SELBERG
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Capability · Step 5 of the Selberg model

AI, Machine Learning and Agents

Systems that read, answer and act, within your rules.

We design and operate AI that solves a defined business problem and keeps working in production. Not a model on a laptop, and not a chatbot: the complete environment, from the data it learns from to the monitoring that tells you it is still right. The objective is to move from AI that answers questions to AI that helps perform work.

Problems this solves
We want AI but aren't ready for it. Our pilot never made it into real work. We spend too much time on documents and routine decisions.
01

AI readiness

Most organisations want AI before their data is ready for it. We assess the whole foundation an enterprise AI programme needs, name the use cases that will create measurable value, and say what must exist before the first one is built. It is the fastest way to avoid a year of pilots that cannot scale.

What the assessment covers
  • Data availability, quality, architecture and integration maturity
  • Metadata, governance, security and privacy
  • Infrastructure, cloud architecture and model readiness
  • Knowledge management and operational workflows
  • Candidate use cases and their expected business value
02

Machine learning in production

Predictive models that become part of how the business runs: forecasting, classification, recommendation, anomaly detection, computer vision. We engineer the full loop, so the model is validated before it ships, monitored after, and retrained when the world changes.

The loop we build
Data → Features → Model → Validation → Deployment → Monitoring → Improvement
Feature engineering · model deployment and monitoring · MLOps · automated retraining · model evaluation and governance
03

Enterprise agents and intelligent automation

Governed AI systems that work with your information and take part in your processes: they read the documents, answer in plain language, use the systems they are allowed to use, handle the routine cases and bring the unusual ones to a person. The rules are yours. The judgement stays with your people.

What we build
  • Enterprise knowledge systems and retrieval over your own documents and records
  • Document intelligence and intelligent document processing
  • Conversational analytics and natural language to SQL
  • Operational, decision-support and customer-support agents, with tools and APIs
  • Workflow orchestration, multi-agent systems, human-in-the-loop approval
  • AI evaluation, observability, security and governance
Illustration
Invoice arrives → fields extracted → purchase order checked → discrepancy found → approval requested from a person → posted to the ERP. An example, not client data.
What changes
Ask your business a question in plain language.
And get an answer drawn from governed data, with the source beside it.
Documents processed automatically.
Contracts, invoices, claims and applications read and checked before a person sees them.
Routine decisions handled, exceptions escalated.
Your people spend their time on the cases that need them.
Reliable AI needs reliable data. If the foundation is not there yet, the assessment says so, and what to build first.
How we work Talk to an engineer
Next: Data Engineering and Governance →