FOUNDING SEAT
Technical Co-Founder
About Voltaire
Intelligence is becoming abundant. Companies still haven't converted it into meaningful gains in productivity and revenue.
That’s largely because the decisions that make or break a company — which outcomes to pursue and in what order — are still made by instinct and analogy.
We’re building Voltaire to become an engine that can run a company: it decides which outcomes the whole company should own, directs people and agents toward them, and learns from every result.
In its end state, the company runs itself. Agents do the work; Voltaire holds the intent and the record of what worked; humans own the company.
The companies of the future will run on an AGI-powered engine.
The race to build it is a race to solve a handful of open problems, and Voltaire intends to lead it.
We’re currently engaged in discussions with Tier 1 VCs for a $10M+ pre-seed round.
The research frontier
The decision layer - what a company should own - does not exist yet.
Building it means solving seven open problems.
- 01
Outcome discovery
From the company's state, its history and its market, generate the outcomes worth owning, each with a target, a deadline, its evidence, its value, a confidence level. Detectors yield anomalies and candidate moves; an outcome is a target over a horizon, composed from those signals and from what the company wants. Whatever ranks the candidates, the top estimate tends to be the most overestimated one: the regressional form of Goodhart's law.
- 02
The objective
What the company maximizes, over which horizon, at what tolerance for risk, under which constraints, and whose judgment prevails when its people disagree. This must be inferred from the data and from what people adopt, defer and reject. Written policy alone already defeats frontier models, which passed 25 percent of tau-Knowledge tasks at the first attempt in March 2026; unwritten objectives are harder still.
- 03
Prediction
A model of the company that can be run forward under any move and return a distribution of outcomes: a driver tree whose elasticities are estimated from the company's own history, with priors drawn from comparable companies. The space of moves is combinatorial; by contrast, outcome data is scarce. A 200-person company resolves too few outcomes a year to fit a rich model, and no public benchmark measures the effect of one action on one company's figures.
- 04
The portfolio
The system must arbitrate between outcomes and decide how much to explore against how much to exploit. A portfolio of overlapping actions confounds itself, making allocation and measurement a single problem. No system yet arbitrates above the level of a single outcome.
- 05
Calibration
A figure attached to a decision means something only if its event is defined, if the figure is recorded at approval before the action runs, and if it is scored when the outcome lands. The live odds keep moving as data and other moves change them; the record of what was predicted does not.
- 06
Attribution
Separating what Voltaire caused from what the team would have done regardless: a company with 2,300 customers and 3 percent annual churn cannot prove a 25 percent reduction in churn: at 80 percent power, the test requires about 6,900 customers per arm. The outcomes not chosen have no control group at all, unless what was declined is kept as a set of counterfactuals and the selection itself is graded on regret.
- 07
Reflexivity
An engine that acts on a company alters the data it then uses to decide; an account that the team flags and then saves reads as a false alarm. Each outcome needs rules for when it is complete, when to retarget it and when to abandon it. Autonomy is earned per class of action, and adopting or retiring an outcome is a class of decision with no evidence base yet.
The foundations
Beneath the deciding layer sit four problems the field is working on now.
- 01
Simulation
Evaluating the engine on work that spans weeks or months requires a simulated company, with its people and its systems modelled from the company's own data and traces. A run can then be rewound, forked on a single change and played forward again, without the simulated world contradicting itself.
- 02
Memory
What the engine learns about a company must be stored, retrieved and compacted over years: its facts, its preferences, its exceptions and the decisions already taken, kept current as records land and as people change their minds.
- 03
Self-improvement
Loops that turn traces into verified changes to the engine's skills, memory, code and policies, judged by an evaluator the modifier cannot touch, so that an improvement in one place cannot degrade another.
- 04
Credit
Scoring and credit across trajectories that run for months, where several actions, several agents, and the team's own work touch the same figures. The profit and loss arrives too late, and too coarse, to serve as a training signal on its own.
The split
This is a founding seat, in person, in San Francisco.
The founder owns the market: positioning, design partners and investors. HEC Paris Grande Ecole, X-HEC Masters, four startups, one exit, four years at Logitech.
You own the system, and you own the architecture. The research agenda, the product, the raise and the order in which we take the market are decided together.
The bar
You have built a system that made decisions with money or customers at stake.
You have founded or run a business with its own profit and loss.
You treat a prediction as a claim with an event, a horizon, and a score, and you distrust a ranking until you know how you selected its winner.
You draw the line between what a model may draft and what code must decide, and you can defend where you drew it.
You write the code. The company model, the ledger, the gate: whatever you design, you can build alone before anyone joins you.
Strong signals: harness, post-training, or applied AI work at a lab or on an applied AI team; published research, especially on agent memory or continual learning.
Design choices already made
The model reads, drafts and plans; deterministic code estimates, ranks, gates and scores. Agents read and workflows write. Model output never touches a confidence, a sizing or a rank. The company's history is bitemporal, so that no prediction can see its own future. The engine is model-agnostic. The rest is yours to decide.
The deal
Co-founder seat. Double-digit equity on a standard four-year vest. A strong and opinionated voice in where the company goes. The seat is equity-first: no cash compensation until the funding round closes.
If you want to tell us how you’d tackle the seven open problems above, let’s talk.