assistants plugged into your procedures and documentation, to answer business questions faster.
Regulatory watch
Automated tracking of the regulatory changes relevant to your activities.
Controls and reconciliation
Preparing consistency checks, detecting anomalies and preliminary reconciliation, with approval from your teams.
Onboarding and client files
Collecting and checking the documents in onboarding files, preparing checklists and flagging missing documents.
Architecture and compliance
We can design an architecture suited to your compliance requirements, then document the data flows and the controls chosen.
What bounds the agent

Every action can be traced.

This page's FAQ promises an auditable log. Here it is: on the left what the agent is allowed to do, on the right what it did, to the second.

Before — what is allowed

Every system gets the rights the task needs, and not one more.

Example permissions granted to an agent, tool by tool.
Client registerFile documentsPayment systems
allowed : read a recordallowed : readdenied : read
allowed : log a checkallowed : file and summarisedenied : modify
denied : change a piece of datadenied : deletedenied : execute

Example. The actual split is decided during framing, tool by tool, and access can be revoked.

After — what it did

Every control passed leaves a line.

  1. 10:22:14file 29401 opened
  2. 10:22:167 documents read
  3. 10:22:18rule 14 checked
  4. 10:22:18proof of address missing
  5. 10:22:21request for a document prepared
  6. awaiting human approval

An example. The agent approved nothing: the file is incomplete, and the request for more documents waits for a signature before it goes out.

Your first agent

Where a finance department would start

A first scope is chosen small, and with no direct regulatory impact: one documentary task, its rules, a measurement after thirty days.

first project · documentary reviewExample

Preparing an onboarding documentary review

What the agent covers

  • vérifié : Checking that the expected documents are present
  • vérifié : Checking the data is consistent across documents
  • vérifié : A request for missing documents drafted
  • vérifié : A log of every check carried out
  • exclu : Approving or rejecting the file

What it needs from you

  • your list of expected documents
  • your consistency rules
  • a batch of files already processed

What stays with you

  • approval and rejection
  • deciding on ambiguous cases
  • any communication sent to the client

What we look at after thirty days: the share of files whose status is established with no rework, and the time before the first request for missing documents.

estimate · files per monthYour figures
files / month
minutes

Time consumed today

≈ 50 hper month

This total is yours: it only comes from the two figures alongside. Only part of this time can be automated — we look at the volume, how stable the process is, the exceptions and the data available before saying so.

Get this process reviewed
How a project runs

Four steps, from framing to measurement

We move in stages, to check the value and the compliance before investing in a rollout.

  1. 01

    Frame

    • The tasks to support, the data involved, your security and compliance requirements and the approval rules. You leave with an explicit scope and explicit success criteria.

  2. 02

    Test

    • A first assistant on the chosen case, on real data and within a bounded pilot. You adjust its behaviour with us.

  3. 03

    Deploy

    • Integration with your tools, the architecture chosen, the controls, the documented data flows and the supervision agreed during framing.

  4. 04

    Measure

    • The gap against the success criteria, reviewed with your team. You decide on scope extensions, one step at a time.
Frequently asked questions

Compliance, data and rollout

Straight answers on what AI can do inside a regulated financial environment.

Is AI compatible with compliance requirements?
Yes, if the architecture is defined before use. We document the data flows, the services chosen, the permissions and the controls, and we adapt the hosting to your requirements: local, hybrid or cloud. Nothing is used without your approval.
Is this use case really worth automating?
Not always, and we say so when it is not. We check the volume, the time it consumes, how stable the process is, how many exceptions it has and what data exists before proposing an agent. A rare or unstable task, or one full of special cases, costs more to automate than to do.
Where is our data processed?
Depending on how sensitive the data is and on your obligations, the architecture can be local, hybrid or cloud, with hosts you approve. The choice is made during framing and every flow is documented: no transfer is implicit.
Can AI make financial decisions?
No. It prepares and proposes: it gathers information, structures files and flags anomalies. Any binding decision, such as approving a file or sending it out, stays subject to human approval configured with you.
Can we audit what the AI does?
Yes. Every action is logged: date, data consulted, decision taken, human approval where applicable. You can audit the system at any time, and the project's documentation lets you explain how it works to regulators.
Where should we start in a heavily regulated sector?
With a pilot bounded to a well-defined documentary or administrative task, with no direct regulatory impact. The prototype lets you check the value and the controls before widening the scope, one step at a time.

Let's talk about your compliance constraints

Every financial player has its own regulatory requirements. Let's go through yours, and the use cases worth considering within them.

Book a call

To go further

Other formats complement this offer, depending on where you're starting from.

Bespoke AI agent development

Agents that chain the steps together and use your tools, with the permissions and controls you choose.

AI and automation audit

Two weeks to identify the tasks you can automate, whether they are feasible, and a 90-day roadmap with before-and-after measurement.

AI for accounting firms

Data entry, document processing and report preparation: closely related use cases for accounting firms.

AI agent training

Build your team's skills on AI agents and their business use, to better frame your projects and steer your teams.