In high-compliance verticals, the AI value isn't in the model - it's in the deployment. Both healthcare and energy infrastructure require local hosting, signed audit trails, and per-query data lineage.
PILLAR 02 · ARCHITECTURE + MAINTENANCE
Bespoke AI
Workflows
Your competitors are pasting your data into ChatGPT and hoping. We build the local, private alternative - fine-tuned models on your hardware, retrieval over your real documents, MCP servers your operators can call from inside Slack. No public-API data leakage. No subscription that raises your prices next quarter.
WHY HEALTHCARE AND ENERGY CANNOT USE PUBLIC AI
The compliance reality public-API products won't talk about.
Every healthcare document contains PHI. Public LLM APIs require signing a BAA (most won't), accepting third-party retention policies, and trusting a provider you do not control. Most of our healthcare clients land here after their compliance team blocks the public-API procurement.
We deploy fine-tuned local models on Proxmox or Docker on hardware you own, with RAG over your real EHR and ops documents and per-query audit logging. Nothing leaves the network.
WE SHIP
Production AI workflows. Not chatbot demos.
ARTIFACT 01
Locally hosted fine-tuned LLM
Llama / Mistral family models quantized and fine-tuned to your domain. Deployed on Proxmox or Docker on hardware you own.
ARTIFACT 02
RAG pipeline over real data
Document ingestion, embedding, retrieval, re-ranking. Built against your actual corpus - EHR exports, compliance docs, historical tickets - not a demo PDF.
ARTIFACT 03
MCP server in your Slack
Custom Model Context Protocol server your operators can call as a Slack command. Workflow automation triggered from chat; results delivered in chat.
ARTIFACT 04
Audit + observability layer
Per-query logging, lineage tracking, evaluation harness. Your compliance team can prove what the model said and what it had access to, every time.
ENGAGEMENT SHAPE
Typical six-week pilot → production handoff.
WEEKS 1–2
Use-case audit + corpus mapping
We pick one operator workflow that's painful, valuable, and corpus-scoped. One.
WEEKS 3–4
Local deploy + RAG build
Model deployed on your hardware. RAG pipeline against your real corpus. First operator query answered.
WEEKS 5–6
MCP server + Slack integration
Workflow lives in your ops Slack. Evaluation harness running. Audit logs flowing.
WEEK 7+
Production handoff or expansion
Either your team takes it from here (we document and exit) or we expand to a second workflow. No retainer trap.
Our compliance team killed three public-AI procurement attempts before we found Reblend. They deployed locally on our hardware, RAG over our NERC docs, MCP in our ops Slack. Audit prep went from six hours to forty minutes.
An AI workflow is only as good as the data it can reach.
The two adjacent pillars build the data ground beneath this one - Predictive Pipeline cleans the warehouse, High-Performance Digital Engineering captures the events that feed it.
Scope a Bespoke AI Workflows pilot.
Typical first sprint - two weeks, fixed fee, ends with a "build, defer, or kill" recommendation. You own the assessment regardless of next steps.