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An orchestration agent, scoped with you before it is priced. This agent coordinates several specialised agents. Its orchestration follows your actual workflows — which is why it is scoped with you rather than bought off the shelf. We establish the scope together, then the quotation commits it. The journeys described below form the scope that this review refines and the quotation commits. The specialised agents it coordinates can be ordered today. Request a quote
● Public sector — Public accounting (M57)

The AI agent for public accounting: more reliable entry, the decision stays yours

Keying in entries, checking consistency against the M57 chart of accounts, preparing reminders and payment schedules take up a considerable share of public accountants' time — without being the heart of the job, which is accounting quality and continuity of service. Your AI agent absorbs that repetitive work. Hosted in France — on local inference or an isolated resource — public financial data stays under control. The public accountant keeps the decision.

Hosted in France Public financial data protected GDPR & AI Act: governed deployment Human oversight

Updated on

Deployed in a few weeks
Public accounting assistant · hosted in France
On the main budget, process this batch of 38 payment and revenue orders and flag the M57 discrepancies before transmission to the assigned accountant.
38 documents read. 34 consistent with the M57 chart of accounts. 4 anomalies: one posting to 6068 instead of 60632, one payment order with no supporting invoice attached, one duplicate (order no. 2231 already issued) and VAT wrongly apportioned on a service.
Corrected entries proposed — to check and approve.
⛓ Source · your financial management software + the file's documents
The order with no supporting document — how can we put it right?
Correction: the supplier's invoice is missing to evidence that the service was delivered. Recommendation: suspend the payment order and request the document from the managing department before transmission, to avoid a rejection by the assigned accountant.
I am preparing the document request and recording it in the file, for your approval.
✎ Action · request ready for review — the public officer approves
Local inference · no data outside the EU
Data hosted in France
Sovereign by designLocal inference or hosting in France
GDPR & AI Act: governed deploymentTraceability & human oversight
TurnkeyDesigned, installed and operated for you
The public officer decidesThe AI agent assists, never approves alone
✦ In brief

In a local authority or a public body, a Blue Lemon Agent agent assists public accountants with repetitive tasks — entering and checking the consistency of M57 entries, detecting anomalies and discrepancies, preparing reminders and payment schedules. It runs on local inference or is hosted in France: public financial data is never exposed to a foreign service, architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity. The time saved is redirected towards accounting quality and continuity of service. Live within a few weeks.

100%
hosted in France in the target architecture
0
transfer outside the EU in the target architecture
7
uses ready to deploy on this scope
0
decision taken without human approval

Reference points describing our offer, not results measured at a client. The scale of the gain is confirmed by a pilot on your own scope.

The context

Why AI matters to public finance departments — and why they hesitate

Public accountants have to guarantee accounting quality, the regularity of M57 entries and continuity of service, often with constrained headcount. But available time is mechanically reduced by data entry and checks — and the data involved (budgets, contracts, pay, third-party accounts) is among the most sensitive there is.

! The issue

The finance department is caught between rising demands — reliable accounts, compliance with M57, payment deadlines to suppliers — and an ever-growing production workload (entry, checks, anomalies, reminders). Yet most consumer AI solutions amount to entrusting budgets, contracts, pay and third-party accounts to a third party, often hosted outside Europe and subject to the Cloud Act.

Our answer

For public data, AI is only of interest if it is sovereign and confidential by design. Local inference or an isolated resource hosted in France, systematic human oversight, decisions reserved to the public officer: the time saved on production is never paid for in lost confidentiality, nor in a breach of equal treatment. The aim is not to replace the public accountant, but to give them back time for accounting quality and continuity of service.

The decisive point

Confidentiality of public financial data: sovereignty & compliance

A public finance department handles the authority's most sensitive data. Here is how the architecture of our agents protects it, file by file.

Local inference

The agent can run on a machine belonging to the authority: no document leaves the network, nothing passes through a cloud.

Hosting in France

Otherwise, a dedicated and isolated resource, hosted in France under French law — your data: processing and access within the European Union targeted by the architecture.

Reduced extraterritorial exposure

Exposure of financial data to the Cloud Act and FISA 702 is reduced by design; location alone does not guarantee immunity.

One isolated resource per organisation

No pooling of financial data: an environment strictly dedicated to your authority or institution.

Encryption & controlled access

Encryption in transit and at rest, role-based access (RBAC), strong authentication and logging.

AI Act: governed deployment

An agent strictly in support; no entry approved automatically; traceability and human oversight from end to end.

What depends on the architecture chosen These points are not general guarantees: they are settled deployment by deployment, in the quotation.

  • The applicable location is that of the architecture set out in the quotation and verified before commissioning.
  • Local execution is announced only for the configuration explicitly described and accepted in the quotation.
  • The applicable isolation depends on the deployment mode set out in the quotation; no dedicated isolation is presumed.
  • The encryption mechanisms in transit and at rest, their components and key management are those documented for the architecture chosen.
  • Roles and permissions are configured and accepted for the identities and systems actually connected.
  • The events logged, their content, their retention period and who may access them are defined for the deployment chosen.
For the most sensitive data, SecNumCloud and HDS options are available depending on your requirements. A single architecture is designed to answer both the GDPR and extraterritorial exposure. Designed for deployment in line with the GDPR and the AI Act, after the processing, roles and context-specific risks have been assessed.
Demonstration

See the agent at work

5 real situations, taken from those that come up most often. Pick one: the exchange unfolds as it would in your organisation.

A scripted demonstration. These exchanges show how the agent behaves — its sources, its refusals, what it leaves to your teams. Nothing is sent from this page, no model is queried here, and the matters named are fictional. That is precisely what we promise your data.
The behaviours shown here — monitoring, automation rules, routing and reminders — are configured with you during deployment, from your tools, your rules and your thresholds.
The architecture points named in these exchanges — location, local execution, isolation, encryption, role-based access, logging — are not a guarantee attached to the demonstration: they are those of the architecture set out in your quotation, and verified before commissioning.

The public body in this demonstration

Fictional public body

Town of Roncelles — municipality of 22,400 residents

Sector
Municipality of 22,400 residents — one main budget and three ancillary budgets (water, sewerage, school catering), accounts kept under the M57 chart of accounts
Headcount
6 staff in the finance department — one head of department, three accounting officers, one contracts officer, one revenue officer; these six people alone run the execution of the four budgets
Public served
22,400 residents, 1,180 active suppliers and 46 spending departments — 3,800 enquiries from third parties and departments each year
Order of magnitude
18,400 payment orders and 6,200 revenue orders a year — 24,600 documents split into 78 payment batches, €41m of actual expenditure, 12 call-off contracts in force
Tools in place
Financial management software, the e-invoicing platform, document management and three years of archived entries — the agent plugs into them read-only, nothing is replaced and nothing is migrated
Who decides
The authorising officer commits and issues the payment order; the assigned public accountant pays it; the head of the finance department approves every entry before it is sent
Room for improvement
The overall payment period stands at 34 days where the rule applying to local authorities sets 30; 7,544 payment orders out of 18,400 go back at least once to the spending department — 41 %; the statement of outstanding revenue comes out six weeks after year-end close; and €214,000 of revenue more than two years old has never been chased

Fictional public body, invented for the demonstration. At Roncelles, six people run the execution of four budgets: 24,600 documents a year, twelve call-off contracts, and a year-end close that ties up the whole department for six weeks. Time is not slightly short, it is very short — and the data at stake, contracts, payroll and third-party accounts, is among the most sensitive the municipality holds. The agent runs on local inference on a machine at the town hall, reads the financial software without ever writing to it, and lets no entry leave without the head of department's approval. The exchanges below span a year, from the overhaul of the return causes to the review presented to the finance councillor.

This public body, its figures and the exchanges that follow were invented for the demonstration. They illustrate a common situation; they describe no real service.

Public accounting assistant · hosted in France
I reread your last three financial years over the weekend — 73,800 documents, 24,600 a year — and I have drawn out a finding your software was carrying without anyone having the time to extract it.
The gap measured, and it decides everything else: 7,544 of your 18,400 payment orders go back at least once to the spending department before being sent — 41 %. And four causes alone carry 5,884 of them, that is 78 %. These are not forty-one different failures: they are four checks missing at the entrance that are then redone at the exit.
What that back-and-forth costs, on figures that are your own:
· The overall payment period stands at 34 days, where the rule applying to local authorities sets 30.
· 1,140 invoices paid beyond the deadline last year, and €9,400 of late-payment interest paid outpublic money that funded no service at all, neither for residents nor for staff.
· Checking a batch of payment and revenue orders accounts for 60 % of the time the batch takes: 4 hours out of the 6 h 40 a batch of 312 documents takes, from filing to dispatch.
What I propose, and it is not a promise — it is already done: I ran the four checks over the 78 batches of the past year, as a dry run. They would have stopped 5,884 documents at the entrance, where today they are stopped at the exitsame work, six days earlier on average, and without the second pass.
The time this shifts, once the checks sit at the entrance: checking a batch goes from 60 % to 18 % of the batch's time — from 4 hours to 1 h 12. Over 78 batches a year, that is 218 hours returning to the department.
The next step, and it takes one meeting: your head of department reads the four written checks, cause by cause, with the number of documents each would have stopped. She settles them, and they run on the next batchbringing them into service is the only act I leave to you, and it is the one that binds the municipality. causes-of-return_7544-orders-out-of-18400.pdf4 causes behind 41 % of returns, each measured over the year
⛓ Sourced · financial management software, 3 financial years, 73,800 documents, log of returns to spending departments
There are six of us for four budgets. I am not going to open four projects at once: where do we start?
With three checks, not four — and I have ranked them by the number of documents they spare you, not by difficulty.
M57 is the chart of accounts for local authorities: it states which account every item of expenditure and revenue must be posted to. Certified service is the confirmation that the work has actually been delivered before the expense is paid — the check that prevents paying for what has not been received. The assigned public accountant is the one who pays the expense the authorising officer has ordered, and who refuses it if a document is missing.
The four causes, counted over the year:
· Certified-service document missing or insufficient: 2,960 payment orders. It is the leading cause of rejection by the assigned public accountant, by a wide margin.
· Posting outside the M57 chart of accounts: 1,610 payment orders — of which 612 on a single pair of accounts, cleaning services posted as supplies.
· Duplicate invoice: 780 payment orders, 71 % of them on the twelve call-off contracts.
· Incorrect VAT split: 534 payment orders, almost all on the water and sewerage ancillary budgets.
These first three carry 5,350 of the 7,544 returns: three checks out of four, 71 % of the back-and-forth. The fourth can wait until the end of the first quarter without changing anything to your workload.
What I do on top, and that nobody has time for: on the posting cause, I do not merely flag the gap — I reread your 612 cleaning payment orders and wrote the posting rule that covers them all, in the form of your internal guidance, with the account used and the account retained side by side. You judge on a written, measured rule, not on 612 cases to settle one by one.
The next step I propose: let the three checks run on the next batch, alongside your usual check, for a fortnight. You compare the two outputs line by line: if mine add nothing, they stop at a word. On the trial batch I have already run, they stopped 25 documents out of 312, and 22 of the 25 would have come back from the spending department ten days later. control-plan_three-checks-for-71-percent.pdf4 causes measured, 3 checks to open, the posting rule written
⛓ Sourced · count of returns by cause over 3 financial years, written posting rule, trial batch of 312 documents
All of that assumes you read our contracts, our payroll and our third-party accounts. Where exactly do they go?
They go nowhere. I run on local inference on a machine at the town hall, and nothing I read leaves the municipality's network.
Local inference means the model computes on your own machine: the amount of a contract or a member of staff's pay line crosses no external network to be processed. If the authority would rather not host a machine, the other route is an isolated resource hosted in France, dedicated to Roncellesno pooling with another authority, which is the condition for the continuity of your finance department.
What that changes, point by point:
· Your financial data trains no model, neither ours nor a third party's.
· I work read-only on the software, and the technical account I read through has no right to writethis is not a promise, it is a permission that can be checked with one command, and your IT department will run it in front of you.
· Encryption in transit and at rest, role-based accessrights follow the job: the contracts officer opens commitments, not payroll statements. 6 roles for your 6 staff, and the log shows 0 out-of-role accesses since go-live.
· Hosting in France, under French law, architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity.
And the point that is worth more than all the rest for a finance department: I serve the separation between the authorising officer and the public accountant, I never bypass it. The one who decides the expense is not the one who pays it: two people, two checks, and that is what has protected public money for two centuries. Everything leading up to the payment order, I have already done — the entry built on your chart of accounts, the certified-service document matched, the control note stating what is compliant and what is missing. Issuing the payment order stays with the authorising officer, payment with the public accountantand it is precisely because these two acts stay with two different people that an exhaustive check weakens no one: it arrives before them, it takes neither one's place.
The figure that sums all this up: 0 financial data out of the municipality's network across the 24,600 documents of the year, and processing in the EU targeted.
What I propose: that I keep up to date the record your data protection officer and your public accountant will both ask for — hosting, data processed, retention periods, who accesses what, and the exact permissions of the technical account. It is asked for once a year and takes two days to rebuild; the first version is written and you have it attached. technical-framework_where-financial-data-lives.pdfLocal inference, read-only, processing in the EU targeted
✎ Framework · deployment architecture, technical account permissions, access log, first version of the processing record
Local inference · no data outside the EU

Your case is not here? That is exactly what a 15-minute conversation is for. Book the free audit

Use cases

The uses of AI for public accounting

Each use corresponds to an agent we deploy. All of them work in support, subject to approval by the public officer.

Included in your agent The 7 capabilities essential to this promise are included, at no extra cost.

Check the supporting documents for M57 entries and payment orders

Reading invoices, payment orders, revenue orders and contracts, extracting the data and preparing M57 entries — proposed, for approval.

M57 consistency checks

Verifying postings, evidence that the service was delivered, and completeness of documents before transmission to the assigned accountant.

Anomaly & discrepancy detection

Spotting duplicates, wrong postings and discrepancies for human verification, before the accounts are closed.

Reminders & payment schedules

Preparing revenue reminders and payment schedules, along with everyday replies to third parties, for approval.

Variances & dashboards

Budget positions, indicators and variances ready to comment on, supporting management control.

Accounting follow-up of contracts

Reconciling commitments, purchase orders and invoices on public contracts, to make payment orders more reliable.

Replies to third parties & departments

Answering suppliers' and managing departments' recurring questions: documents to provide, status of a payment order.

Controls and safeguards These 4 controls are built into the agent: they frame what it does, whatever plan you pick. They are not chosen and are not added to your order.
Human validation, exceptions and escalation Status, safe closure and audit trail Reconcile supporting documents, rules and accounting entries with an audit trail Handle discrepancies and validations before any accounting entry or payment
What the agent must be connected to This connection is required for the agent to work. It concerns your information system and is scoped during the audit.
Integrate with accounting and banking software and with approved platforms (French e-invoicing)
Other needs our agents cover Each card says where the matching agent stands: available, on quote, or still being architected.
The gain

How much time can a finance department win back?

By automating document reading and anomaly detection, a department can aim for a reduction by half in production time on standardised entries — reinvested in accounting quality and continuity of service.

Checking a batch of payment and revenue orders
Today · done by hand
Prepared by the agent, to approve
Anomaly detection on a file
Today · done by hand
Near-instant
Answering a recurring question from a third party
Today · done by hand
Automatic
Qualitative, non-contractual comparison: the proportions shown illustrate the shift of the work towards review, they represent no measurement. Every output of the agent is reviewed and approved by a competent person.
How it works

The stages of your AI agent project

1

Audit & scoping

15 minutes to target the use case with the best return.

2

Quote or direct sign-up

A catalogue offer is bought online; a specific need gets a costed quote.

3

Design

We design the agent and its guardrails.

4

Integration & testing

We connect your tools to the agent, which is itself hosted in France.

5

Rollout

Going live and training your team.

6

Operation

Continuous supervision and improvement.

Pricing

Three options, one agent

An agent for public accounting (M57 entry, anomalies, reminders), installed and operated for you. Choose according to how you work. Prices exclude VAT — annual subscription, the time it takes for the gains to settle in.

This agent is priced with you, not online. We are adjusting its scope at the moment, and online subscription stays closed while we do. Tell us what you need: we will come back to you with a price. Request a quote
Our commitment

Four guarantees that matter to a public finance department

Financial data never leaves the authorityLocal inference or an isolated resource hosted in France; no document entrusted to a foreign third party.
Data in France, under French lawDeployment objective: processing and access within the European Union for financial data; architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity.
The public accountant keeps the decisionThe agent produces verifiable entries, checks and summaries; no approval is automated.
Human oversight & traceabilityReading supporting documents: every change to the service is logged, in line with the requirements of the AI Act.
Frequently asked questions

Your questions, our answers

Does the agent follow the M57 chart of accounts?
Yes. It is configured on the framework applicable to your authority or institution. It checks postings, spots deviations from the chart of accounts and proposes corrected entries, which the public accountant verifies and approves.
Does it replace the public accountant?
No. It makes entry and checks more reliable and faster; the regularity check and the approval remain human. The AI agent assists, the public officer decides — no entry is approved automatically.
Does it connect to our financial software?
Yes, to the main financial management systems. The agent connects to your existing tools (financial software, e-invoicing platform, document management) and complements them, without imposing a migration.
Is public financial data protected?
Yes. Budgets, contracts, pay and third-party accounts are sensitive: our agents run locally within the authority or are hosted in France, with the deployment objective of processing and access operated within the European Union and an architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity.
How does the agent help detect anomalies?
It reads the documents, checks the consistency of M57 entries and flags duplicates, wrong postings, missing documents and discrepancies for verification. Every signal is documented and recorded in the file; the public accountant decides what follows.
How are the GDPR and the AI Act taken into account?
Yes. The architecture is sovereign by design: data localisation and minimisation, systematic human oversight, logging. The agent stays strictly in support, which meets the requirements of the AI Act for public sector use.
How long does it take to deploy the agent?
A few weeks as a rule, after a free audit that identifies the most useful use case, then a phase of design, integration with your software and testing before going live and training your officers.
Let's talk

Let us estimate the potential in your finance department

A few minutes to identify the most useful use case — hosted in France, supervised, under the control of the public officer.