Question 1

How much does an enterprise AI project cost?

Accepted Answer

Enterprise AI projects at Scalexa typically range from $25K for a proof of concept to $250K+ for a full production deployment, billed weekly on actual hours worked rather than fixed-price fictions. Before any build, we run a paid Architecture Assessment ($2K Starter / $5K Strategic / $15K+ Enterprise) so cost projections are grounded in your real stack, not a sales pitch. The biggest cost drivers are data readiness, model evaluation rigor, and production deployment — not the model itself.

Question 2

Should I hire AI engineers in-house or use an AI consultancy?

Accepted Answer

Most mid-market companies should start with a senior-only consultancy and only hire in-house once the system is generating measurable revenue or cost savings. Reasons: (1) the AI talent market is brutally competitive — a strong senior ML engineer costs $250K+ all-in and takes 6+ months to hire, (2) early-stage AI work is bursty and benefits from a multi-disciplinary team (data eng + ML + platform + security), (3) 73% of enterprise AI projects fail, and most failures are platform/data problems an in-house ML hire cannot fix alone. Scalexa runs senior-only — every engineer has 10+ years of experience — so you get the multi-disciplinary depth without the hiring risk.

Question 3

What is fine-tuning, and when does it make sense for an enterprise LLM?

Accepted Answer

Fine-tuning means taking a base model (GPT-4, Llama, Claude) and continuing its training on your proprietary data so it speaks your domain. It makes sense when (1) retrieval-augmented generation (RAG) is not enough because your domain language is too far from the base model's training data, (2) you need consistent output formats the base model gets wrong, or (3) you have a high-volume, low-margin use case where a smaller fine-tuned model is cheaper than API calls. For 80% of enterprise use cases, RAG plus prompt engineering outperforms fine-tuning at a tenth of the cost.

Question 4

How do you track data lineage for ML training jobs running as Kubernetes pods?

Accepted Answer

We instrument every training job with an MLOps tracker (MLflow, Weights & Biases, or Comet) that captures the exact dataset version, code git SHA, container image hash, hyperparameters, and resulting model artifact. The Kubernetes pod spec is committed to the same Git repo as the training code, and pipeline runs are orchestrated by Airflow or Argo Workflows so every job has an immutable lineage trail. For regulated industries we add a hash-chain over the dataset manifest to make tampering provable.

Question 5

What's the best CI/CD for machine learning teams?

Accepted Answer

There is no single best — the right answer depends on whether your bottleneck is data, models, or deployment. For data-heavy teams, use GitHub Actions or GitLab CI for code plus DVC or LakeFS for data versioning. For model-heavy teams, layer in MLflow or Weights & Biases for experiment tracking, plus a model registry. For deployment-heavy teams, use Argo or Tekton with progressive rollout via Seldon, BentoML, or KServe. Scalexa typically deploys a layered stack: GitHub Actions for code, MLflow for models, Argo Workflows for pipelines, and ArgoCD for production rollouts.

Question 6

How long does it take to deploy a production AI model?

Accepted Answer

Realistic timelines: 2-4 weeks for a proof of concept, 8-12 weeks for a production MVP with monitoring, and 4-6 months for a fully governed enterprise deployment with model risk controls and observability. The single biggest delay is always data — clean, well-labeled, accessible training data takes longer than the modeling itself in 90% of projects.

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