AI applied to digital products
Start with a business problem, not a model
PartITech designs and integrates artificial intelligence features into web and business applications. Our role is to identify relevant uses, organise data, select an architecture and measure quality before production.
Generative AI can accelerate search, summarisation, classification and assistance. It remains probabilistic: its outputs must be evaluated, traced and controlled according to the service’s level of risk.
- Value
- An explicit business outcome and success criteria
- Control
- Managed data, access and outputs
- Measurement
- Test sets before and after release
Focused uses
Where AI can create verifiable value
We retain a use case when the expected benefit, available data and level of control can be defined.
Search and knowledge
RAG, semantic search and sourced answers across documentation, catalogues or knowledge bases.
Documents and content
Extraction, classification, summarisation, matching and content preparation with human validation where needed.
Business assistants
Contextual support within existing tools, with explicit permissions, history and limits on actions.
Controlled automation
Qualification, routing or enrichment of flows, with confidence thresholds and manual fallback.
Architecture matched to constraints
API, private cloud or controlled infrastructure
The choice does not depend solely on model performance. It includes data sensitivity, latency, volumes, costs, contracts, portability and operational skills.
Frame an AI projectAPI services
Fast start and managed services, with analysis of transfers, retention and vendor dependency.
Private deployment
Greater control of data and operations, in exchange for increased technical responsibility.
Hybrid architecture
Workloads distributed according to sensitivity, expected performance and acceptable cost.
Beyond the prototype
Conditions for operable AI
Data and permissions
Identified sources, controlled quality, propagated permissions and a defined retention policy.
Security
Protected inputs, secrets and tools; prevention of leaks, injections and unauthorised actions.
Evaluation
Reference cases, answer quality, citations, refusals, latency and cost monitored over time.
Human oversight
Validation, recourse and logging matched to the possible consequences of an error.
Operations
Observability, budgets, prompt and model versions, fallback strategy and incident management.
Adoption
Clear interface, stated limits, training and user feedback integrated into improvement.
Three entry points