Production AI, deployed on your infrastructure and connected to the systems you already run

Built for enterprise teams that can’t send data to a public cloud. We deliver in stages (PoC in 5–10 days, MVP in 6–8 weeks), wire the model into your existing systems, and run the whole thing on servers you control.

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Credit Agricole – AI Service Assistant, in production

95%
classification accuracy
62%
of incoming tickets automated
50%
faster processing

Custom AI, built to your process and deployed inside your perimeter

Hire us when the work is too manual for people and too sensitive for off-the-shelf AI

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Take a working pilot to production

You’ve got a PoC that looked great in a demo and has sat there for months. We do the integration, the security review, and the production hardening that puts it in front of real users under real load.
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Connect a model to your existing systems

We wire the AI into the CRM, ITSM, and ERP you already run. Tickets route into your Service Desk, extracted data writes back to your case system, and your team learns no new tooling.
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Deploy inside your own perimeter

The model works wherever your data lives: on your servers or in a cloud you control. No data resides in your environment, and you’re registered for full compliance with security policies and industry regulations.
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Add AI without ripping out the system that runs it

Your core systems stay where they are. We add a layer that classifies, drafts, or retrieves, then hands control back to a person wherever the decision actually matters.
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Swap or add models without rebuilding

The deployment is model-agnostic. Start on a commercial API, move to a local model like Bielik when compliance demands it, route by language or cost, and the integration underneath doesn’t change.
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Start small, with a way out at every step

Every engagement runs as gated stages. If a stage misses its KPIs, you stop there. You’ve spent days, not a year, and nothing is locked in.
PROCESS
The delivery model: staged, gated, reversible

You fund one stage at a time. Each one ends with a KPI check, and the next stage starts only if the numbers clear. If they don’t, you stop.

PoC (5–10 days)

One narrow slice: a single mailbox, one document type, one process. No integration yet. You get a KPI report measured against your current baseline.

Gate:

Do the accuracy and automation numbers justify going further?

MVP (6-8 weeks)

The key process end to end, with core integrations live and your main categories covered. A working system handling real cases in a controlled scope.

Gate:

Does it hold up on your data, with your edge cases, not ours?

Production (Live rollout)

Full rollout across channels and mailboxes, live integrations, monitoring in place. A running system plus a runbook and dashboards your team can operate.

Gate:

SLA and quality thresholds met before it carries real volume.

Scale (Ongoing)

More volume, more channels, ongoing tuning. From here you either hand off to Managed Services or run it in-house with our support.

Handover:

You own the deployment and decide who operates it next.

Deployment, sovereignty, and control

Two questions decide whether a technical buyer trusts an AI vendor: where does the data go, and what can I see once it’s live? Here’s both.

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Where it runs is your call

On-prem, isolated or air-gapped, private cloud, or hosted. The system is containerized on Docker and Kubernetes and scales out horizontally when load spikes.
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Any model, including local

Commercial or open-source: Bielik, LLaMA, Mixtral, Qwen, or whatever fits. Routing picks the model per request by language, complexity, or cost. For regulated data, everything can run fully local.
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A person stays in the loop

The system suggests; your people decide wherever the call carries risk. You set where that line sits, and it can move as confidence grows.
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You watch it like the rest of your stack

Logs, metrics, and health checks feed into ELK, Grafana, or Prometheus, so model behavior in production is visible to the same team that runs everything else. Ongoing quality checks catch drift before your users do.
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Answers trace back to a source

Responses are grounded in your documents and cited, so you can see why the model said what it said. That traceability is the main control against hallucination, and it’s auditable after the fact.
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The stack, in brief

Runtime: Docker / Kubernetes; Observability: ELK · Grafana · Prometheus; Compliance: GDPR · DORA · NIS2

Testimonial

What our clients say?

By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.

Katarzyna Tomczyk – Czykier
Director of the Innovation and Digitization Division – Retail Banking
Start with a feasibility call, not a sales pitch
Book 45 minutes with our solutions architects. We map your target process, the systems we’d integrate with, and a PoC scope you can approve, with a rough timeline and the KPIs we’d measure against. You leave knowing what a first stage costs and how long it takes.
Book your discovery workshop

let’s get in touch

Contact our consultant

Book 1-on-1 consultation 

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Grzegorz Motriuk

Head of Sales | Application Development

Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.

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FAQ: AI implementation & integration

  • How long does it take to go from idea to a working AI system in production?

    We work in gated stages: a PoC takes 5–10 days and proves the concept on a narrow slice of your process. An MVP (core integrations live, main categories covered) takes 6–8 weeks. Full production rollout follows only after both pass their KPI gates — so you’re never funding something that hasn’t already shown results.

  • Can the AI run entirely on our own infrastructure, without touching a public cloud?

    Yes. The system is containerized with Docker/Kubernetes and can be deployed on-prem, in an air-gapped environment, or in a private cloud you control. No data needs to leave your perimeter.

  • How does the AI integrate with our existing tools like Jira, CRM, or ERP?

    We wire the AI layer directly into the systems you already run. Tickets can route into your Service Desk, extractions can land in your CRM, and the AI can query your ERP –  without replacing any of those systems. Your core stack stays where it is.

  • Which AI models do you use, and can we switch later?

    The deployment is model-agnostic. We can start on a commercial API (OpenAI, Anthropic, etc.) and migrate to an open-source local model – Bielik, LLaMA, Mixtral, Qwen — without rebuilding the surrounding system. Model routing selects the right model per request, so you’re not locked in.

  • How is the AI monitored once it’s in production?

    Logs, metrics, and health checks feed into ELK, Grafana, or Prometheus – whichever observability stack your team already uses. Ongoing quality checks catch model drift before it affects users.

  • How do we maintain oversight and control over what the AI decides?

    A human stays in the loop wherever a decision carries risk. The system suggests; your people decide at the thresholds you define. All responses are grounded in your

  • What happens if a PoC or MVP stage doesn’t hit its KPIs?

    You stop there. Every stage ends with a defined KPI check; the next stage only starts if the previous one passes. You’ve spent days or weeks, not months – and you walk away with a clear answer rather than a sunk cost.

  • Do you have security/compliance documentation we can use for vendor due diligence (audits, certifications, privacy)?

    Yes. Deviniti’s Trust Center provides downloadable security and compliance documentation for vendor due diligence, including:

    • ISO/IEC 27001 certificate (ISMS)
    • ISO/IEC 27017 certificate (cloud security controls guidance)
    • SOC 2 Type 1 report
    • Privacy & Security Overview
    • CAIQ Lite – Apps (Cloud Security Alliance questionnaire)
    • Cloud Hosting Locations & Data Residency Options
    • Information Security Policy (AUP)

    For GDPR due diligence, Deviniti provides a Data Processing Agreement (DPA) under GDPR Article 28. As a general rule, personal data processing takes place within the EU/EEA. Where international transfers are necessary, Deviniti uses lawful transfer mechanisms (e.g., EU adequacy decisions and Standard Contractual Clauses) with additional safeguards. For products that support it, you can also choose service delivery exclusively via infrastructure located in the EEA.