Services
AI consulting services
From your first machine learning use case to models scaled across departments: strategy, predictive models and custom AI applications that deliver measurable value, not technology experiments.
In short
Stralya's AI consulting practice takes you from a first machine learning use case to models scaled across departments: strategy, predictive models and custom AI applications that deliver measurable value. Practitioners, not researchers, work with your IT team, and tell you honestly when AI is not the right answer.
-38%
Manual review workload
A claims fraud detection model for an insurer, delivered ahead of schedule.
72 h
Failure lead time
Predictive maintenance across three production lines, since expanded to all twelve.
3 to 9 months
Full implementation
From solution design to deployment, with regular checkpoints throughout.
AI services
Our AI consulting services
Each service has its own page with the method, the deliverables and the guides that support it.
AI strategy
Where AI creates the most value in your organization: process mapping, data readiness evaluation, and a use-case roadmap ranked by ROI, feasibility and time to value, with governance to sustain it.
Machine learning
The full lifecycle: problem framing, feature engineering, training, validation, deployment and monitoring. NLP, computer vision, supervised and unsupervised learning, with interpretability your teams can defend.
Predictive models
Demand forecasting, churn prediction, failure anticipation, fraud detection, real-time pricing. Backtested, cross-validated and shadow-deployed before touching live processes, with feedback loops against drift.
Custom AI applications
Intelligent assistants, recommendation engines, document processing pipelines, semantic search and domain copilots, integrated with your ecosystem and architected for maintainability.
When to call us
Signs you need an AI consultant
- 01
AI is on the agenda but no use case is ranked
A roadmap ranked by ROI, feasibility and time to value replaces enthusiasm with a decision.
- 02
Pilots work in a notebook and never ship
Models create value only once they are deployed, monitored and integrated into your ecosystem.
- 03
Your data readiness is unclear
A data audit shows what can be built now, what needs preparation and what should not be attempted.
- 04
You need a model your own team can maintain
No black box: we work iteratively and transparently, and train your team before handover.
How it works
Structured process value from week one
PHASE 01
Discovery & assessment
Two to four weeks immersed in your business context: data audit, stakeholder workshops, and an AI opportunity assessment ranking use cases by impact and feasibility.
PHASE 02
Solution design
Technical architecture, data pipeline requirements, modeling approach and success criteria, validated by a proof of concept on a representative slice of your data before full-scale commitment.
PHASE 03
Build & validation
Models trained and rigorously evaluated against the agreed metrics, in regular checkpoints. No black box: we work iteratively and transparently, incorporating your feedback throughout.
PHASE 04
Deployment & handover
Integration into production, team training, monitoring frameworks, and a defined support period to fine-tune against real-world edge cases until full operational ownership.
Why us
Practitioners not researchers
Shipped to production
Used beats accurate
Consultants who have shipped AI into production and lived with the consequences. A 95%-accurate model your teams distrust is worth less than an 85% one they use every day.
No black box
Your maturity, not our dependency
Decisions documented, methodology explained, knowledge transferred. The goal is an organization that can lead its next AI initiative from within.
Honest about AI
We say no before you invest
If a use case is not viable, we tell you. If a simpler solution achieves the same result, we say so. Our reputation depends on outcomes, not billed projects.
Pricing
How AI consulting engagements are priced
A detailed timeline is given at proposal stage, with regular checkpoints throughout the engagement.
| Format | Typical scope | Duration | Price |
|---|---|---|---|
| Discovery and assessment | Data audit, stakeholder workshops and an AI opportunity assessment ranking use cases | 2 to 4 weeks | Fixed quote after diagnosis |
| Full implementation | Solution design, build and validation, deployment and handover | 3 to 9 months | Fixed quote after diagnosis |
| Support period | Fine-tuning against real-world edge cases after deployment | Defined at proposal | Fixed quote after diagnosis |
| Monitoring retainer | Model monitoring and iteration once in production | Monthly | Monthly fee, quoted upfront |
What changes the price: data readiness, number of use cases, integration depth and how much of the monitoring your team takes over.
Client outcomes
Measured results from real engagements
-38%
Manual review · Insurance
A claims fraud detection model that cut manual review workload by 38% while increasing fraud recovery, delivered ahead of schedule.
-21%
Unplanned downtime · Manufacturing
Predictive maintenance with 72-hour failure lead time across three production lines, since expanded to all twelve.
45min → 3min
Document processing · Finance
An OCR + NLP pipeline for unstructured loan documents that paid for itself within the first quarter of operation.
Reviewed by
Julien Mauclair
Co-founder, technical delivery · Stralya
Last updated:
FAQ
AI consulting FAQ
Straight answers to the questions we hear before every AI engagement.
How long does a typical engagement last?
Discovery and assessment: two to four weeks. A full implementation, from solution design through deployment: three to nine months, with a detailed timeline at proposal stage and regular checkpoints throughout.
What data do we need before engaging?
Less than you might think. The discovery phase includes a data audit that shows what can be built now, what needs preparation and what should not be attempted. Many first use cases start from data you already collect in your operational systems.
Can you work with our internal IT team?
Yes. Our practitioners work alongside your IT team throughout the engagement, from solution design to deployment. Decisions are documented, the methodology is explained and your team is trained before handover, so it can operate and extend the models on its own.
How do you handle data security?
Data stays in your environment whenever possible, with least-privilege access granted for the engagement only. Personal data is processed in line with GDPR and your sector rules, and we document which data each model uses, where it is stored and who can access it.
What if AI is not the right solution?
Then we tell you before you invest. If a use case is not viable, or a simpler rules-based or analytics solution achieves the same result, we say so in the assessment. Our reputation depends on outcomes, not on billed projects.
Related guides
AI guides
The guides that will support each AI service page, planned for the next publishing waves.
AI strategy
Ranking AI use cases by ROI, feasibility and time to value
Planned guide · coming soon
Machine learning
Is your data ready for machine learning?
Planned guide · coming soon
Predictive models
Predictive models: backtesting before you deploy
Planned guide · coming soon
No AI guide is published yet. Each planned guide will link back to its service page.
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