Your data is the reason your AI stalled. We start there.
Pexon embeds senior forward deployed engineers inside industrial companies with €50 million to €2 billion in revenue. We start at the data layer, connecting SAP, MES, PLM and decades of documents, then build AI systems that respect your permission model, run inside your own cloud tenant, and are contracted under German law.
Everyone has run the pilot. Almost nobody has shipped.
Between 2024 and 2026 most industrial companies proved that AI works. The demo answered the question. Then it stopped — at the data.
The data cannot be reached
SAP, MES, PLM, thirty years of SharePoint, SQL nobody has touched in a decade. No prompt reaches into that — it takes engineers who read your schema.
Entitlements cannot be expressed
A system that shows everyone everything cannot go to production. Your real permission model has to travel with the context, end to end.
The person who could is busy
The one engineer who would push it through is committed elsewhere, and is not hireable in months on this market.
The companies with the best models in the world concluded, all at once, that the models are not the constraint.
In the last twelve months every major AI organisation moved capital into embedded engineering. This is the strongest available evidence that the delivery model works.
Sources, each re-verified before launch: OpenAI, 11 May 2026 (deployment company; Tomoro acquisition, ~150 FDEs; >$4 bn initial investment) · Microsoft, Frontier Company · AWS Partner Network, Forward Deployed Engineering · Forbes, 28 May 2026 (Palantir Q1 2026) · Indeed data via Business Insider (FDE postings, April 2025 → April 2026).
Data-to-AI, built inside your business.
We work the way the frontier labs now work — engineers embedded in your environment rather than a project team reporting from outside. The difference is where we start: not with a workflow, but with the systems your workflow depends on.
We start at the data layer
SAP, MES, PLM, SharePoint, ticket systems, mainframes. We extract, clean, mask and structure it — then carry your real permission model through end to end, so a system can go to production instead of staying a demo.
We are neutral on models and clouds
Frontier models where they earn their cost, open-weight models where the data class demands it, fully on-premise where the law demands it. We have no licence to defend.
Your knowledge stays yours
Everything runs inside your tenant. Your data never leaves it, never trains an outside model, and never becomes anyone else's advantage. The goal is your team operating it without us.
No pilot phase. One thread through to production first.
The failure pattern in enterprise AI is a wide pilot that touches everything and ships nothing. We invert it: one real data source, one real use case, all the way through to something a person uses.
Get in
Access, repositories, data reality, permission model. Three to five measurable sprint goals, agreed in writing.
First thread
One source end to end: extract, cleanse, mask PII, index, carry access rights through. A usable answer, not a demo.
Production
Evals, monitoring, cost control, runbooks. Handover to your team begins here, not at the end.
Widen or hand over
More sources, business units and use cases — or transition into Ops. Both are acceptable outcomes.
Compound
Every engagement leaves reusable connectors, masking pipelines and eval harnesses behind. The next one is faster.
That is the point of doing this repeatedly in one industry. The connectors, masking pipelines and eval harnesses we bring with us were paid for by earlier work, and you inherit them on day one rather than funding them again.
What you end up with: a company that can query what it already knows.
Every enterprise runs four infrastructure layers. A fifth is being built now — and unlike the other four it cannot be bought off a shelf, because it is made of your own knowledge.
Y Combinator named “Company Brain” one of fifteen categories in its Summer 2026 Requests for Startups. The through-line: the missing piece is company context, not model capability — and what gets sold is the finished work, not the tool.
Where this pays for itself.
Eight patterns, each starting from a system you already own. Each begins as a two-week blueprint — none begins as a twelve-month programme.
| Source system | What we build | What changes |
|---|---|---|
| SAP (ERP) | Governed semantic layer over master and transactional data, with metric definitions fixed at source | Questions about orders, margins and stock get one answer, traceable to the row it came from |
| MES / production data | Historised machine and batch context joined to quality results | Root-cause analysis in hours instead of weeks; recurring faults become visible |
| PLM / CAD / drawings | Searchable technical corpus with part and revision awareness | Engineers find the existing solution instead of redesigning it |
| Service desk | Retrieval pipeline over internal documentation with a confidence threshold and human review | A meaningful share of L1 and L2 volume resolves without a human touching it |
| Contracts & supplier documents | Clause-level extraction with obligation and deadline tracking | Renewal and liability surprises stop arriving by email |
| Quality records & certificates | Automated inspection of incoming material documentation against specification | Manual certificate checking stops being a person's full-time job |
| Legacy code & mainframe | Business rules extracted and documented, tests generated around them | Modernisation becomes possible because someone finally knows what the code does |
| Sensor & telemetry archives | Feature pipelines and monitored models against real failure history | Maintenance moves from calendar-driven to condition-driven |
Four things that decide it.
You are the client, not an exception
The large deployment teams are built for global enterprises. We are built for companies between €50 million and €2 billion in revenue — the segment with the same problem and none of the attention.
We have no stack to defend
We are not selling licences underneath the engineering. Model and cloud decisions get made on your requirements and your data classification — including the answer that nothing leaves your building.
The accountability is German
One named senior architect, German-speaking, personally accountable for delivery. Contracts, SLAs and the DPA under German law, through a German entity.
You can start for €4,900
A two-week blueprint, not a programme approval. If it does not convince you, you have lost two weeks and a rounding error.
Almost every company-brain product optimises for retrieval over unstructured text. When an agent and the CFO calculate “revenue” differently, wrong answers arrive silently in board material. For regulated and industrial clients, traceability beats answer coverage: metrics come from defined sources with defined definitions, not free-text similarity search.
Procurement and legal — the questions that decide it.
Do the engineers work on site with us?
The forward deployed lead works on site or hybrid by agreement. For regulated programmes we offer a dedicated engineer four days a week. The delivery pod works in your time zone and inside your tools.
Who owns the code, pipelines and prompts?
You do. Code, data pipelines, prompts and documentation are yours, and usage rights to sprint output transfer on payment of the first monthly invoice. The stated goal is your team operating the system without us.
Where does our data sit and who has access?
Everything runs inside your Azure, AWS or GCP tenant, using your identities, in your regions, on your cost centre. Your data never leaves that tenant and never trains an outside model.
Not a sales call. An architecture call.
Thirty minutes with the architect who would actually run the engagement. You leave knowing whether this fits and what the first source should be. If your pilot is still a pilot, the problem is probably reachable.