Internal knowledge is hard to find
Teams search across scattered documents, tools and conversation histories.
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AI & machine learning · Applied intelligence
AI connected to your data.Evaluated in your context.
We turn a use case into a testable system: data, model, integration, evaluation criteria and human control are designed together.
Since 2021 · 20+ projects · Europe & Gulf
AZ / SERVICEAI & machine learning
SCOPEPRODUCT → PRODUCTION
FOOTPRINTEUROPE / GCC
When this service is useful
When AI has to move beyond the demo.
The question is not only whether a model responds, but whether it is useful, controllable and possible to integrate.
Documents require too much manual work
Reading, classification, extraction or preparation follow repetitive rules.
A prototype will not reach production
Quality, cost, privacy, latency or integration have not been measured.
A decision could use a predictive signal
Data exists, but its quality and the value of the use case still need testing.
How we frame the problem
Start with the decision to improve.
We define what the system should help someone do before selecting a model.
01
The problem
An AI demo can look persuasive while remaining inaccurate, expensive or impossible to integrate.
02
Our intervention
We frame the data, risk and evaluation criteria, then test a complete workflow with its controls.
03
The intended result
A use case whose value, limits and operating conditions can be judged from observable evidence.
Capabilities
From use case to evaluated system.
Technology follows the problem, the data and the level of risk.
01
Feasibility & data
Source quality, constraints, risks and a comparison baseline.
02
Agents, RAG & language
Augmented retrieval, business tools and text or document workflows.
03
ML & computer vision
Prediction, classification or image analysis where the data supports it.
04
Evaluation & integration
Test sets, human controls, APIs, cost tracking and production behaviour.
Possible deliverables
What enables a decision and responsible operation.
Deliverables expose observed performance and the conditions of use.
- 01
Feasibility note and data map
- 02
Prototype or pilot connected to the workflow
- 03
Evaluation set and acceptance criteria
- 04
Agreed RAG pipeline, agent, model or inference service
- 05
Documented human controls, logs and limitations
- 06
Integration, monitoring and evolution plan
Example scopes
Scopes that test a hypothesis.
Each example begins with a measurable question, not a promise of universal AI.
Internal knowledge assistant
Search a defined corpus, cite sources and organise user feedback.
RAGSearchEvaluation
Assisted document processing
Extract or classify information with human validation and traceability.
DocumentsWorkflowHuman control
Prediction or vision pilot
Assess data quality and compare a model with an explicit baseline.
MLVisionBenchmark
Guardrails
What we refuse to hide.
A probabilistic system needs explicit criteria and responsibilities.
- 01
No accuracy level, complete automation or return on investment is guaranteed before evaluation.
- 02
Sensitive uses define suitable human oversight and routes for review.
- 03
Privacy, data rights, model providers and retention are stated in the scope.
- 04
Quality can change with data and models; required monitoring is decided before production.
How the engagement runs
Reduce risk before industrialising.
Production is a decision based on observed results.
01
Formulate
Define the task, current baseline and affected users.
02
Assess the data
Check access, quality, volume, rights and representativeness.
03
Test
Build a pilot and measure behaviour, errors, cost and latency.
04
Integrate
Connect the system, controls and monitoring to the real workflow if results justify it.
Frequently asked questions
Applied AI questions.
Good answers start with the data and level of risk.
01Do we need a large amount of data?
It depends on the use case. RAG can use a defined document corpus; predictive modelling needs representative data and a reliable baseline.
02Do you build your own models?
We can integrate existing models and managed services or train specific components when the data and need justify it.
03How do you measure quality?
With an evaluation set, comparison baseline and criteria tied to the real task, plus human validation where needed.
04Can you guarantee accuracy?
No. We measure observed performance, document limitations and help decide whether it is sufficient for the context.
05How is our data protected?
Provider choice, access, retention, transfers and responsibilities are defined around the project’s data and obligations.
Let’s discuss the need
Which use case genuinely deserves AI?
Start with the task, available data and the decision you want to improve.
Explore all servicesNo commitment · Clear scope · No specification required