AI implementation partner: from strategy to production and maintenance

Most AI initiatives don't stall on the model, but on the handover. A consultancy delivers a report, a development shop builds a proof of concept, and then you're left to work out how to take it into production and keep it running. We are a hybrid implementation partner: the same people who shape the AI strategy also build the software, set up the MLOps pipeline and remain responsible for operations.

AI readiness audit Strategy and roadmap Proof of concept to production MLOps and observability Operations and ongoing development

Discuss your AI initiative Why hybrid works

Why a hybrid implementation partner works differently from a consultancy

Traditional AI initiatives are split between three parties: a strategy firm that produces the roadmap, an implementation partner that builds it, and an internal IT department that has to run it. Every handover costs time, context and money. We bring those roles together — strategy, engineering and operations — in one team that owns the entire journey.

An AI strategy that ignores existing infrastructure, data quality or the development team that has to build it remains a PowerPoint document. Conversely, a development shop that starts building without strategic context often picks the wrong model for the wrong problem. Only when both perspectives sit with the same people can you compress the time between idea and production from months to weeks.

Our positioning is deliberately not that of a management consultancy. We have no separate advisory arm that steps back once the slides are finished. Every strategist in our AI practice has hands-on experience with production AI: setting up retrieval-augmented generation pipelines, fine-tuning open-source models, integrating vector databases such as pgvector or Qdrant, and keeping LLM inference running on vLLM or BentoML. As a result, our advice is grounded in what is technically feasible and what remains operationally affordable, not in what appears in a trend report.

At the same time, we are not a pure development shop. A development partner that only builds to specification delivers what you ask for, not what you need. With AI, that difference is painful: a poorly chosen architecture can mean starting over after six months. We ask questions about your business case, the AI Act risk classification of the application, the DPIA requirements for personal data and the total cost of ownership of the model in production — before a single line of code is written.

AI development Enterprise AI implementation AI consultant AI discovery workshop

The five phases we own

An AI implementation is not a project with an end date. It is a lifecycle that runs from the first idea through to ongoing management. We guide every phase, not as separate engagements, but as one continuous journey with the same people.

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Strategy and AI readiness audit

We map out your use cases, data landscape and organisational maturity. Which processes lend themselves to AI, which data is available and usable, and where does the greatest business value lie? The output is an opportunity map with prioritised cases and a roadmap that accounts for dependencies, risks and realistic timeframes.

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Pilot and proof of concept

The prioritised case is turned into a working prototype. We build an end-to-end pipeline, from data source through model to user interface, so you can tangibly validate whether the hypothesis holds. Using evaluation frameworks, we measure output quality objectively, not on the basis of anecdotal demos.

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Production architecture and MLOps

The pilot is rebuilt for production: scalable inference infrastructure, CI/CD for models, version control with MLflow, automated re-training and rollback mechanisms. We use proven MLOps tooling such as BentoML, vLLM and Kubernetes-native serving rather than home-built scripts.

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Scaling and governance

Once the first use case is live, the challenge shifts to scaling: multiple models, multiple teams, multiple business units. We set up governance around the model registry, access rights, cost control per workload and compliance monitoring in line with the AI Act risk classification.

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Maintenance and drift detection

AI models age. Data drift, concept drift and performance regression are part of daily work. Our observability stack monitors latency, cost per request, output distributions and accuracy against a gold-standard test set. When a metric drifts, you receive an alert, not a surprised customer.

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Security, GDPR and AI ethics

Personal data, sensitive business data and generated output call for a carefully considered security model. We draw up DPIAs, implement PII redaction in prompts and logs, set retention policies for model input and ensure the solution fits the risk classification under the European AI Act.

How this differs from a consultancy or a dev shop

The three main models on the market are clearly distinguishable. A management consultancy delivers strategy and then refers you to an implementation partner. A dev shop builds to specification but does not advise on the right use case. A hybrid partner combines both roles and carries the result through to production.

Aspect	Consultancy only	Dev shop only	Hybrid partner
Output	Report, roadmap, slides	Code to specification	Working system in production
Accountability	Stops at handover	Starts at specification	End-to-end, one team
Technical depth	Based on trends	Based on stack preference	Based on TCO and evaluation results
Business case validation	In the strategy phase	Not explicit	Continuous, with evaluation frameworks
POC lead time	No POC, plan only	Fast, sometimes without context	Weeks, with measurable validation
Operations and ongoing development	Handed over	Separate SLA, different people	The same team remains accountable
Risk of vendor lock-in	Low, but no output	High on stack choices	Open source first where possible

No single model is universally better. For a board-level transformation agenda, a large consultancy can be useful. For a standalone build project with clear specifications, a dev shop works well. But when you want to embed AI structurally in your core processes, with multiple use cases over several years, the friction of handovers between separate parties becomes costly. That is where the value of a hybrid model lies.

IT modernisation consultant Enterprise software Platform engineering

Which technology we use and why

Our technology choices are pragmatic and open-source-first. We don't want you to be locked into a vendor three years from now because the inference layer or model registry is proprietary. At the same time, we use managed cloud services where that makes operational sense, for example for GPU capacity during peak periods or for compliance-sensitive hosting within the EU.

For retrieval-augmented generation, we work with vector databases such as pgvector (integrated into PostgreSQL) or Qdrant, depending on scale and query profile. Model inference runs on vLLM or BentoML, with fallbacks to managed providers where you prefer not to self-host or are unable to. We build evaluation frameworks around OpenAI Evals, RAGAS or bespoke test harnesses, depending on the domain-specific quality metrics. For model tracking and versioning we use MLflow, and for pipeline orchestration Prefect or Dagster.

We build multi-agent architectures, where several LLM agents collaborate on a more complex task, when they demonstrably deliver better results than a single-prompt approach. We are cautious about the hype surrounding agent frameworks: a well-designed RAG pipeline with clear tool calls is often more robust than an autonomous agent graph that is difficult to debug. Our choice depends on your use case, the evaluation results and the operational complexity your team can sustain.

Python PyTorch Hugging Face Transformers vLLM BentoML MLflow pgvector Qdrant LangChain LangGraph RAGAS Prefect Dagster FastAPI Kubernetes Docker Terraform PostgreSQL OpenTelemetry Grafana

When a hybrid implementation partner makes the difference

Not every organisation needs a hybrid partner. These are the situations where the handover friction between separate parties becomes most painful, and where an integrated team delivers the most value.

First serious AI application in production

You previously had external consultants draw up an AI strategy and a development shop build a proof of concept, but the step to production never happened. The reports ended up in a drawer and the prototype stopped running. This time you want a partner that doesn't just advise and build, but also puts the solution into production and keeps it running, including an MLOps pipeline, observability and ongoing model management.

AI strategy without in-house data science capacity

Your IT organisation is strong in traditional software but lacks data scientists, ML engineers and MLOps specialists. You don't want to launch a years-long recruitment campaign, yet you also don't want to become dependent on an external party. We provide the full range, actively transfer knowledge to your own people and build the solution so that you can later choose to outsource its management without requiring a rebuild.

Multiple use cases on a shared platform

You see five to ten areas in the organisation where AI can add value, from customer service automation to document processing to predictive analytics. Rather than five separate projects with five different vendors, you want a shared AI platform with central governance, common infrastructure and reusable components. We build that platform and the first use cases on top of it.

Compliance-sensitive sectors under the AI Act

You operate in healthcare, fintech, the public sector or another sector where AI falls under high risk according to the AI Act. Alongside model development there is work on DPIAs, risk management systems, transparency requirements and human-in-the-loop controls. We combine technical development with compliance engineering, rather than having separate lawyers write reports that the builders never read.

AI document processing AI customer service Predictive analytics Custom healthcare software Fintech software

Governance, compliance and the AI Act in practice

The European AI Act, the GDPR and sector-specific legislation place real demands on production AI. We do not treat compliance as a final chapter, but as a design principle from day one.

Risk classification under the AI Act

Every AI application is given a classification: minimal risk, limited risk, high risk or unacceptable risk. Together with you and your legal team, we determine which category your use case falls under and which obligations apply — from transparency requirements to mandatory conformity assessment. That outcome directly shapes architectural decisions.

DPIA and privacy by design

Whenever personal data is processed — for training or inference — we prepare a Data Protection Impact Assessment. We implement PII redaction in prompts and logs, retention policies for LLM input, and data processing agreements with sub-processors. Training data is pseudonymised where possible and anonymised where necessary.

Audit trails and model registry

For every model prediction in production, we record: which model version, which prompt, which retrieved context and which output. A model registry built on MLflow tracks versions, hyperparameters and evaluation results. In the event of an audit or incident, reconstruction is not archaeology but a query.

Human-in-the-loop and explainability

For high-risk applications, human intervention is not optional but a requirement. We design interfaces where a person can accept, reject or correct an AI suggestion — and where that feedback feeds back into the improvement process. For explainability, we use techniques such as SHAP, attention visualisations and traceability of retrieved context.

Cost optimisation and FinOps for AI

Inference costs escalate quickly without control. We implement caching layers, prompt compression, model routing between smaller and larger models, and cost monitoring per request and per business unit. You know what an AI feature actually costs and can steer on cost per outcome rather than cost per token.

EU hosting and data sovereignty

For compliance-sensitive workloads, we run models within European data centres or on your own infrastructure. Open-source models that we host ourselves are often a better option than a US-based SaaS API where data residency is a requirement. We advise on the trade-off between operational simplicity and data sovereignty.

Information security policy Coordinated vulnerability disclosure IT operations optimisation

Frequently asked questions about an AI implementation partner

What exactly is a hybrid AI implementation partner?

A hybrid partner combines strategic advice, technical implementation and operational management in one team. Rather than a consultancy that delivers a report and then leaves, or a development shop that only builds to specification, the same group of people remains responsible for the AI solution from the first idea through to production and management. This prevents handover losses between parties and considerably shortens time to production.

How does it differ from an AI consultancy?

A traditional AI consultancy delivers strategy, roadmap and business case — but no working software. For implementation, they refer you to a development party. We do both: the same people who shape your AI strategy also build the production pipeline, set up MLOps and observability, and remain responsible for management after go-live. That reduces handover friction and ensures strategic choices remain technically feasible.

When is a hybrid partner the right choice?

Especially when you want to bring AI into your organisation structurally, with multiple use cases over several years, and when you still have limited internal data science and MLOps capacity. For a standalone, self-contained project with clear specifications, a dev shop may be sufficient. For a transformation roadmap alone, without a build mandate, a large consultancy can be useful. The hybrid model comes out on top where continuity and handover costs are highest.

Which technologies do you use for production AI?

Our stack is open-source-first: Python, PyTorch, Hugging Face Transformers, vLLM and BentoML for inference, MLflow for model tracking, pgvector or Qdrant for vector search, and Kubernetes for orchestration. For evaluation we use OpenAI Evals, RAGAS or domain-specific test harnesses. Where managed cloud services make operational sense, we deploy them, but never at the expense of vendor lock-in on critical layers.

How do you handle the European AI Act?

Together with you and your legal team, we determine the risk classification of each AI application: minimal, limited, high or unacceptable risk. For high-risk applications, we implement the corresponding obligations from the design stage: human-in-the-loop, transparency statements, audit trails, conformity assessment and a functioning risk management system. Compliance is not an afterthought but a design principle.

How long does it take for the first use case to go live in production?

A proof of concept usually takes a few weeks. The move to production depends on integration requirements, data quality, compliance classification and the number of interfaces with existing systems. We work iteratively in sprints, so you see results along the way and can adjust course before investing heavily.

What determines the investment in an AI implementation project?

The main cost drivers are the complexity of the use case, the quality and availability of data, the number of integrations with existing systems, the compliance requirements and the desired scale in production. An internal knowledge base chatbot is a very different project from a high-risk classification under the AI Act with human-in-the-loop. We always start with a clearly defined first use case to validate the business case.

Do you stay involved after go-live?

Yes, that is precisely the hallmark of a hybrid partner. After go-live we monitor model performance, latency, cost per request and data drift. When accuracy regresses or the market shifts, we retrain models, revise prompts or adjust retrieval strategies. You can also gradually take over this management once your own team is ready for it, as we build in a way that allows this without re-architecting.

Looking for a long-term AI implementation partner?

We discuss your use cases, the maturity level of your data landscape and which phases we can take on within your programme, without obligation. No report without consequence, no isolated POC, but a working AI system in production.

Schedule an exploratory conversation