It answers confidently and gets it wrong
The assistant speaks about your policies, your prices or your lead times with total conviction. Nobody can verify where that answer came from, so nobody uses it to decide anything.
Solution · On-premise AI
Generic AI knows everything except your company.
It knows everything but how you work. We build RAG over your internal knowledge and deploy it wherever you decide: your datacenter, your private cloud or a hybrid setup.
The symptom
This is how it breaks today.
The knowledge lives where nobody looks
Contracts on a drive, procedures on the intranet, the actual criteria buried in ticket history and in a handful of people's heads. Every internal question gets resolved by asking someone.
The project stalls in legal review
The use case makes sense, the team is ready, and the conversation ends when someone asks where those documents travel to. Without a clear answer, the pilot never starts.
What changes
The same assistant, with your information and under your control.
It answers with the source in plain sight
Every answer arrives with the document and passage backing it. It can be verified on the spot — which is what makes people actually use it.
The data never crosses your perimeter
The corpus, the indexes and the model run inside the infrastructure you already control. There is no third party you need to walk through your data policy.
It's an asset you own, not a subscription
The knowledge index, the configuration and the criteria stay in your house. If the model provider changes tomorrow, one piece changes — not the system.
Auditable end to end
Who asked, what was retrieved and what was answered is all logged. Compliance stops being the blocker and becomes one more design requirement.
Deployment models
The choice isn't technical, it's about risk.
We don't sell a single architecture. We pick the one that matches the data you're about to expose, together with your team.
On-premise
In your datacenter
- Where the data lives
- Everything inside your network. Traffic never reaches the internet.
- Who operates the infrastructure
- Your infrastructure team, with our support and full knowledge transfer.
- When it fits
- Regulated data, information that cannot leave the country, or internal policy that forbids external processing.
Private cloud
In your own cloud account
- Where the data lives
- In your tenant and in the region you define, isolated from the rest of the organization.
- Who operates the infrastructure
- Your cloud team, on top of the infrastructure-as-code we hand over.
- When it fits
- You already operate in the cloud and want control and traceability without standing up your own hardware.
Hybrid
By data sensitivity
- Where the data lives
- Sensitive material is processed inside the perimeter; the rest can lean on external services.
- Who operates the infrastructure
- Shared, with explicit routing rules and masking before anything leaves.
- When it fits
- Critical documents coexist with general content and you want to optimize cost without exposing what matters.
In all three models the corpus and the query logs stay under your control.
How it works inside
The journey of a single question.
For the technical team that will validate this: no piece is a black box.
- 01
Ingestion
We connect your sources — documents, intranet, tickets, databases — and keep them in sync as they change.
- 02
Indexing
Content is chunked, normalized and indexed so it can be retrieved by meaning and not only by exact wording.
- 03
Retrieval with permissions
For each question we pull the relevant passages, filtered by what that person was already allowed to read in the source system.
- 04
Generation with citations
The model writes the answer using only that retrieved material, and returns the references backing it.
- 05
Observability
Query, context and answer are traced so you can measure quality, spot knowledge gaps and answer audits.
How we implement it
From assessment to operations, without betting the business on step one.
Each phase ends in something concrete you can evaluate before moving to the next.
- 01
Assessment
We map sources, regulatory constraints and existing infrastructure. This is where the deployment model and the stack get decided — not before.
- Inventory of sources and of who can see what
- Legal and internal policy constraints
- Initial use case with an agreed success criterion
- Proposed architecture and deployment model
- 02
Pilot on a real case
A bounded corpus and a case that matters to someone specific. Measured with real questions from your operation, not with demos.
- Bounded corpus, loaded and indexed
- Evaluation question set built with your team
- Retrieval and answer quality measurement
- A go / no-go decision backed by evidence
- 03
Production rollout
The system ships into the defined perimeter, with permissions, masking and observability in place from day one.
- Deployment in your infrastructure
- Permissions inherited from the source system
- Personal data masking
- Usage, quality and cost dashboards
- 04
Operations and expansion
The system improves with use, and the same index unlocks the next use cases without starting over.
- Periodic review of quality and knowledge gaps
- New sources on the same platform
- Knowledge transfer to your team
- Support with a service level agreement
What we define with you
The stack gets decided during requirements gathering, not in a sales deck.
Anyone handing you the definitive architecture before seeing your operation is selling, not designing. These are the variables we work out together.
- Nature of the sources
- Format, volume, rate of change and how structured what you have today actually is.
- Regulatory constraints
- Which framework applies to you, which data cannot leave, and what evidence your audit requires.
- Existing infrastructure
- What already runs in your house and can be reused, instead of adding new pieces someone will have to maintain.
- Criticality of the case
- Whether the answer informs or decides. That defines how much human verification belongs in the middle.
- Who operates it afterwards
- Whether your team takes the system over or we operate it. It changes the design from the start.
Common objections
What we always get asked.
Do we have to buy servers?
Not necessarily. On-premise is one of the models; it also deploys into your own cloud account with no new hardware. What fits comes out of the assessment, looking at your real infrastructure.
We already use Microsoft or Google — isn't this included?
Those suites ship general assistants over what lives inside the same suite. What they don't cover is your knowledge scattered outside them, or data control in a deployment you own. We coexist with what you already have.
Who maintains it after go-live?
You decide during the assessment: we hand the system over to your team with documentation and training, or we operate it under a service level agreement.
What if the model still gets it wrong?
That's why every answer cites its source and why critical cases go through human approval. The system doesn't replace your people's judgement — it brings them the right information to exercise it.
Where do we start?
With a bounded case that hurts someone specific. A pilot with a clear success criterion tells you more about viability than any presentation.
Let's start with your case, not with the technology.
A conversation to review your sources, your constraints and which deployment model makes sense in your context.
06 — Contact
We start with a 45-minute technical session.
No endless form. Tell us briefly about the challenge and we will book a call. If it is not a fit, we say so.