AI / ML solutions
Machine learning that runs on your data, in your systems.
We build forecasting, classification and document-extraction models in Python and deploy them behind APIs your .NET and web applications can call. Each solution starts with an AI readiness assessment so you know what is worth building.
- Readiness assessment
- $2,500
- Proof of concept
- 3–4 weeks
- Stack
- Python
What we deliver
What our AI / ML solutions team delivers
AI readiness assessment
A one-to-two-week review of data quality, candidate use cases, risks and expected return.
Document extraction
Pull fields from invoices, statements and forms into structured records with confidence scores.
Forecasting and scoring
Demand, churn and risk models trained on your historical data and retrained on a schedule.
Classification and routing
Triage tickets, emails and records to the right team automatically.
MLOps
Versioned models, monitoring for drift and simple rollback.
Technology
- Python
- scikit-learn
- PyTorch
- TensorFlow
- Hugging Face
- pandas
- FastAPI
- Docker
- Azure ML
How we ship AI
Five gates before any agent reaches production.
- 1
Assess
Map data, risks and the workflow worth automating. One to two weeks.
- 2
Build
Agent, tools and retrieval wired to your SQL Server, Oracle or Postgres and APIs.
- 3
Senior review
An engineer with production experience reads every prompt, tool and permission.
- 4
Pre-deploy test
Evaluation sets, red-team prompts and cost ceilings before go-live.
- 5
Observe
Tracing, token cost and policy alerts in production, with a full audit trail.
Related work
Proof from a real engagement
See how we delivered similar work, or browse all case studies.
Merit Wealth · Sydney, Australia
two years building financial and tax systems.
One senior engineer spent two years building financial and tax systems for Merit Wealth, a Sydney licensee that supports accounting and financial advice firms.
Read case study →FAQ
Common questions
Where do we start with AI?
With a $2,500 readiness assessment that ranks use cases by value and feasibility, so the first build pays back.
Do we need a data science team?
No. Our engineers handle data preparation, modelling and deployment, and document everything for your team to own.
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