AI that runs on your operations.

We connect your data, your knowledge and your processes into one operational layer, then build the RAG systems, agents and analytics that work on top of it. Built to run for years, not to impress in a demo.

Book a discovery session See the architecture

The operational layerYour systems stay. We build on top.

04
Action
Back into your systems
Write-back to ERP and CRMHuman approvalNotifications and tasksAudit log
03
Intelligence
What we build
Custom RAGAI agentsForecastingDocument AIEvaluated · governed · model choice stays open
02
Foundation
A model of your business
CustomerContractOrderInvoiceAssetObjects · relationships · permissions · history
01
Sources
What you already run
ERPCRMSharePointMailboxesPDF and scansSpreadsheetsAPIs

01 / The foundation

Your data, modelled the way your business works.

Before AI is worth anything, it needs to know what a customer, a contract or an order is in your company, and how they connect. We build that shared model once. Every assistant, agent and dashboard then works on the same facts, with the same permissions.

A
Objects and relationships
Customers, contracts, assets and orders, linked as they are in reality.
B
Actions with rules
What may change, who may change it, and what needs approval.
C
Lineage
Every figure traces back to the system and the record it came from.

02 / Custom RAG

Answers grounded in your documents. Every source shown.

Retrieval-augmented generation connects a language model to what your organisation knows: contracts, manuals, tickets, wikis, reports. It finds the relevant passages first and answers from them only, citing where every statement comes from. When nothing relevant exists, it says so instead of guessing.

What makes ours custom

Your sources, kept in sync
We index the systems you already use and keep the index current as documents change.
Access rights carried through
People only get answers from documents they are allowed to open.
Every answer cites its sources
Each statement links back to the passage it came from, so it can be checked in one click.
Measured before launch
Answer quality is tested against a set of your real questions, before go-live and after every change.
Hosting that fits your data
In the EU, or on your own infrastructure where the data must not leave.

Illustrative exampleRAG

Question

What notice period applies to our supplier contracts?

Your knowledge base

[1]Supplier contract template.pdf0.91
[2]Purchasing policy 2025.docx0.86
[3]Framework agreement.pdf0.82
Board minutes Q2.pdfNo access
[0]Service SLA.pdf
[0]Onboarding wiki
[0]Ticket #4821

Answer

Standard supplier contracts end with three months’ notice to the end of a quarter1. Framework agreements are the exception: six months, in writing3. Any shorter period needs approval from legal2.

Answer with sources

03 / Agents and workflows

Agents that finish the work. People where it matters.

An agent reads what comes in, looks up what it needs in your foundation and your documents, applies your rules, and acts in your systems. Anything outside its rules stops and waits for a person.

Every step logged and replayable
Rules you set, not prompts you hope for
Approval gates on anything irreversible

Agent run · Supplier price changeIllustrative

09:14:02TriggerEmail received: “Updated price list Q4”
09:14:03ClassifySupplier price change · Sample Supplier Sàrl
09:14:05RetrieveContract C-2041 · clause 7.2, price capRAG
09:14:06CheckIncrease of 6.5% exceeds the agreed 5% cap
09:14:09DraftReply to supplier citing clause 7.2
09:14:09ApprovalWaiting for: Head of Purchasing
—WriteSend reply · keep price table unchanged in ERP

Approve and sendEdit draftReject

04 / Data analysis

Your data already knows the answer. It is just in five places.

Most companies do not lack data. They lack a foundation they can trust. Before AI is worth anything, the data has to agree with itself. This is how we get there.

01
Connect every source
ERP, CRM, spreadsheets, inboxes, PDFs and APIs. Pipelines pull from the systems you already run, on a schedule or in real time, without replacing any of them.
02
Clean and model
Duplicates merged, formats aligned, and one agreed definition of what a customer or a sale is. The result is a versioned data foundation where every figure traces back to its source.
03
Analyse and forecast
Dashboards built around the decisions you actually make, plus forecasting and anomaly detection on your own history. You see what is coming, not only what happened.
04
Act with AI
On that foundation, RAG assistants and AI agents can work reliably: answering from your knowledge, drafting, booking, flagging. Anything that matters still goes to a person.

01 / 04 · Connect every source

05 / Documents to data

A document arrives. Nobody retypes it.

Invoices, delivery notes, forms and contracts are read, their fields extracted and checked, and the result written straight into the system it belongs in.

Reads PDFs, scans and photographed paper.
Every extracted field carries a confidence score.
Uncertain fields go to a person before anything is booked.

Illustrative example

Extracted record

Supplier: Sample Supplier Sàrl; 99%
Invoice no.: 2026-0412; 99%
Date: 14/09/2026; 98%
Net: €1,240.00; 99%
VAT 17%: €210.80; 99%
Total: €1,450.80; 99%
IBAN: LU28 0019 4006 4475 0000

Confidence

Ready to book

06 / Evaluation and governance

Measured before it ships. Governed after.

Evaluation set · your real questionsIllustrative

Question	Expected source	Result
Notice period for supplier contracts?	Contract template §7	Pass · citedRunning …
Who approves orders above the limit?	Purchasing policy §3	Pass · citedRunning …
What did the board decide in Q2?	Board minutes · restricted	Pass · withheldRunning …
Holiday entitlement for part-time staff?	Not indexed	Pass · said “not found”Running …
Response time for priority 1 incidents?	Service SLA §2	Review · wrong sectionRunning …

01
Access inherited
Roles and rights come from your existing systems.
02
Everything logged
Every answer, decision and action can be traced.
03
GDPR and EU AI Act by design
Considered from the first day, not bolted on before launch.
04
Model choice stays open
Change the model without rebuilding the system around it.

07 / AI workshops & concepts

Before we build anything, we map where AI actually pays.

We run AI discovery workshops with operations teams and turn them into an executive concept: the bottlenecks, what each one costs a year, the architecture that removes them, and a phased plan to get there. It is a document you can take to a board, not a pitch deck.

Funding

Through the SME Package Digital and SME Package AI, the state reimburses 70% of eligible costs on a project of €3,000 to €25,000 ex. VAT, so at most €17,500 per package. Eligibility is decided at the pre-analysis, not by the price.

01
Discovery, on site
A working session with the people who actually run the processes, not a management interview. We walk each workflow with the person who lives in it and record where it stalls.
02
Every flow, end to end
For each major process we reconstruct the full path of information, from the trigger to the resolution, noting every manual handoff, every tool involved and every point where work waits.
03
Costed, not asserted
Each bottleneck is converted into an estimated annual cost from your own volumes and a conservative loaded hourly rate, then sanity-checked against comparable firms. You get arithmetic you can argue with, not adjectives.
04
An architecture that integrates
The systems you already run stay. The AI layer sits on top of them, so the existing investment is protected and adoption can be gradual instead of a migration.
05
A phased roadmap
Twelve months, four phases, each one shipping something usable in production. No AI project worth doing runs as a single launch, and the first tangible gains land inside the first two months.

A recent concept

For a Luxembourg property-management group we ran a discovery session with its operations and process leads, mapped seven business processes end to end, and delivered a thirty-page executive concept.