We build Agents. You build the Future.
● B2B offer — Support & expertiseLevel 2 support: the context gathered, the lines opened
A level 2 incident is rarely unresolved for want of expertise: it is resolved slowly because reconstructing the context takes time. Version, configuration, history of exchanges, similar incidents already handled — your agent gathers all of it and proposes lines of diagnosis, each tied to what justifies it. Data hosted in France; the AI model is called at its publisher, except with the Sovereign model option. With the 100% Sovereign package, your customers' configurations stay with you. The engineer runs the diagnosis; the agent saves them starting from scratch.
Updated on
Fictional simulation — not a client result
Three similar incidents found in your base, with how they were resolved and the factor they had in common.
Two lines of diagnosis, each tied to the element behind it.
🔗 Sourced · lines tied to past incidents
I flag it rather than presenting the line as established — that is the kind of gap that costs half a day if it goes unnoticed.
✎ Support · the gap is flagged, not hidden
For an organisation established outside the European Union, the law of its own country applies; at its request, we put equivalent safeguards in place (human review before any action, a log of the criteria, information for the people concerned).
A Blue Lemon Agent level 2 support agent gathers the full context of an incident — version, configuration, steps already tried, customer history — finds the similar incidents already resolved in your base and proposes lines of diagnosis tied to what justifies them. Where a line rests on an imperfectly comparable case, it says so. Data hosted in France; the AI model is called at its publisher, except with the Sovereign model option. With the 100% Sovereign package, your customers' configurations stay with you, architecture designed to reduce exposure to extraterritorial legislation, location alone not being enough to guarantee immunity.
These figures describe our offer, not results measured at a client. How large the gain is on your volume of level 2 incidents is confirmed by a pilot.
Indicative estimate, to be confirmed by a pilot on your real files (general terms of sale, article 4).
One package, one agent
A level 2 diagnosis agent (context, comparable cases, documented lines), installed and operated for you.
Setup + controlled subscription
- Installation, configuration and training for your teams
- Operation, human oversight, updates and support
- Hosting in France, a dedicated and isolated resource
All inclusive, no setup fee
- Setup included (installation, configuration, training)
- Operation, human oversight, updates and support
- Hosting in France, managed end to end
On site, you own it
- Hardware installed on your premises (you own it)
- French / European AI models run locally
- Secure remote maintenance (Pro support included)
What does the agent actually do?
One agent, several stages of the diagnosis, with a link to a neighbouring agent. The agent carries out by itself what you authorise it to do; you decide what it does on its own and what it submits to you.
Reconstructing the context
Gathers version, configuration, steps already tried and the customer's history.
Comparable incidents
Finds in your base the similar cases already resolved and their common factor.
Flagging variances
States how a past case differs from the present one, rather than concluding too quickly.
Need to go further?
These agents handle a different business process, with their own owner and their own price. They are added to this one.
In 30 minutes we identify the most relevant agent — without oversizing the project.
See the agent at work
5 everyday 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.
Fictional simulation — not a client result
· Forty-one cases were closed as "external cause". I had ten reread: nine were, one was not.
· Twenty-two per cent of cases are closed "not reproducible". Seven of them were the same defect.
· Eleven customers were told "it is fixed" for a version they have not installed.
· Three requests concerned data I have no access to. I asked for it, I did not take it. morning-watch_4-flags.pdf41 "external cause" · 7 not reproducible, one defect
⛓ Source · 1,840 second-line cases, closure log
What I record: 41 cases closed as "external cause" — the customer's network, third-party hardware, a misconfiguration at their end. None was reread, and none has any reason to be: once the cause is declared external, the case no longer counts among product defects.
Why that is structurally dangerous: it is the conclusion that suits everybody. It closes the case, it leaves the indicator, and it is often true — which makes it credible even when it is not.
What the rereading showed: of 10 drawn at random, 9 were indeed external. One was not — a behaviour of our product under an unusual but legitimate network configuration. One in ten is four cases out of the 41.
What I now do: a sample of the "external cause" closures is reread every month, and the rate appears in the summary. A case closed as external is not shut: it is set aside with its date.
What I prepare for every external cause, and what gets signed: the whole file — the configuration observed, what was reproduced at our end and what was not, the third-party element involved, and the message to the customer already drafted. The announcement itself is signed by a human: "the problem is at your end" is support's costliest sentence when it is wrong — and it is wrong one time in ten, four cases out of the 41. Validation takes two minutes per case, and that is the price of the one sentence a customer does not forgive. 41-cases_1-in-10-requalified.pdfThe conclusion that suits everybody
⛓ Source · 41 cases, 10 reread, 1 requalified
What each case carries: what was reproduced and under what conditions, what was ruled out and on what basis, the exact version at the customer's, and what I could not verify.
Routing follows who can act: a reproduced defect goes to development, with the minimal case; an unreproduced case is kept and matched against the others, never deleted; a probable external cause goes to a human before anything is said to the customer; an existing undeployed fix goes to the customer, with the version carrying it.
With a monthly summary: cases by cause, requalification rate of external causes, unreproducible cases matched, available fixes not deployed.
Three operating rules, and what they give you. Access to customer data is asked for, never taken: I name the exact window I need, I say why, and I stick to what is sent — which is what makes the case file showable to the customer themselves. A failure to reproduce concludes nothing: the case is kept with all its conditions and matched against the others, and that is how seven isolated cases became one reproduced defect, fixed in three days. The fix date is signed by development, the only ones it commits: I hand them the minimal case, the version, the volume and the sequence — everything, in other words, that separates an estimate from a date that holds.
✎ Framework · no data access · no date promised
Fictional simulation — not a client result
What I record: 22% of cases are closed "not reproducible". In most tools that closure removes the case from defect statistics — it proved nothing.
What it actually discards: precisely the defects that depend on a rare combination — a version, a data volume, a sequence of actions, a time of day. They are the hardest to find, and they are the only ones this closure eliminates.
What I do: I keep every unreproduced case with all its conditions, and I match them against each other. One case alone proves nothing; seven cases sharing three conditions out of five is a defect.
What that gave: 7 cases closed as not reproducible over five months, at five different customers, shared the same version and the same sequence. Reproduced on the seventh match, fixed in three days.
What I write to the customer, and it is exactly what happened: "I could not reproduce it", with the conditions I tried and the ones I am missing — the case stays open and matched against the others. "There is no defect" is a conclusion, and it is not drawn from a failure: both sentences are delivered the same way in most cases, and it is the second one that loses customers. 7-cases_5-customers.pdfOne case proves nothing, seven sharing three conditions are a defect
⛓ Source · 22% closed not reproducible, 7 cases matched, 5 customers
What I record: 11 cases closed with "fixed in version 4.2". Of the 11, 9 customers were on 4.0 or 4.1, and nothing in the message said so.
What they understood: that their problem was solved. Which was true at our end and false at theirs. Six reopened the case within three weeks — and reopening cost them a second full account of it.
What I do: I never close a case on a fix's existence. I close on its installation, or I leave the case open marked "fixed in 4.2, you are on 4.0" and what the upgrade involves.
What I add, because it is the real question: what the update requires at their end — downtime, dependencies, configuration rework. An unreachable fix is not a fix.
What I flag to the product team: fixes available for more than three months and the number of customers who do not have them. This quarter, a security fix was in that position at 34 customers. 11-cases_34-customers-without-the-fix.pdfAn unreachable fix is not a fix
⛓ Source · 11 cases, 9 customers on an earlier version, 6 reopenings
What I gather in 40 seconds: the version installed at the customer and its update date, the effective configuration — the 23 settings that differ from the default, not the other 180 —, the steps already tried by the customer and by level 1, and the customer's history: their 14 earlier tickets, 3 of them on the same module.
What rebuilding that context used to cost: 34 minutes per ticket for your engineers, spent re-reading message threads and asking the customer again for what they had already written. Across 2,100 level 2 tickets a year, more than 1,100 hours — more than thirty-one 35-hour weeks, rounded down. The context now arrives with the ticket, on one page.
Comparable incidents, and how I find them: I match the new ticket against your 9,400 closed incidents across five versions, on five conditions — version, module, sequence of actions, data volume, time of day. For this one, 6 comparable incidents come up, all on version 4.1 with the same sequence, and four were resolved by the same action: clearing the index cache after import. The common factor is named, not guessed: it rests on the five shared conditions, and I give you the four ticket numbers.
The figure I publish about myself: of my first 200 matches, 31 listed comparable incidents that were not comparable — 15.5 %. All shared the module and not the version, and the module alone proves nothing: it was the gap between 4.0 and 4.1 that carried the defect. I made the version mandatory among the shared conditions; over the next 600 matches, the rate fell to 4 %, and your engineers stopped opening a list only to close it.
⛓ Sourced · 34 min of context rebuilding per ticket, 9,400 incidents compared, 6 matched
Fictional simulation — not a client result
What I am often offered: permanent access to the customer's systems, "to save time on diagnostics". It would save time, that is true.
Why I refuse: an access opened for diagnosis stays open between diagnoses. It never closes by itself, nobody revokes it, and it ends up used for something else — out of convenience, never out of malice.
What I do: I ask for exactly what I need, saying what it is for: "the module X log between 2 and 3 pm on the 12th", not "your logs". The customer sends, and they know what they are sending.
What I do with what arrives anyway: sendings often contain more than was asked. I use only the part requested, and I say so: "received 400 MB of logs, used the 2–3 pm window". The rest is deleted when the case closes.
What I keep from a case when it closes, and it is what actually serves the next one: the symptom, the version, the configuration and the cause found — without a single line of customer data. Across 1,100 cases, that base of causes turned up a comparable case in 62 % of diagnoses, while the log extracts are deleted at closure. An extract kept "because it might serve" never serves and ends up in a customer database no contract provides for — a cause, by contrast, is reusable indefinitely. what-is-asked_and-what-is-deleted.pdfAn access opened for diagnosis stays open between diagnoses
✎ Framework · no permanent access · only the requested part is used
What I record: across the sendings received, 17 contained personal data about their own customers — a database export to illustrate a display problem, logs containing full addresses.
Why the customer does not see it: they send what reproduces their problem, not what it contains. An export that reproduces a display bug contains the displayed data, and there is no reason to think about it while trying to get help.
What I do: I flag it immediately to my contact at the customer, I ask whether an anonymised extract would do, and I delete the sending if so. Of the 17, 12 were replaced by a reduced extract.
What I do meanwhile, and what I leave to be decided: the case moves on without the sending — I resume the diagnosis on the non-identifying part, and I write out for the customer the reduced extract that would do: the fields to keep, the ones to strip, the expected volume. What identifies their own customers is theirs to decide and to sign — anonymising means having read it, and that is not settled on their behalf. It is that precise request which made the twelve replacements possible in under twenty-four hours.
What I keep of the remaining 5: nothing beyond the case. They are deleted at closure, and the deletion is logged. 17-sendings_12-replaced.pdfThey send what reproduces their problem, not what it contains
⛓ Source · 17 sendings containing personal data, 12 replaced
Fictional simulation — not a client result
What was reproduced: the scheduled export stops at 40% when the product runs alongside a third-party signing module that your support contract does not cover. The minimal case is written, it replays every time, and it never happens without that module.
The rule to apply here has not yet been defined: your tool offers “product defect” or “external cause”, and this case is both at once — the defect is ours, the condition that triggers it is at the customer's. So I have closed nothing on my own — and the workaround went to the customer at 8:50.
Three possible outcomes. Close as external cause: in line with your written scope, and the case leaves the indicator although the module is installed at one customer in three. Record a product defect with the condition named and the list of customers concerned: development decides with full knowledge, at the cost of a defect open on a scope you never committed to cover. Create a third outcome, “observed out of scope”: the figure finally exists without polluting the other two, but the state has to be added to your tool and someone has to review it.
I recommend the second. It is the only one that keeps the case countable and reviewable: your own rule says an external cause is the conclusion nobody ever checks, and it is that rule which had one case in ten requalified at review.
You can also give me another rule of your own — for instance: product defect when the third-party module is present at more than one customer in ten, external cause below that, and the threshold is reviewed each quarter.
Shall I remember it for the next cases? Of your 1,840 level 2 cases, 63 involve an uncovered third-party module, and 11 were closed as external cause last quarter. If you approve the rule, I will handle the next ones that way, and I will give you each month the list of cases concerned and their outcome.
⛓ Source · minimal case reproduced 11/08, support contract, 63 cases involving a third-party module out of 1,840
The rule, in one sentence: a reproduced defect that only triggers in the presence of a third-party module is recorded as a product defect, with the module's name and version in the reproduction condition, and the list of customers who have installed it.
Why your correction matters more than it looks: a minimal case stripped of its condition is a case that does not reproduce. It would end up closed “not reproducible” — the outcome that, here, carries 22% of cases and sets aside exactly the defects that depend on a rare combination.
What it changes from this morning: the defect is recorded under reference D-4412 with the condition “signing module, versions 2.3 and 2.4”, and the 31 customers who have installed it are named in the record. The original customer received the workaround, the reference and the recording date.
What lets you undo it: the rule is written into your closing rules under its own name, with its date, the person who approved it and the case that gave rise to it. You remove one line, and these cases go back to external cause.
✎ Rule approved · closing rules, entry “defect conditioned by a third-party module”, approved on 11/08
What I looked at: the 63 cases involving a third-party module, the module named in each, and the diagnosis time they took.
What comes out of it: the 63 name 9 different modules, and 3 of them cover 41 of the 63 cases. Those 41 took 2 h 40 of diagnosis each — 109 h 20 over the quarter — and in 38 cases out of 41, the time went into finding out which module was installed.
Where the opportunity sits, and it is not in the diagnosis: your three most frequent modules are known, the versions that agree with the product are known, and none of it is published. The customer finds out by opening a case.
What I propose, and it is one page to publish on your side: the list of the three most frequent modules with the product version that agrees with each, kept up to date at every release. I fill it in from the cases, you review it, it is published under your name.
What it is worth: across those 41 cases, 29 carried a module version older than the one that agrees — the page would have avoided them before the call. And the other 12 would have arrived with the module's name already in the request, roughly an hour less diagnosis each.
The decision is yours: publishing a compatibility list commits the company on what it supports, and I do not touch it before you have written it.
✎ Proposal · 63 cases, 9 modules, 3 modules for 41 cases, 109 h 20 of diagnosis over the quarter — publishing a compatibility list belongs to the company
Fictional simulation — not a client result
What I announce without hesitation: that the defect is reproduced, under what conditions, that it is recorded under such a reference, on what date, and the workaround where one exists. That is what a customer most often lacks, long before a date.
What I open if you wire it up: the fix date as development recorded it, with its last update. The date comes from them, I carry it — I manufacture none.
The only date I announce is a decided date, and I say who decided it: "development recorded 14 March, updated on 2 February". A date announced by support commits development, which did not set it, and the customer holds it as a commitment. What I supply when development has recorded nothing yet: the workaround, the defect reference, its recording date, and where the case sits in development's queue — across 240 cases those four elements were enough, in 78 % of them, for the customer to stop pressing on the date.
The sentence I stopped letting through: "it is fixed in version 4.2". 11 files were closed that way while the fix was not deployed at the customer's site. "The fix exists" and "the fix is with you" are two unrelated sentences — I now say the second, or nothing. what-is-announced_what-is-not.pdfWhat opens · the date coming from development · the abandoned sentence
⛓ Source · 11 files closed on a fix not deployed at the customer
What is kept: the reproduction conditions and the minimal case, what was ruled out and how, the closed files with their conclusion and its re-reading, the fixes and their real deployment date at each customer, and the submissions received with their purge date.
Why customer data does not stay: I ask for a dataset to reproduce, and I purge it once the minimal case is established. Of the submissions received, 17 contained personal data belonging to my customer's own customers — sent unintentionally, in a full export. Permanent access to the customer's systems, often offered "to save time", would turn that accident into a permanent state.
What systematic re-reading showed: 41 files closed as "external cause". Ten re-read: several were not. It is the one conclusion nobody ever checks — it takes the file out of scope, therefore out of control. It is now re-read as a matter of principle.
And on "not reproducible": 22 % of files. Seven of them described the same defect — it is the fate reserved for the hardest defects, not a neutral category. what-you-keep_level2.pdf5 items kept · customer data purged once the minimal case is set
⛓ Source · 41 external-cause closures re-read, 17 submissions with personal data purged
Your case is not here? That is exactly what a 30-minute conversation is for. Discuss your project →
The stages of your AI agent project
Audit & scoping
30 minutes to target the use case with the best return.
Quote or direct sign-up
A catalogue offer is bought online; a specific need gets a costed quote.
Design
We design the agent and its guardrails.
Integration & testing
We connect your tools to the agent, which is itself hosted in France.
Rollout
Going live and training your team.
Operation
Continuous supervision and improvement.
What does an AI agent bring to your level 2 support?
Your engineers' expertise is not the limiting factor: reconstructing the context is. Gathering it automatically gives them back time to diagnose.
! The issue
A level 2 incident calls on scarce expertise, and most of the delay goes into reconstructing the context: which version, which configuration, what has already been tried, have we seen this before? The agent gathers those elements, finds the comparable incidents in your base and opens documented lines — the engineer starts where they used to start after an hour of searching.
✓ Our answer
The lines proposed are tied to what justifies them, and the gaps with past cases are flagged rather than passed over in silence — that is precisely the kind of detail that costs half a day. Data hosted in France on an isolated resource; the AI model is called at its publisher, except with the Sovereign model option. With the 100% Sovereign package, your customers' configurations, often revealing of their infrastructure, do not leave your premises.
Your customers' configurations and their incident history: sovereignty & compliance
Your customers' configurations and their incident history describe their infrastructure. Here is how the architecture of our agents protects them.
Local inference
With the 100% Sovereign package, the agent runs on a machine belonging to your organisation: no customer configuration and no incident history leaves the network.
Hosting in France
Otherwise, with the Agility and Serenity packages, a dedicated, isolated resource hosted in France, under French law: data hosted in France; the AI model is called at its publisher, except with the Sovereign model option.
Reduced extraterritorial exposure
For your customers' configurations and their incident history, the architecture aims to reduce exposure to the Cloud Act and FISA 702; being located in France or in the European Union does not, on its own, guarantee immunity.
Isolated resource
No pooling: an environment strictly dedicated to your company and its incident base.
Lines tied to their justification
Every line refers back to the incidents or the elements behind it, gaps included; encryption, role-based access and logging of every diagnosis prepared.
AI Act: governed deployment
The agent carries out what you have authorised; no line is presented as established and no intervention is applied; 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.
- 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.
How much diagnosis time can a team recover?
By gathering the context and the comparable cases, the searching that precedes the diagnosis proper disappears. How large the gain is depends on your volume and remains to be confirmed by a pilot.
Four guarantees that matter to your clients
Related resources
Your questions, our answers
Are the lines proposed reliable?
Does the agent act on the customer's systems?
How does the agent find similar incidents?
Is our clients' data protected?
Which tools can our customers use to talk to the agent?
Does it connect to our support tool?
How long does it take to deploy this agent?
Other agents for support
Let's size up the potential on your complex incidents
30 minutes to assess your history and your integrations — hosted in France, supervised, with no commitment.