Outcomes you get
- A model or generative-AI service running behind a versioned API
- Documented evaluation harness so quality regressions are caught before release
- Deployment and rollback procedure your own team can operate
Solution
AI Engineering
Design, build and deploy production machine learning, generative AI and computer vision systems.
Conceptual visualization only
The problem we solve
Promising models stall in notebooks because there is no path to evaluation, deployment or monitoring.
What we deliver
- Architecture decision record and data-flow diagram
- Training or fine-tuning pipeline with reproducible configuration
- Inference API with authentication, rate limits and structured logging
- Evaluation report covering accuracy, latency and failure modes
- Monitoring dashboards and alerting rules
A good fit if
- Teams with data but no production ML path
- Products needing retrieval-augmented generation over private content
- Existing models that need reliability, evaluation and observability
How This AI Agent Works
The published process for this agent: what triggers it, what it validates, which approved tools it calls, where a human approves and what it delivers.
AI engineering workflow
From a requirement to a deployed, monitored model.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
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Workflow summary as a text list
- 1. Requirement: Trigger. Business objective, target metric and constraints.
- 2. Dataset and model assessment: Validation. Check data sufficiency, leakage and licence terms.
- 3. Experiment: Approved tool. Train candidate models and log every run.
- 4. Evaluation: AI agent. Compare candidates on the agreed metrics and slices.
- 5. Security check: Validation. Review data handling, secrets and model exposure.
- 6. Deployment approval: Human approval. An owner approves before the model is promoted. Human approval required.
- 7. Model deployment: Action. Ship the approved model behind a versioned endpoint.
- 8. Monitoring: Monitoring. Track drift, latency, cost and error rates.
- 9. Model package: Output. Model card, evaluation report and deployment record.
Approval gate: Deployment approval. Deliverable: Report.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| machine-learning-ai-engineering | 9 | Yes. Policy: always | Report | Available | Available (signed in) | Available via Studio · requires ai_provider | Not yet covered |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Capabilities
Delivered as consulting, build, automation or managed service.
Machine learning systems
Deep learning
Generative AI
Computer vision
Natural language processing
Speech AI
Forecasting
Recommendation systems
Predictive maintenance
Model fine-tuning
Model deployment
MLOps and monitoring
AI API development
Retrieval-Augmented Generation
Vector search
AI application development
How an engagement runs
Phase 1 · 1–2 weeks
Discover
Data review, feasibility check, success metrics agreed.
Phase 2 · 1–2 weeks
Design
Architecture, evaluation plan, integration contracts.
Phase 3 · 4–10 weeks
Build
Pipelines, model work, API, tests and evaluation runs.
Phase 4 · Ongoing
Deploy & improve
Release, monitoring, retraining cadence, handover.
Common questions
Do you build custom models or use existing ones?
Both. We start from hosted foundation models where they meet the accuracy and cost target, and train or fine-tune custom models when the domain requires it.
Who owns the code and models?
You do. Everything is delivered into your repositories and cloud accounts with documentation.
Ready to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.