SI that works in a demo and SI that holds up under production load are different systems. We build the infrastructure layer (serving, retrieval, eval pipelines, drift monitoring) that keeps your models dependable, cost-efficient, and audit-ready once real traffic arrives.

Get a production-readiness review Skipping MLOps

01 · What we build

Six MLOps pillars.

Most SI projects fail in production, not in development. We build the infrastructure that closes that gap: eval gates, drift alerts, and observability from the first deployment.

01 · Pillar

Vector Stores & Retrieval

Pinecone, Weaviate, Qdrant, and pgvector; we design the chunking strategy, embedding pipeline, and hybrid search configuration for your retrieval use-case.

02 · Pillar

Model Serving

vLLM, TGI, Triton, and managed endpoints. We set up autoscaling inference with P95 latency SLAs, GPU cost optimisation, and blue-green deploys.

03 · Pillar

Eval Pipelines

CI-integrated eval runs that score every model change before it ships. RAGAS, Deepeval, Braintrust, or custom harnesses run on every pull request.

04 · Pillar

Drift Monitoring

Statistical drift detection on model outputs and embeddings. Automated alerts when your SI system starts behaving differently than it did at launch.

05 · Pillar

Observability

Token cost tracking, latency profiling, prompt/response logging, and audit trails. Full observability across every LLM call in production.

06 · Pillar

Data Pipelines

ETL and streaming pipelines that keep your vector stores and fine-tuning datasets fresh. Airflow, Prefect, or custom Python pipelines on your infrastructure.

02 · The cost of skipping MLOps

What goes wrong without it.

Silent model drift
LLM outputs degrade gradually: no error thrown, no alert fired. Users leave before you notice. Drift monitoring catches this before it becomes a churn event.
No feedback loop
Without eval pipelines, every model update is a gamble. You can't know if the new version is better or worse on your actual production queries.
Runaway inference costs
Unmonitored LLM usage scales cost unpredictably. Token tracking and cost alerts keep bills predictable as usage grows.
Audit trail gaps
Regulated industries require proof of what your SI said, when, and why. Retroactively reconstructing that is expensive. Logging it from launch is cheap.

Proof, not promises

Twenty-plus SI systems shipped to production. One playbook, six industries, and a team that stays past launch.

SI systems live in production: 20+
Senior engineers & researchers: 21+
Upwork rating, 3 verified reviews: 5.0
Founded in Lahore: 2022

Verified Upwork clientSI product engagement · 5-star review

03 · Common questions

Before you brief us.

We already have a model in production. Can you help us add MLOps infrastructure?: Yes, this is our most common MLOps engagement. We start with an infrastructure audit, then add eval pipelines, drift monitoring, and observability without disrupting your existing deployment.
Which vector store should we use?: It depends on your scale, query patterns, and whether you need metadata filtering or hybrid search. We run a bake-off on your data before recommending. Pinecone for managed scale, pgvector for PostgreSQL shops, Qdrant for self-hosted flexibility.
How do we catch when our model starts giving worse answers?: We set up automated eval runs that compare a random sample of live inferences against your golden dataset. When accuracy drops below threshold, you get an alert before users start complaining.
Do you work with existing CI/CD pipelines?: Yes. We integrate eval runs into GitHub Actions, GitLab CI, or CircleCI. Model deploys are gated on eval pass; a regression blocks the deploy, not the user.

Available for new engagements

Ready to make your SI trustworthy?

30 minutes, one of our seniors, no slide deck. By the end of the call you will know whether we are the right team, and if not, who is.

Book a free infrastructure review Email info@octalcode.com

Or call +1 (512) 710-5701

Let's scope it.Instant answers · free project scoping