AI Workload Engineering & Deployment

A model that works in a notebook is not a model that works in production. Serving stack, batching policy, quantization, retrieval pipeline and evaluation harness all decide whether your AI delivers consistent quality at acceptable cost. Novera Intelligence engineers the full workload path — from training runs to production endpoints.

We deploy LLMs and SLMs on infrastructure you control or in the cloud, optimize for your latency and throughput targets, and wrap every deployment in the MLOps and LLMOps discipline needed to keep it healthy.

The Challenges We Solve

• Serving latency and throughput gaps

  Endpoints that miss latency targets or collapse under concurrency. We tune serving stacks, batching, KV-cache and quantization against your real traffic profile.

• Training runs that stall

  Distributed training blocked by communication overhead, checkpointing and IO. We engineer data pipelines, parallelism strategy and fault tolerance that keep GPUs busy.

• RAG that doesn't retrieve

  Retrieval quality determines answer quality. We engineer chunking, embeddings, hybrid search and evaluation loops that make RAG measurably accurate.

• No evaluation discipline

  Model changes shipped without regression testing produce silent quality drift. We build evaluation harnesses and canary processes so changes are provable.

What We Deliver

• LLM & SLM deployment

  Selection and deployment of open and commercial models across vLLM-class serving stacks, with GPU sizing matched to your traffic.

• Model serving optimization

  Continuous batching, KV-cache management, quantization and speculative decoding strategies tuned to latency and throughput targets.

• Distributed training & fine-tuning

  Parallelism strategy (data, tensor, pipeline), data pipeline engineering, checkpointing and resumability for reliable large-scale training.

• RAG engineering

  Chunking strategy, embedding selection, hybrid retrieval, reranking and grounding with evaluation loops that measure answer quality.

• AI agents

  Agent architectures with tool orchestration, guardrails, state management and cost controls for enterprise automation.

• Quantization & compression

  Precision reduction, distillation and caching strategies that cut cost without breaching quality thresholds.

• MLOps & LLMOps

  Model registries, versioning, rollback, feature/data pipelines and drift monitoring integrated with your delivery practice.

• API integration

  Robust integration of models into business applications with auth, rate limiting, caching and degradation behavior.

• Evaluation & monitoring

  Automated evaluation suites, quality dashboards, online metrics and production troubleshooting playbooks.

Architecture in Layers

The production inference path

1. Model layer

   Model selection, fine-tuning, quantization and registry with lineage for every artifact.

2. Serving layer

   Optimized serving runtimes with batching, cache management and autoscaling to traffic.

3. Retrieval layer

   Vector and hybrid search, reranking and grounding pipelines for knowledge-intensive workloads.

4. Application layer

   APIs, agents and application integration with guardrails and graceful degradation.

5. Assurance layer

   Evaluation suites, quality monitoring, drift detection and rollback paths.

How We Engage

Delivered through a disciplined lifecycle that carries the initiative from assessment to dependable, optimized operation.

1. 01

   Workload definition

   Traffic profile, quality targets, latency budgets and cost envelope agreed with stakeholders.

2. 02

   Architecture & baseline

   Serving stack and pipeline design; baseline benchmarks establish the performance bar.

3. 03

   Build & tune

   Deployment, optimization iterations and evaluation against the baseline.

4. 04

   Production integration

   API integration, guardrails, monitoring and delivery pipelines.

5. 05

   Handover

   Documentation, evaluation suites and operational training for your team.

Deliverables You Can Expect

• Serving architecture and GPU sizing model
• Optimized model deployment with benchmarks
• RAG pipeline and retrieval evaluation report
• Evaluation harness and quality dashboards
• MLOps/LLMOps pipeline configuration
• Production runbooks

Use Cases

• Enterprise assistant

  A production assistant with private models, retrieval grounding, evaluation gates and predictable latency under enterprise traffic.

• Domain model adaptation

  Fine-tuning and deployment of models adapted to specialized domains, with evaluation that proves the adaptation is worth its cost.

• Agent automation platform

  Agentic workflows with tool integration, human oversight points and cost-per-task visibility.