A private AI layer on top of your business systems. Your employees write what they want to achieve – the AI performs authorized actions in your systems. Your data never leaves your control.
Elevate connects a private language model to your existing systems via their APIs – with your permission model as a hard boundary. The difference from a chatbot: a chatbot answers questions, Elevate performs authorized actions in your systems.
Employees describe what they want to achieve in everyday language – no training in system interfaces, no macros, no exports to Excel.
Every action is performed via the systems' APIs with the user's own permissions. The AI can never do more than the user is allowed to do.
Elevate sits on top of what you already have – ERP, ticketing, finance systems. No migrations, no new master data.
Real workflows, measured before and after. The figures are typical results from pilot measurements.
Scenario 3 in practice – a finance assistant works with the business system without opening it.
Four principles make Elevate something other than "a chatbot against a public API".
The language model runs on a dedicated GPU in our Swedish data center. No prompts, no customer data and no responses pass through OpenAI, Anthropic, Google or any other public AI service. Your data never leaves Swedish jurisdiction.
Integrations are built on MCP (Model Context Protocol) – an open standard for connecting AI to systems. Each integration becomes a reusable component, not a proprietary one-off you're stuck with.
Reads happen immediately, but actions that create, change or send something are confirmed by Elevate first: "Shall I do this?". You define which actions require approval – and which may happen automatically.
Every question, every answer and every action performed is logged with user ID and timestamp. The audit trail shows exactly what Elevate did for whom – audit-ready documentation for GDPR and NIS2.
The basic rule: if the system has an API, Elevate can work with it.
The prerequisites are fewer than you think – we help you through every step.
We always start with a well-defined pilot – so that value can be measured before you scale. After the pilot, you move to a monthly cost for running the AI layer (GPU cost depends on inferencing load – determined during the pilot).
What's needed to get started – we help you through every step.
| API access | Your systems need to have APIs (REST/SOAP/GraphQL) or database access that Elevate can integrate with |
|---|---|
| Permission model | Existing user directory (AD/Entra ID or equivalent) to map actions to the right permissions |
| GPU capacity | A dedicated GPU in our Swedish data center is included in the service – alternatively on-prem at your site if you have your own GPU hardware |
| Pilot scope | We recommend starting with 1–2 systems and a limited user group, expanding thereafter |
One system, one workflow, measurable results. No "AI transformation" – a well-defined project with a clear end.
We select the workflow together with your pilot owner: what is done today, by whom, how often and how long does it take? The baseline is measured – that's what we compare against at the end.
An MCP connector is built against your system, the permission model is mapped to your user directory and the private model is set up on a GPU in our data center. Checkpoints are defined: what requires approval, what may happen automatically.
A limited user group works with Elevate in their daily flow. We fine-tune prompts, integrations and checkpoints based on real usage – every week.
The results are compared against the baseline: time saved, errors reduced, knowledge dependency lowered. You get a decision basis – scale up, expand to more workflows or conclude.
Minutes before vs. after, per workflow. Typical result: 90–95% faster for query and data-entry workflows.
Share of tasks requiring rework. When Elevate reads from the source system instead of someone transcribing, errors approach zero.
How many on the team can perform the task? With natural language as the interface, the dependency on "the person who knows the system" disappears.
No. The model runs on a dedicated GPU in our Swedish data center. Prompts, data and responses never leave the data center – no public AI service is involved at any stage.
No. Every action is performed via the systems' APIs with the logged-in user's own permissions. Elevate can never see or do more than the user – the permission model is a hard technical boundary, not a policy rule.
Actions that create, change or send something require explicit approval before they are performed – you see exactly what will happen. Read queries are risk-free, and everything is logged traceably per user.
The pilot group works live with Elevate from week 5. A full evaluation against the baseline is done after 8–12 weeks – with measured figures on time, errors and knowledge dependency, not estimates.
A dedicated GPU in our data center is included in the service and is sized during the pilot based on actual inferencing load. If you have your own GPU hardware, Elevate can also run on-prem at your site.
Yes. We act as data processor under a DPA, data processing takes place in Sweden, and every question and action is logged auditably. It's often easier to defend in an audit than employees pasting customer data into public AI tools.
Book a Workflow Review and we'll sketch a pilot setup for your specific systems – or start with a demo.