AI & Machine Learning Ops
Treat AI as Infrastructure, Not a Feature
A working demo and a system you can operate are not the same thing. The gap between them is filled with the unglamorous work: routing and rate limits, cost control, measurable quality, safety filtering, and the ability to see what a model actually did when something goes wrong. Singam Tech builds that layer — applying the same engineering discipline to AI workloads that we bring to cloud, Kubernetes and DevOps, so what you put in front of customers is supportable on a Monday morning.
What We Build and Operate
Agentic Workflows
Systems that iterate rather than answer once.
We design workloads that plan a task, take an action, check the result, and adjust — repeating until the work is genuinely finished. It is a markedly different engineering problem from a single prompt-and-response call, and it is where most real business value now sits.
Tool & Data Integration
One standard interface instead of bespoke glue.
Model Context Protocol lets a model reach your mailboxes, repositories, databases and internal services through a single consistent contract. We implement those integrations so capability can be added without rewriting the application each time.
Multi-Agent Architecture
Delegated work, isolated context.
Splitting a large problem across focused agents, each with its own tools and its own clean context, produces sharper reasoning than one over-loaded agent. We build the orchestration that dispatches the work and merges the results coherently.
Model Gateway
One control plane across every provider.
Authentication, rate limiting, routing and failover belong in one place, not scattered through your applications. A gateway also keeps you commercially free to move between providers — or run open models — as pricing and capability shift.
Inference Economics
Tokens are a line item on your cloud bill.
Model usage now behaves like any other consumption cost, and it is just as controllable. We instrument spend per feature, exploit caching where it pays, and right-size model selection so you are not paying premium rates for work a cheaper model handles well.
Evaluation Harnesses
You cannot ship what you cannot measure.
We build test suites that score model output against cases drawn from your actual domain, so a prompt or model change can be judged on evidence. Without this, teams are guessing whether a release improved anything.
Guardrails
Protecting users, data and reputation.
Filtering runs on the way in and on the way out — stripping sensitive data before it reaches a model, and catching unsafe or off-policy responses before they reach a customer. This is the layer that makes AI defensible to a risk committee.
Observability
You cannot fix what you cannot see.
Traces, logs and metrics across the whole agent workflow, surfaced on dashboards your engineers already use. When a system misbehaves, the question is what happened, where it broke, and why — and that has to be answerable in minutes.
The Reference Architecture Behind It
The capabilities above sit inside a layered platform architecture covering local and quantised foundation models, fine-tuning, retrieval grounding, agentic orchestration and continuous evaluation — including how it is delivered in FDA-regulated and HIPAA-covered environments on infrastructure you already run. Read the full write-up in Enterprise AI Platform Architecture.
How Engagements Start
Most clients come to us either with a promising prototype that will not survive production, or with a clear business problem and no settled view on how AI applies to it. We are comfortable starting from either end.
- AI readiness and use-case assessment
- Proof of concept through to production rollout
- MLOps pipelines on Azure Machine Learning
- Retrieval-augmented generation over your own content
- Model gateway and multi-provider strategy
- Evaluation, guardrail and red-team frameworks
- Cost modelling and inference spend optimisation
- Observability, tracing and incident response