A private AI layer on top of an existing system – for example an ERP, intranet or ticketing system. Employees describe what they want to achieve, and Elevate performs controlled, authorized actions. Permissions, approval points and logging govern what the AI layer may do.
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. Actions are checked against the user's permissions and the rules defined for the integration.
Elevate sits on top of what you already have – ERP, ticketing, finance systems. No migrations, no new master data.
Illustrative examples of workflows before and after. The figures are illustrative – not guaranteed results. Actual outcome is measured in the pilot against your own baseline.
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 in your controlled environment or on a dedicated GPU in our Swedish data center per the agreed architecture. Prompts, customer data and responses do not pass public AI services. Data flows and any external dependencies are documented before go-live.
Integrations are built on MCP (Model Context Protocol) – an open standard for connecting AI to systems. It can make integrations more reusable and reduce dependence on a single AI provider.
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.
Systems with documented APIs are often possible to integrate. We assess authentication, permission model, license terms, read and write support, data model and technical limitations before a pilot.
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. The goal is measurably shorter time for query and data-entry workflows – actual outcome is measured in the pilot.
Share of tasks requiring rework. When Elevate reads from the source system instead of someone transcribing, it reduces the risk of input errors.
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.
Elevate does not use public AI services for prompts, customer data or responses in the agreed standard architecture. The model runs in your controlled environment or on dedicated capacity in our Swedish data center. The exact data flows, operating location and any external dependencies are documented and approved before deployment.
Actions run through the system's defined integrations and are checked against the user's permissions and rules. A correctly configured integration limits what the AI layer can do; the scope is set and tested together with you.
Actions that create, change or send something require explicit approval before they are performed – you see exactly what will happen. Read queries do not change the source system but can still contain sensitive information or return incorrect answers. Reading is therefore also covered by access control, logging, testing and relevant data-protection measures.
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.
No. Elevate can connect to systems in your current environment – public cloud, on-premises or our data center. It does not require buying our migration or managed operations, but can be combined with them. Which parts run where is set by the agreed architecture.
When Smart Source AB processes personal data on the customer's behalf, the role as data processor is governed by a data processing agreement. Every question and action is logged auditably, which gives a clearer basis than employees pasting customer data into public AI tools. The final assessment depends on the actual processing and the division of responsibility.
Book a Workflow Review and we'll sketch a pilot setup for your specific systems – or start with a demo.