Customised Solutions

Customised AI solutions built for your reality

We engineer AI systems for use cases where standard products cannot meet your data, integration, sovereignty or process requirements.

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Generic AI solves generic problems. We build for yours.

Standard AI products solve standard problems. The highest-value opportunities usually sit somewhere else: domain-specific workflows, proprietary data, complex integrations, or environments where data cannot leave controlled infrastructure. That is where off-the-shelf tools run out of road.

evoila engineers customise AI solutions across conversational AI, RAG and Graph-RAG, classical machine learning, computer vision, predictive analytics, operational intelligence and agentic workflows.

  • Fit-for-purpose architecture: AI systems designed around your data, processes, integrations and operating constraints.
  • Faster path from prototype to production: Clear delivery patterns for prototypes and full system builds.
  • Private deployment where regulation demands it: Cloud, hybrid or on-premise architectures for regulated and data-residency-sensitive environments.
  • Production readiness considered from day one: Evaluation, observability, security, governance and operations considered from the start.

Standing still costs more than you think

Every month spent evaluating a generic tool is a month a competitor spends running the workflow you are still doing by hand. Standard products will not close that gap for you.

The Challenge

Your AI prototype works in the demo. Production is a different story.

Off-the-shelf AI rarely fits complex enterprise reality. Prototypes can look promising and still fail before go-live, because integration, observability, evaluation and human oversight were never designed in. Real systems must connect to existing infrastructure without creating new silos, and private deployment requirements add GPU orchestration and model hosting on top.

Off-the-Shelf does not fit

Your workflows, data and compliance requirements are too specific for a generic product. A vendor template solves someone else’s problem, not yours.

Pilots stall before production

Observability, evaluation and operational handover are missing from most prototypes. What impresses in a demo often cannot survive daily production load.

Data readiness becomes the bottleneck

Access, quality, governance and joining logic require real engineering work. Most AI timelines slip here, not in the model itself.

Private deployment adds complexity

On-premise AI requires GPU, model and infrastructure expertise most teams do not have in-house. Sovereignty requirements do not excuse you from production standards.

The good news: none of this means starting from scratch

It means working with a partner who treats production engineering as the starting point, not an afterthought.

Our Solution

One assessment. The delivery path that actually fits.

Customised AI starts with a structured assessment of your use case, data, system landscape, integration depth, regulatory requirements and operational constraints, not a vendor reference architecture.

From there, we define the right path: some use cases begin with a clickable prototype to align stakeholders, others move directly into requirements engineering, system design, implementation and deployment. We build conversational AI agents, RAG and Graph-RAG systems, custom ML pipelines for classification, forecasting, anomaly detection, computer vision and NLP, plus fine-tuned models and agentic workflows.

Benefits

  • Assessment before architecture, every time
  • Cloud, hybrid or on-premise, same standards
  • Reusable components across future AI work

Customised AI Solutions

From real-world context to production

Assess the reality

Understand the situation before designing anything.

• Use case & business objective
• Existing processes & workflows
• Data availability & quality
• System landscape & integrations
• Security, compliance & operating constraints

APPROACH DEPENDS ON:
Use-case maturity, data readiness, integration depth and regulatory constraints

Prepare the foundation

The work that makes AI actually fit your operation.

• Process adaptation
• Data access & integration
• API & legacy-system enablement
• Human-in-the-loop controls
• Governance & ownership

OFTEN THE DECIDING WORK:
Process adaptation and data readiness are usually the real bottleneck.

Engineer the solution

The right technique for the use case at hand.

• Conversational AI agents
• RAG & Graph-RAG systems
• Custom ML pipelines
• Computer vision & NLP
• Agentic workflows
• Fine-tuning where justified

PATTERN PICKED, NOT PRESCRIBED:
Prototype, MVP or production build, depending on what the use case needs.

Deploy & Operate

Run it well and keep it working.

• Cloud, hybrid or private deployment
• Evaluation & monitoring
• Observability & audit trails
• Optimisation & retraining
• Handover or managed operations


PRIVATE DEPLOYMENT:
Where regulatory or operational requirements call for it.

The foundation layer across all four stages

Production Engineering Discipline
The difference between a working prototype and a system you can trust in production.
CI/CD | Security review | Evaluation pipelines | Data governance | Observability | Operational readiness

The delivery path is chosen for the use case , not a fixed template.

Learn moreGet started
Tech Deep Dive

Customised AI is rarely one architecture

It is a composition of managed model endpoints, retrieval systems, vector and graph stores, orchestration layers, fine-tuned models, evaluation pipelines and operational controls.

Process and application adaptation

AI rarely creates value by being added on top of an unchanged process. We analyse how the target process currently works across users, applications, data flows and decision points, then adapt the workflow so AI can support it reliably. This may include redesigning handovers, changing approval steps, exposing legacy functionality through APIs, adding human-in-the-loop controls, adapting application interfaces or restructuring how information is captured and reused. The result is not only an AI component, but a process and application setup that can actually use AI in production.

Cloud-native build on Azure

For Azure-based environments, we design solutions using Azure landing zones, AKS, Azure Storage, Azure AI Foundry, Azure OpenAI, Azure ML and managed observability services. Application layers are selected to fit your enterprise stack or build from scratch for match your individual needs.

Lakehouse-Centric build on Databricks

For data-heavy AI systems, Databricks can provide the lakehouse foundation across hyperscalers. We use Databricks Agent Bricks, Mosaic AI, MLflow and lakehouse-native pipelines for ML and LLM workflows where centralised data governance, model lifecycle management and cross-platform analytics matter. This pattern is especially relevant for predictive analytics, operational intelligence, large-scale feature engineering and data-intensive RAG use cases.

Private and on-premise build

Where data residency, sovereignty or IP sensitivity rule out public endpoints, we design private AI architectures on Broadcom VMware Private AI Foundation with NVIDIA. Models, vector stores, orchestration services and application components can run inside the customer environment. This is relevant for BaFin-relevant, KRITIS-related, defence-related, public sector and other regulated or security-sensitive contexts.

Technical Advantages

Six reasons the engineering holds up in production

Built around your reality

Architecture decisions follow your data and constraints, not a template.

Same discipline, any deployment

Azure, Databricks and on-premise follow one standard for evaluation and governance.

Engineered for Go-Live

Integration, error handling and monitoring are designed before launch, not after.

Reusable by design

Proven components reduce effort on every AI initiative that follows.

Operations from the start

Runbooks, telemetry and ownership models are part of the architecture phase.

Compliance-aligned

Audit trails, model lineage and human oversight support regulated environments.

Your partner of choice

Assessment-first consulting.
Production-grade engineering.

evoila combines assessment-first AI consulting with production engineering across the full customised AI lifecycle, from requirements through architecture, implementation, deployment and continual improvement. Delivery spans Microsoft Azure, Databricks and Broadcom VMware Private AI Foundation with NVIDIA, with direct experience in BaFin-relevant financial services, KRITIS-related infrastructure, public sector and defence-related industries.

Technologies & Partner

One ecosystem, benchmarked per engagement

Microsoft Partner ecosystem
As a Microsoft Partner, evoila delivers customised AI solutions using Azure AI Foundry, Azure OpenAI, Azure ML and related Azure services. For qualifying customer-specific engagements, partner co-funding options may be available.

Broadcom Partner ecosystem
As a Broadcom Partner, evoila supports private and sovereign AI deployments with Broadcom VMware Private AI Foundation with NVIDIA for on-premise LLM scenarios under strict data-residency or regulatory requirements.

Databricks Partner ecosystem
As a Databricks Partner, we use Databricks Mosaic AI, MLflow and lakehouse-native data pipelines for ML, LLM and operational intelligence workloads across hyperscalers

Multi-model LLM ecosystem
We work with managed model endpoints and open-weight deployments from various providers. Models are benchmarked per engagement on accuracy, latency, cost, security and data-residency requirements.

Open protocols and frameworks
Model Context Protocol, OpenAPI, MLflow, LangGraph, Pydantic AI, FastAPI and Streamlit help integrate AI systems into existing enterprise architectures, developer workflows and operational processes.

Open-source foundation
Our engineering builds on PyTorch, Hugging Face, MLflow and the broader open-source ML ecosystem, combining enterprise platforms with proven community-backed technologies where they fit the use case.

References

Four industries, one engineering standard

From Steel Lists to Sovereign RAG: Built to Fit Each Time

Financial Services
Conversational AI Agent

For a leading German financial services organisation, evoila delivered end-to-end development of a conversational AI agent for domain-specific customer and employee interactions. The engagement covered requirements analysis, dialogue-flow design, implementation with Azure OpenAI, LangGraph, Streamlit and FastAPI, and integration into existing infrastructure. Continuous evaluation and optimisation ensure domain relevance and production quality over time.

Real Estate
Cross-platform Rental Management

For one of Germany’s leading digital real estate platform providers, evoila developed a cross-platform rental management application on Microsoft Azure. The solution combines a tenant portal for contract access and modifications with a landlord dashboard for property and business management. AI-orchestrated workflows automate routine rental-management processes, reducing manual coordination and creating a more scalable operating model.

Public Sector
RAG-based Conversational AI

For public security organisations operating under strict data-residency and security constraints, evoila designed and implemented a RAG-based conversational AI system. The solution combines semantic retrieval, prompt optimisation, conversation-context management and response-quality monitoring. Users can access complex information through a controlled conversational interface while reducing hallucination risk and supporting secure, domain-specific knowledge access.

Industry
Operational Intelligence Platform 

For an established industrial operations software provider, evoila built an operational intelligence platform that integrates logistics tracking, manufacturing process data and workplace information across multiple enterprise systems. Agentic workflows retrieve, correlate and interpret operational signals from connected platforms, enabling real-time insights and alerts across manufacturing and logistics processes.

Industry
Graph-RAG Knowledge Platform

For an industrial customer, evoila provided strategic consulting, design and architecture for a Graph-RAG-based knowledge platform on Microsoft Azure. The engagement covered proof of concept, cost optimisation, technical design and implementation. The customer’s internal wiki became an AI-powered knowledge base accessible through natural-language queries with controlled retrieval and traceable responses.

Industry
AI based Steel List Extraction System

For an industrial steel processing company, evoila developed an AI-based application for extracting, editing and managing steel lists from complex technical documents. The project covered requirements analysis, implementation and operations. At its heart was the design, training, and evaluation of a system that unified multiple AI approaches (Graph Attention Networks, Object Detection, Image Segmentation, OCR), enabling data extraction from a wide range of steel list templates as well as from technical drawings of dimensioned bending-shape sketches.

Move from off-the-shelf compromise to AI engineered for your reality.

One assessment tells you whether that means a click prototype, a full systems build, or something in between. Either way, you find out in weeks, not quarters.

Let’s get in touch

Talk to the person who builds this.

One direct line to our AI lead, no ticket queue in between.

Dominic Petrak

Dominic Petrak

Unit Lead – AI

FAQs

Commonly asked questions about Customised Solutions

No. Agentic AI is autonomous, tool-using systems. Customised AI Solutions is broader, covering RAG, classical ML pipelines, computer vision and integration projects that may not need agents at all.

Yes. We design private AI deployments using Broadcom VMware Private AI Foundation with NVIDIA and locally hosted open-weight models, supporting BaFin-relevant, KRITIS-related and security-sensitive environments.

Get in touch and describe your use case, data and regulatory constraints. We recommend whether a Solution Design Session, Click Prototype or full Systems Engineering path fits best.