SerSan · AI Features & Reliability
AI Features & ReliabilityMake AI features reliable enough to depend on.
The AI feature works sometimes, but nobody can trust it yet. We add evaluation, monitoring, deployment and rollback to copilots, RAG systems, agents and production models.
Review, hardening sprint or ongoing · fixed scope
What goes wrong with AI in production
An AI feature your team can't trust is a feature your team quietly stops using.
It works in the demo. In production it's right most of the time, wrong occasionally, and nobody can say which. Costs drift, latency spikes on the worst possible day, and no test would have caught it. The fix is rarely a better model — it's measurement, and being able to roll back in a minute.
Typical build includes
What it takes before an AI feature is safe to depend on.
Which of these you need depends on what the feature decides and who it can affect. We agree the mix first, and leave out the parts the risk doesn't justify.
- 01
Proof it still works, before every release
A written definition of what 'good' means for this feature, turned into test cases anyone can re-run. Drift metrics on production inputs, safety checks on sensitive outputs, run in CI on every change.
- 02
Ship a new version, undo it in one command
Versioned model artefacts in a registry. Promotion from staging → canary → production gated by eval pass. Rollback to any prior version in one command.
- 03
Monitoring: latency, cost, accuracy
You see the cost and the slowdown before a customer does. Per-model dashboards on speed (p50/p95/p99), cost per prediction and accuracy where it's measurable, with threshold alerting.
- 04
New versions proven on real traffic first
New models run in shadow against production traffic first. Then a single-digit-percent canary. Then full rollout. Each gate has a kill switch.
- 05
Retraining triggers and ownership
When drift exceeds threshold, the pipeline opens a retraining ticket, not an outage. A named owner approves the retraining run and the redeployment — someone on your side, or us under a support arrangement.
Where this lands
Three ways an AI feature loses a team's trust.
01 · Use case
An AI feature that works inconsistently
It's right most of the time and wrong often enough that nobody relies on it. We define what good looks like, build the eval set that proves it, and harden the feature until the team trusts the output.
02 · Use case
AI cost or latency is unpredictable
The invoice moves without explanation and the slowest requests are the ones customers notice. We instrument cost per call and latency per step, set budgets and fallbacks, and make the trade-offs visible.
03 · Use case
Production ML that needs governance
Auditable pipelines, traceable training data, model cards, and the documentation a regulator actually wants to see. The compliance work is design, not paperwork bolted on at the end.
Deliverables
What you actually hold at the end.
Concrete artefacts, handed over at the end. The eval suite, the pipelines and the dashboards that make the feature measurable — readable by your team without us.
- Eval suite with documented test cases
- Model registry + promotion pipeline
- Monitoring dashboards + alerting rules
- Shadow / canary / rollback infrastructure
- Retraining playbook + named-owner handover
- Optional: 60-day period where we operate it with you
Selected work
Projects behind this service.
5 projects that exemplify this service, from prior senior delivery before SerSan.
Common questions
Answers from real projects.
Do you work with our cloud / our ML platform?
Yes. We work with AWS SageMaker, Vertex AI, Databricks, Azure ML, and bare-metal setups. We don't push a particular platform. We work inside what you've already chosen.
What about LLM-as-a-service models vs trained-in-house?
Both are MLOps problems. Hosted LLMs need eval, monitoring, cost guardrails, and rollback to a previous prompt version. In-house models need all of that plus the training pipeline. Same engineering discipline, different surface area.
How do you handle evals when ground truth is hard to get?
Layered approach: synthetic test cases for the easy 60%, LLM-as-judge for the next 30%, human review queues for the last 10%. We measure inter-rater agreement on the human layer and treat the judge model as something that itself needs evals.
Can you take an existing model into production without retraining?
Usually yes. Often the job is exactly that: 'this model works, get it operable'. We wrap it with eval, monitoring, rollback and a deployment story, without touching the training code. We review it first and give you one verdict: build, harden or stop.