Problem. Consulting for regulated clients — defense, healthcare, finance — is slow because each engagement is bespoke: a VPC, custom auth, client-specific data contracts, a deploy pipeline, audit logs, handover docs. Every new client = another org’s DNA expressed from scratch. Same genome, different cell, radically different organism (your words).

Build. A Prem Consulting harness: a Temporal (or Inngest) workflow that takes a client intake spec → provisions VPC → wires Encrypted API keys → stands up a Secure AI Workspace tenant → runs client-specific acceptance evals via Braintrust → writes the audit runbook. Every engagement compresses from weeks of bespoke to a parameterised pipeline with an observable run ID per client.

I've done this: I've shipped the pieces at SalesMonk already — durable workflow engine (100k+ actions/mo), multi-tenant agent + memory stack (mem0 + Temporal + RAG), account scoring pipeline on Inngest (100k+ accounts) — plus Ronin on GCP as a live proof-of-pattern outside work.

Sketch. v0 in two weeks: one client end-to-end through the pipeline. v1 in six: parameterise across 3 regulated verticals. The harness itself becomes a product surface that Suhan’s team can version, ship, and fork per client.

Problem. Prem Chat is “coming soon” on your homepage. Every general-purpose chat on private inference already exists; what doesn’t exist is one that remembers what its user owes — to whom, by when. Your obligation-economy essay named the gap: “A task is something you’ve decided to do. An obligation is something someone else expects you to do. Step one is where everything breaks.” That’s the feature Prem Chat should launch with.

Build. An obligation-graph memory layer underneath Prem Chat: ingest unstructured comms (email / Slack / Telegram / meeting transcripts) → classify intent (ask / promise / deadline) → write into a typed store keyed by (user × counterparty × deadline). Runs entirely on Prem’s stateless encrypted-memory substrate — the “data exists only in encrypted memory during inference” claim becomes a selling point because nothing else in the market can promise that with real memory. Proactive sweeps surface “things you owe today” before they miss.

I've done this: I ship this exact memory substrate at SalesMonk — client memory layer (mem0 + Firecrawl + LlamaIndex + Qdrant), RAG chat with Slack escalation (2M+ visitors/day), multi-tenant agent stack on Temporal.

Sketch. One-sprint prototype on a single Telegram + Gmail account: measure recall@5 on a hand-labelled week of “missed obligations”. If recall holds >0.75, generalise across channels and launch Prem Chat around it instead of another generic privacy-preserving chat.

Problem. Prem Studio ships fine-tuned models. Prem API serves them. The Consulting team drops both into a regulated-client VPC. Today those three stages live in three codebases — each engagement re-glues them. That’s where the 6-week integration comes from: stitching Studio’s output, API’s deployment, and the client’s compliance contract by hand, every time.

Build. One opinionated pipeline that turns client-spec.yaml into a shipped integration: Studio fine-tuning kicked off from a client dataset → MiniGuard guardrails auto-attached → Prem API deployed to the client’s VPC with dedicated GPU + zero-retention config → acceptance evals via Braintrust → signed audit runbook. Same Temporal-driven harness the Consulting-team proposal (01) needs, extended one layer up into Studio. Every run gets an observable ID support can replay.

I've done this: this is the surface I ship daily — Next.js + TypeScript + NestJS + Python at SalesMonk, LangGraph agents + Braintrust eval harness, Evolv Fit · cross-platform shipping discipline.

Sketch. Start with a single regulated client end-to-end. Measure: days from intake to deployed VPC, MiniGuard pass rate on client eval set, audit-runbook completeness. If the pipeline cuts integration time in half on the first client, it’s the Consulting team’s force multiplier for every engagement that follows.