MLOps prospects come in at wildly different maturity levels. Some have one data scientist running a Jupyter notebook in prod and want a 'platform.' Others have a dozen models in production with deployment pipelines and just need observability. A few have a hundred models and want feature store consolidation. The discovery call spends the first thirty minutes excavating where they actually sit on the maturity curve.

A SleekAI chatbot scoped to your consultancy site does that excavation in chat. It asks about model count in production, current deployment process (manual, scripts, Airflow, Kubeflow, SageMaker, Vertex), training compute (on-prem, AWS, GCP, Azure), and the specific painful symptom (deployment fragility, no monitoring, no reproducibility, feature drift). From the answers it maps to your services (deployment pipeline build at $58k for 10 weeks, feature store implementation at $72k for 12 weeks, observability framework at $34k for 6 weeks, ML platform foundation at $120k for 16 weeks).

Case studies are pulled from your case-study CPT filtered by maturity stage and platform. Prospects too early for an MLOps consultancy (no models in prod yet, no data science team) get redirected to a partner or sent home.

MLOps is a field with a steep and confusing maturity curve. A Series B SaaS with eight models in production needs entirely different work than a public-stage enterprise with two hundred models across a dozen teams. Most prospects can't articulate where they sit on the curve because they don't know what the curve looks like.

They ask for 'an ML platform' and partners spend the first half-hour of every call extracting what they have, what they need, and where they're failing today. A scoped SleekAI chatbot does that placement in chat. It asks the maturity-locating questions, places the prospect at a tier, and routes to the corresponding starting scope (deployment pipeline, feature store, observability, full platform foundation).

Prospects arrive at the booked call already understanding their tier, having seen the matching case study, and with a realistic price band in mind. Partners then focus on the harder questions: team ownership, runbook practices, post-engagement operating model. SleekAI runs inside your WordPress install with your codified maturity framework as the bot's instruction.

Your case study CPT and services postmeta feed the bot through mapped variables, so the bot stays in sync with the rest of your site automatically. Disqualified prospects (zero production models, no data science team, or pure analytics needs) get redirected to a data engineering partner so your funnel stays focused on actual MLOps work. Conversations log with model, tokens, and origin page so partners review by maturity tier quarterly and refine the routing logic as the field evolves.

The compounding effect over a year is that architecture calls shorten, win rate climbs on booked engagements, and the bot becomes the consultancy's institutional maturity-diagnosis voice, available continuously.