A polished tour of somebody else's data.
rehearse before you run
The Digital Twin Lab · powered by DemoForgiBusiness runs live.
Every decision is take one.
Aviation rehearses. Surgery rehearses on twins. F1 runs thousands of virtual laps. The enterprise still runs take one. The Digital Twin Lab closes that gap — a synthetic twin of your enterprise where all 34 SemAi products prove themselves before production.
01 Forge the world
02 Run it forward
03 Decide with evidence
A live simulated world feed: business events, agent actions, scenario results and governance gates streaming from a running synthetic semiconductor enterprise.
meridian-semiconductor (OSAT) · live world
1929 · Aviation
The Link Trainer — pilots rehearse before they fly.
Surgery
Rehearses on patient twins before the first incision.
Formula 1
Thousands of virtual laps before Sunday's race.
2026 · The enterprise
Still runs take one — live, with real orders on the line.
The validation gap
Why most enterprise AI dies in the pilot — and the Lab that fixes it
MIT NANDA (Aug 2025): ~95% of enterprise GenAI pilots show no measurable P&L impact. The missing step is validation. The Lab supplies it — every product demonstrated, calibrated and certified on a synthetic twin of your enterprise first.
GenAI pilots with no P&L impact
0%
MIT NANDA, Aug 2025
Cost of an error caught in operations
29–1500×
vs at design — industry error-cost escalation
Your data needed for demos & workshops
0rows
real data enters at a paid pilot only
Events beating through a running twin
0k/min
12 months of coherent, reconciling history
five ways enterprise ai dies — tap a card
Six months, one plant — your real orders as the test bench.
No logbook, no exam, no flight hours.
No counterfactual baseline, no attribution a CFO signs.
Your data demanded just to see a demo. The Lab needs zero rows.
the rehearsal engine
Forge the world. Run it forward. Decide with evidence.
DemoForgi builds a living, decision-grade replica of your enterprise — sites, suppliers, orders, tools, money — and lets governed AI agents work inside it. Everything that happens becomes decision evidence.
products run unmodified The thing demoed is the thing shipped — what you evaluate is what you deploy.
- Five layers: master data, calibrated behaviour, 12 months of reconciling history, the paperwork to match — plus dormant risks for agents to find.
- Built from a semiconductor blueprint — warm before the first meeting.
- Zero rows of your data. Nothing to leak or mask.
- Pause it. Warp a quarter into minutes — a six-week pilot in one afternoon.
- Inject the failures you fear: allocation crunches, supplier fabs down, export rules flipping overnight.
- Deterministic replay — same seed, same story.
- Fork the world — "git for worlds" — and watch both futures run.
- Value = KPI(what you did) − KPI(what you almost did) — signed, versioned.
- Every simulated figure stamped SIMULATED. Evidence a CFO can audit.
why fabs need this
The industry with the least room for a wrong write
The most sensitive IP, the most expensive tools, the least rehearsal space of any industry — exactly the combination the Lab was built for.
Demos, discovery and workshops run entirely on a synthetic twin. Real data enters only at a paid pilot — scoped, under NDA/DPA, with a deletion-on-exit certificate.
0rows of your data before a paid pilot
A mistake that reaches operations costs 29–1500× what it cost at design. Rehearse the wrong write in a twin — don't absorb it live, next to $100M tools.
29–1500×error-cost escalation in operations
One substrate notice moves a lead time from 14 to 22 weeks and puts $410k of expedite exposure on the table. In the Lab, that Tuesday happens safely — as often as you need.
14→22 wkone simulated supplier notice
The scenarios you watch become the pilot's signed success criteria — baseline signed week 0, value measured weekly against the do-nothing branch. Pilots convert on evidence, not enthusiasm.
Week 0counterfactual baseline, signed
Digital-twin demos
Thirty-four products. One living twin. Watch them work.
Pick any product. An alert fires, the agent reasons in the open, a cited proposal lands, a named human approves — and the KPIs heal. Seven seconds of sim-time, replayable forever.
⏸ Pause it
⏩ Warp it — a six-week pilot in one afternoon
⑂ Fork it — git for worlds
⟲ Replay it — same seed, same story
choose a twin · 34 products · 7 suites
flagship demo
Yield AI
Meridian Semiconductor (OSAT) · Client Twin
Simulated
These are the same products on the platform — nothing demo-only. See them run full industry workflows →
How the platform runs
The platform runs itself. People decide.
Fourteen residents run DemoForgi around the clock. They never call in sick, they log everything they touch — and they know exactly where their authority ends.
Conductor
plans the night; never sleeps
World-Keeper
waters the worlds daily
Fabricator
forges 12 months of history before lunch
Scenario-Smith
invents bad days for a living
Personas
your toughest users, minus the coffee
Adversary
paid to break in; tips filed as tests
Judge panel
three votes. zero mercy.
Registrar
grants diplomas — revokes them too
Triage
sorts trouble into tidy piles
Mechanic
won't fix what it can't replay
Stitcher
keeps product seams from splitting
Analyst
writes the morning story, with receipts
Concierge
always on duty. always cites.
Steward
guards the wallet; sweeps idle worlds
six moments always require a human yes
G1Publish to a customer world
G2Promote an agent's autonomy
G3Twin data in / out
G4Catalog release
G5Anything commercial
G6Merge an agent's code fix
Every action signed with the ⬡ provenance mark; every agent on a budget with a kill switch. Bulk-approve deliberately does not exist. How gates work platform-wide →
Governed autonomy
Agents go to school before they meet your business
Inside the Lab, agents train on forked worlds, sit blind exams before a three-judge panel, and earn scoped diplomas. Autonomy is earned in simulation, granted by a human — and the Registrar can take it back.
01 · curriculum forked worlds — bad days included
02 · blind exam scenarios it has never seen · three judges
03 · diploma scoped autonomy, granted by a human at G2
04 · drift patrol scores tracked forever — the Registrar revokes
Shadow
Watches and scores itself against real outcomes. Touches nothing.
Propose
Drafts cited recommendations. Humans decide everything.
Act with approval
Executes only what a named human approves, gate by gate.
Bounded autonomy
Acts within earned, explicit bounds — for workflows that passed the exams.
Sustained score regression auto-proposes demotion. Failures on forked worlds cost nothing and teach everything.
Value first. Data last.
From demo to gated autonomy — one auditable ladder
You never hand over data to find out whether something works. The ladder climbs on evidence; your data enters exactly one rung, and you know which.
Step 1 · Demo
A live world in your industry
Disruption, response, money — in the real product UIs, 45 minutes.
your data: none
Step 2 · Golden scenarios
The demo becomes the contract
The scenarios you watched become signed success criteria, kept green by nightly runs.
your data: none
Step 3 · Shadow mode
Agents watch your real decisions, silently
L0 inside a paid pilot: calibration measured, not assumed. Counterfactual baseline signed week 0.
your data enters here — NDA/DPA, deletion-on-exit certificate
Step 4 · Assisted operations
Propose and approve, decision by decision
L1→L2: cited proposals, a named human approves every write. Weekly counterfactual ledger, signed.
yours, in your twin
Step 5 · Gated autonomy
Earned bounds, revocable in one click
L3 for workflows that passed the blind exams. Drift patrol never sleeps; regression auto-proposes demotion.
autonomy: earned, never defaultWhy CEOs sign this ladder
Every rung leaves an artifact a board can audit: golden-scenario passes, a calibration report, a signed week-0 baseline, a weekly counterfactual ledger. Adoption becomes a sequence of receipts.
And the two riskiest phases — evaluation and negotiation — happen while your data is still entirely yours.
Our partnership model →
Determinism & trust
Engineered honest. Attacked weekly.
Honesty rules are enforced in code — and a resident adversary attacks the platform every week. Findings become permanent regressions.
- SIMULATED, always labelledevery synthetic figure wears the tag
- Branches, never factscounterfactuals render as the road not taken
- No unlabelled AI outputevery artifact carries the ⬡ provenance mark
- Same seed, same storydeterministic replay, reproducible to the event
- Products run unmodifiedwhat you evaluate is what you deploy
- Calibration disclosedlead times ±1 wk · PPV ±0.6% · demand link ±3% — amber where weaker
Platform services live
0
verified build, Jul 2026
Green tests
~0
38 on the world kernel alone
Code promoted from production
0%
inherited from production code
Median feedback closure
≤5days
every item arrives with its reproduction attached
Blocker-severity feedback pages a human mid-session. Fixes are proven on a forked world against your original replay — and only you can close the item.