Models applied to real operations.
Machine learning, natural language processing and computer vision applied where they change the numbers — predictive analytics through to intelligent automation.
Overview
A model that performs well in a notebook has proved almost nothing. The distance between that and a system your business runs on is measured in data pipelines, monitoring, retraining, latency budgets and the unglamorous question of what happens when the model is confidently wrong. Most AI projects fail in that gap rather than in the modelling.
We work backwards from a decision someone has to make. What is the decision, what would improve it, and what does being wrong cost? That determines whether the answer is a large model, a small one, or a well-tuned rule that costs nothing to run — and we will tell you when it is the last of those, because it often is.
The things you actually receive.
Concrete deliverables rather than adjectives — this is the work, itemised.
Predictive models
Forecasting, scoring, classification and anomaly detection built against your data and your accuracy requirements.
Natural language
Extraction, classification, summarisation and search over documents, tickets, transcripts and correspondence.
Computer vision
Inspection, recognition and measurement from images or video, including on constrained edge hardware.
Data pipelines
The feature engineering, storage and refresh that a model needs in production — usually most of the actual work.
Deployment & serving
Inference infrastructure with the latency, throughput and cost profile your use case can actually sustain.
Monitoring & retraining
Drift detection and retraining pipelines, because a model degrades quietly from the day it is deployed.
Where this discipline goes wrong.
Four decisions that separate work which lasts from work that has to be redone.
Start from the decision
We identify the decision the model is meant to improve and what a better one is worth. Projects that begin from the technology rather than the decision produce impressive demos and no measurable change.
Establish the honest baseline
Before any model, we measure how well the current process performs. Without that number nobody can say whether the model helped, and a surprising share of AI initiatives never establish it.
Prefer the smallest thing that works
Simple models are cheaper, faster, easier to explain and far easier to debug at three in the morning. We escalate complexity only when the simpler approach has demonstrably fallen short.
Plan for being wrong
Confidence thresholds, human review for low-confidence cases, and a fallback when the model is unavailable. A system with no answer for its own failure modes is not production-ready.
How this one is usually run.
The same engineering practice applies at every size. What changes is the shape of the team.
Focused Build
One application or integration, defined scope and price.
Product Team
A full delivery team owning the product end to end.
Enterprise & Government
Multi-team delivery against audit and procurement requirements.
A proof of value in 4 to 8 weeks; a production model with pipelines typically 3 to 6 months.
Typically built with
- Python
- PyTorch
- scikit-learn
- Pandas
- Vector search
- MLflow
- AWS SageMaker
- Docker
The ones worth asking.
Including the answers that lose us work — those are the ones worth publishing.
Do we have enough data?
How accurate will it be?
Should we build or buy?
Can you explain the model's decisions?
What happens when the model degrades?
Does our data get sent to third parties?
Often paired with.
Most engagements draw on more than one capability. These are the usual neighbours.
Let's talk about artificial intelligence.
Tell us what you're trying to ship. We'll tell you honestly whether we're the right team for it — and what it would take.
Prefer email? info@triyant.sg
We reply within one business day. Your details are used only to respond to this enquiry and are never shared with third parties.