Artefact

artefact.com

Artefact’s documented technology practice covers both planning and implementation: defining data and AI priorities, establishing governance and delivery arrangements, building applications, and engineering platforms and ML deployment pipelines. Buyers should distinguish these scopes rather than assume an advisory engagement includes production delivery. Named cases substantiate application development and platform implementation, but their maturity varies from prototypes and pilots to implemented data foundations. The Research Center and Skaff accelerators are separate activities, not independent evidence of a contracted implementation or managed service. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6] ^[7]

Services and delivery

AI and generative-AI application development

Implementation. Develops, deploys, and scales enterprise AI applications, addressing use-case implementation, response quality, latency, scalability, and front-end experience. The commercial offer covers application delivery alongside supporting infrastructure and adoption work. Buyers should specify whether the contracted endpoint is a prototype, pilot, or production application. ^[2]

AI delivery operating model and governance advisory

Advisory. Helps enterprises establish the organisational arrangements needed to deliver AI solutions. Scope includes governance, operating models, infrastructure decisions, adoption, training, and ethics. This advisory component is distinct from building the applications themselves and should have its own agreed outputs. ^[2]

Machine-learning engineering and MLOps

Implementation. Moves machine-learning work from proof of concept toward deployable products. Engineering includes testing data, features, and models; versioning data, models, and experiments; preparing rollbacks; and integrating ML assets into continuous integration and delivery pipelines. This establishes production engineering, not a separately verified round-the-clock operations service. ^[3]

AI for customer care

Implementation. Implements conversation analysis, real-time agent assistance, and first-level support automation. Documented applications include identifying contact reasons and dissatisfaction, providing contextual scripts and information retrieval, qualifying requests, and routing complex enquiries. The offer includes a named MAIF example of NLP algorithm development for call-topic analysis. ^[8]

Data and AI strategy advisory

Advisory. Defines a data and AI vision, prioritised use cases, and an actionable roadmap. Work includes governance, organisational arrangements, platform planning, delivery capabilities, and associated change considerations. This is technology strategy advisory; subsequent software or platform implementation requires a separately defined scope. ^[1]

Data-platform assessment and architecture advisory

Advisory. Assesses existing data environments, supports technology selection, and designs platform architecture. This helps buyers determine the foundations needed for data integration and processing before implementation. Platform advisory alone does not establish model development or an AI application delivery scope. ^[9]

Data-platform implementation

Implementation. Configures and implements data platforms, integrates sources, establishes master-data management, improves data quality, and implements real-time processing. The service supports enterprise data foundations; it should not be treated as AI model development without additional scope. ^[9]

Artefact Research Center

Research. Conducts research connecting academic work and industrial applications, including model transparency and ethics. The reviewed material establishes a research activity, not a defined, separately purchasable client service. ^[6]

Skaff technical accelerators

Software product. Provides reusable technical assets including Python packages, Terraform modules, repository templates, and dashboard wireframes. Named assets include a Vertex AI starter kit and a LangChain industrialisation kit. These are delivery accelerators, not evidence of a paid SaaS subscription or standalone managed service. ^[7]

Company details

Engagement and delivery

Artefact describes multidisciplinary feature teams involving business owners, data scientists, software engineers, DevOps, and UX designers. Its ML engineering approach includes release testing, versioning, and rollback preparation. These are documented delivery practices, but the reviewed evidence does not establish standard post-launch support commitments. ^[3]

Track record

For Legrand, Artefact reports a joint team developing proofs of concept and MVPs for Gaia and Elia, addressing multilingual product-content enrichment and product-information assistance. Work included data pipelines, algorithms, dashboards, product ownership, and Azure OpenAI API connectivity. The case reports approximately six months for the two MVPs; broader production stabilisation and industrialisation were subsequent work, not a completed international rollout. ^[10]

For Servier, Artefact reports collaborating with Google on MedtrAIner, an AI-avatar application for medical representatives to practise visits and receive feedback. Named components include Gemini Native Audio, LiveKit, and HeyGen. Artefact reports an operational prototype in eight weeks and pilot testing in two countries. Deployment to more than 6,000 representatives across over 50 affiliates is described as a 2027 target, not a completed result. ^[5]

For Nexans, Artefact reports two workstreams: an AI vision and use-case roadmap, and implementation of a centralised Azure Databricks platform with governance. Documented work includes core-system integration, reusable data assets, monitoring, quality management, and a domain-based operating model. The roadmap’s 2028 horizon is not a completion date, and the case does not provide enough model-level detail to infer specific algorithms. ^[4]

Scope and limitations

The supplied evidence establishes AI advisory and implementation, ML production engineering, and data-platform services, but does not settle pricing, minimum engagement size, IP ownership, or contractual acceptance criteria.
Production engineering evidence does not establish a separately contracted 24/7 managed-operations service. Support coverage, incident response, and service levels require verification.
Client cases demonstrate different delivery stages. Prototype and pilot milestones should not be presented as completed global deployments or guaranteed outcomes.
Research and accelerator materials do not establish standalone commercial research engagements, paid software subscriptions, or managed services.

Questions to ask

Does the proposed scope end at prototype, pilot, production launch, or ongoing operation, and what acceptance criteria apply?
Who owns application delivery, integration, testing, and production acceptance?
Who owns client-specific code and modifications to reusable accelerators?
Which platforms and model providers are contractually supported, and what constraints affect deployment?
What evaluation, monitoring, incident-response, support, and handover obligations are included?
Can Artefact provide references at the required deployment scale and maturity?

Overview