Yungsten Tech · Boston

Take the prototype to production.

We rebuild MVPs from Lovable, v0, Base44, Supabase, and Render into platforms a team can run. Roughly half the cost of an agency, with an actual line of communication.

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Where we work

MVP to platform

Executive AI wiki

Agent and team enablement

AI curriculum, biotech and regulated

Comfortable withLovablev0Base44SupabaseRenderClaudeObsidian

What we do

Four ways we come in.

For founders with a prototype that has product traction and infrastructure debt, executives carrying dense context across companies, and teams that bought Claude last quarter but have not changed how they ship.

MVP to Platform

Take the prototype to production.

We rebuild AI-generated and prototype-first apps from Lovable, v0, Base44, Supabase, and Render into systems a team can run. We also wire the integration layer behind complex AI workflows: shared state, review gates, audit trails, and the glue between tools that were not designed together.

Typical outputs

  • Architecture and risk audit
  • Auth, data model, and deployment rebuild
  • Integration layer with shared state and review gates
  • Roadmap your team can run after we leave

Best for: Founders, operators, and technical teams whose AI work has outgrown its first build

Executive AI Wiki

A private second brain, set up in one session.

We sit down with a senior operator, investor, or advisor and stand up a Claude Desktop and Obsidian workspace tuned to their actual world: companies, people, decks, calls, decisions, confidentiality boundaries. By the end of the session, the wiki holds a real document and answers a real question.

Typical outputs

  • Claude Desktop and Obsidian workspace
  • Domain interview and living CLAUDE.md
  • Entity, source, and decision structure
  • First real document ingested live

Best for: Time-poor leaders carrying dense context across companies, deals, and decisions

Claude and Agent Enablement

Make Claude useful where the work happens.

We help engineering, ops, and research teams put Claude Code, custom agents, and skills into the repos, docs, meetings, and queues they already use. The deliverable is usage that compounds: agents the team understands, review habits that survive a quarter, and session forensics so AI spend stays tied to outcomes.

Typical outputs

  • Claude Code and Desktop setup
  • Custom agents and skills for repeated workflows
  • Repo hooks, review habits, delivery templates
  • Session forensics and adoption coaching

Best for: Teams that bought AI tools last quarter and have not yet changed how they ship

Executive AI Curriculum

AI judgment for regulated industries.

An applied fluency program for leadership cohorts. Three tracks: Board and C-Suite, Functional Leaders, Operating Leaders. The biotech and pharma curriculum covers vendor diligence, governance, 21 CFR Part 11, GxP, HIPAA, and where AI delivers real value versus board-level risk. Case-driven, co-led with industry-credible faculty.

Typical outputs

  • Track A — Board and C-Suite (12 hrs / 6 wks)
  • Track B — Functional Leaders (20 hrs / 8 wks)
  • Track C — Operating Leaders (30 hrs / 10 wks)
  • Roadmap capstone, vendor diligence template, governance proposal

Best for: Biotech, pharma, healthcare, and other regulated leadership groups that need shared language before they sponsor AI work

How we build

What we mean by “production.”

The checklist is boring by design. Before adding features, we make sure the system can be owned by someone besides the person who built the demo.

Access

Roles, tenants, secrets, and the places users are allowed to touch.

Data

Schema, migrations, imports, backups, and one source of truth.

Runtime

Repeatable deploys, useful logs, cost visibility, alerts, and rollback.

AI Work

Agent instructions, review gates, session replay, and traceable output.

Handoff

Runbooks, owner notes, and a plain list of what is shipped or stubbed.

Proof

Recent work.

Patterns we can speak to directly. Claims kept narrow on purpose.

DepthChartIQ

D1 basketball decision platform, soft-launch ready.

Stood up depthchartiq.ai from founder docs, a model artifact, and an invite-only scope. 73 MVP-tagged features shipped across five waves, three Drizzle migrations, three new internal packages, 150-plus vitest passes, and a clean validator gate. Auth, admin, cohort data, payments scaffolding, and per-feature evidence in production.

AlphaRose / RINAE.AI

Three rare-disease tools, one investor-demo pipeline.

Argus, Admiral, and Metamorph each have a sole developer and a different stack. We are wrapping before rewriting: a Next.js orchestration layer on Fly, shared state between tools, human review gates, and a four-phase plan that keeps Quin, Pablo, and Robert in the driver's seat.

Executive Wiki

A working second brain for a biotech operator.

Claude Desktop and Obsidian onboarding for an executive carrying portfolios across operating, advising, and investing. The wiki holds his companies, people, decks, and questions inside confidentiality boundaries he set. He left the session with a real document indexed and an answer Claude alone could not have produced.

Vers1ons, FlipSmart, TradeCanny

Patterns from teams shipping with AI daily.

How non-coders ship full-stack changes safely. How early-career developers move faster with guardrails instead of slower with reviews. How Claude Code, custom skills, and session forensics keep adoption from collapsing back into chat-window habits.

Team

Two operators.

Paul Mikulskis

Paul Mikulskis

Co-Founder · Systems and Engineering

Paul builds production systems at the edge of what AI tools can do. Background spans VMware, LogRocket, Flipside Crypto, and Vers1ons, where he is CTO. Strongest where a prototype needs architecture, context, reliability, and an honest delivery cadence.

Josh Lerman

Josh Lerman

Co-Founder · Business and Workflow

Josh brings the operator lens: how teams actually work, where time leaks, and which automations would matter if they were built. He turns scattered business pain into clear workflows, buyer language, and adoption plans.

Insights

Field notes.

InsightMay 2026

How Session Logs Unlock Token Efficiency, Adoption Measurement, and Real ROI from Enterprise AI

Token costs have dropped 280-fold in two years. Enterprise AI bills keep climbing anyway. Session logs are the instrument that turns spend, adoption, and governance from guesswork into a feedback loop.

Read more
Field Note

The MVP-to-Platform Checklist

The exact list we walk through when an AI-generated app has product traction but cannot yet take real users: data model, auth, deploys, observability, and ownership.

Coming soon
Curriculum

AI Judgment for Biotech and Pharma Leaders

Why off-the-shelf executive AI courses fail biotech and pharma audiences, and what changes when 21 CFR Part 11, GxP, HIPAA, and IP exposure are on the table.

Coming soon

Bring the prototype.

30 minutes, no slides. Show us the MVP, workflow, wiki, or AI rollout that is almost working. We will tell you what we would build first.

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