Model choice, build vs buy, safety posture, pricing in a margin-compressing market, the moat conversation. A boardroom built for founders who watch the cost curve move every quarter.
Train, fine-tune, RAG, or call the API? The Engineer and CFO synths debate the cost curve, the latency trade-off, and the moat implications.
Is your moat the model, the data, the distribution, or the workflow? The Strategist and Futurist surface what survives the next foundation-model release.
The Ethicist surfaces the second-order risks; the Lawyer flags the liability. Critical for any AI product touching healthcare, finance, education, or hiring.
Token costs drop 70% a year. Your price can't. The CFO and Marketer synths work out the pricing strategy that survives the next compression cycle.
Real questions. Multiple expert perspectives. Every time.
“Should we train our own model or stay on top of frontier APIs for another year?”
“What's our moat when GPT-7 ships and undercuts our core capability?”
“How do we price a per-query product when token costs drop 70% a year?”
“Should we pursue an enterprise sale or stay on PLG before the API model commoditizes?”
“Is our safety posture ready for the EU AI Act enforcement window?”
“When do we hire our first head of trust & safety?”
Each expert thinks independently — they won’t just agree with each other.
The Strategist
Maps competitive dynamics and strategic options across multi-year horizons.
The Engineer
Translates ambition into what’s actually buildable, by when, with whom.
The Ethicist
Surfaces second-order consequences and integrity-cost trade-offs.
The CFO
Pressure-tests unit economics, runway, and capital allocation.
The Futurist
Pulls the 5- and 10-year scenarios most teams forget to model.
A synthesized recommendation from your team of experts — not just opinions, but structured analysis.
Moderate Agreement
Key Recommendations
Synthesized Recommendation
Don't train your own model. Concentrate the next 9 months on workflow lock-in and data flywheel. The compute spend buys you a feature; the workflow buys you the moat.
Full analysis continues with detailed reasoning, trade-offs, and next steps...
Watch Out For
Expert Opinions
“AI founders romanticize training because it feels like the real engineering work. The truth is the moat is almost never the model — it's the workflow, the data, the trust. A boardroom forces you to admit that before you light $5M of compute on fire.”
SynthBoard is itself an AI-native business. The synths reason about token costs, fine-tuning trade-offs, and the moat question because we live them too.
Most AI strategy tools treat ethics as a check-box. SynthBoard wires the Ethicist into the room by default — because the second-order consequences are where the regulation lands.
AI strategy decisions made today get reset by the next model release. The Futurist synth runs the scenarios most teams don't budget time for.
Save a session, re-run it next quarter when costs drop. Track the decision through the model cycle — not just the call you made.
The questions people ask before they sign up.
All three — the synth lineup shifts. Foundation-model founders lean heavily on the CFO and Investor (capital intensity). Applied AI founders lean on the Strategist, Engineer, and Customer (workflow lock-in). Infrastructure founders lean on the Engineer, Strategist, and CFO (margin dynamics).
It runs the moat argument in parallel. The Strategist will argue for workflow and data lock-in; the Skeptic will challenge whether the moat survives a frontier release. You get the trade-offs in the open — not a comfortable "yes you have a moat" answer.
It helps frame the strategic posture — when to invest in trust & safety, how to position around the EU AI Act, what to build before regulation requires it. It won't give legal opinions on specific compliance obligations; that's your counsel's domain.
The CFO synth reasons about pricing in the context of cost-curve trajectory. The boardroom will pressure-test whether your current price survives the next 12 months of token deflation — and surface the packaging changes that buy you time.
Yes — SynthBoard uses a multi-model architecture (Claude, GPT, Gemini, Perplexity) and routes per-synth for diverse perspectives. The "circular" critique is fair; the answer is that an opinionated, role-typed AI panel makes different recommendations than a single LLM. We use it ourselves to make SynthBoard's decisions.
Both. Pre-PMF AI founders use it for "what should we build" framing and pivot pressure-tests. Post-PMF teams use it for the moat conversation, the pricing strategy, and the safety/regulatory posture.
Adjacent decisions, audiences, and methods inside SynthBoard.
Adjacent industry — many AI companies become devtools companies.
ExploreThe underlying business model most AI companies eventually run.
ExploreTrain vs fine-tune vs API is the build-vs-buy decision in AI clothing.
ExplorePricing in a margin-compressing market deserves its own playbook.
ExploreThe technical leader's persistent boardroom.
ExploreThe core SynthBoard mechanic, explained.
Explore250 bonus credits at signup. 150 free every month. No card required.