Bring us an important decision. We design the intelligence process.
The decision is yours. The problem is the information around it: incomplete evidence, assumptions nobody has had time to challenge, hidden trade-offs, and experience your team does not have — or cannot spare — before the deadline.
- IQ256 designs the intelligence process for your decision, then runs it.
- Fusion AI, targeted research, independent human experts, and — only where you permit it — targeted context from your own team.
- You manage no profiles, no calls, no prompts, and no temporary consulting team.
- One founder-reviewed result, designed around the action you have to take next.
IQ256 is founder-led and preparing its initial pilots. Candid feedback, investment conversations, and discussions about suitable future pilots are all welcome — write directly to the founder.
Focused intelligence assembled around the question.
Uncertainty branches. The review follows whichever branch the evidence makes important, and converges on one report you can act on.
From an intuitive preference to explicit trade-offs
Most consequential decisions are not blocked by a lack of intelligence. They are blocked by trade-offs nobody has been able to make explicit yet.
Before review
- Option A feels roughly safer and more likely to work.
- Option B feels more ambitious and less certain.
The preference is intuitive. The trade-offs are fuzzy, and nobody can say what would have to be true for the other option to win.
After review
- Expected value
- A 1× (illustrative unit)B 2× (illustrative unit)
- Cost
- A 1×B 3×
- Execution risk
- A Lower — known workflowB Higher — new dependency
- Time to first evidence
- A One quarterB Three quarters
Plus the conditions that would flip the answer — for example a weekly-adoption threshold, or an integration Option B silently depends on.
The decision is still yours. The trade-offs are no longer hidden.
Three families of decisions we review
The initial focus is European B2B software and AI companies, typically 40 to 150 employees, with a consequential decision expected in the next 30 to 90 days.
This is a recommendation, not a restriction. Companies outside that profile and independent advisers are welcome; fit depends on the decision, and a request outside the initial focus may simply require longer expert sourcing.
Product & AI Decisions
- Should we build, buy, partner, or stop this AI capability?
- Is this AI feature commercially valuable or merely technically impressive?
- Which vendor, model, or platform path creates unacceptable lock-in?
Monetization & Market Decisions
- How should we price and package this AI capability?
- Which ICP or vertical deserves concentrated investment next year?
- Which positioning assumption is most vulnerable right now?
Scale & Execution Decisions
- Which technical debt must be addressed before the next expansion?
- Should this capability be built, outsourced, or acquired?
- How should new funding be allocated across product and go-to-market?
What exists now, and what is being prepared
IQ256 is early-stage and founder-led. What follows is the current state, stated plainly, so early conversations start from facts.
In use now
Fusion — multi-model AI workflows — is used internally in selected review workflows today.
In preparation
The operating model and the first detailed decision-review examples are being prepared for publication.
Founder-led delivery
Engagements run over email with founder-owned synthesis. Initial pilots are being prepared with a small number of companies.
Not live
Public self-service, subscriptions, a customer portal, and an API are not live. Contact is direct and human.
What usually happens instead
A consequential decision creates a sudden demand for focused intelligence. When that capability is missing internally — or simply too expensive to divert from other priorities — three things tend to happen. There is a fourth.
- 01
Delay
Postpone the decision until internal attention becomes available — and let the window move.
- 02
Overload
Pull leaders or specialists off deadlines, creating overtime and delivery conflict elsewhere.
- 03
Compromise
Run a quick, partially thorough investigation and commit with hidden uncertainty intact.
- 04
Flexible intelligence
Bring in exactly the intelligence this decision needs, for as long as it needs it, without adding permanent headcount or taking your best people off their work.
Flexible intelligence, sized to the decision — without permanent headcount and without spending your leaders' attention on research they should not have to do.
Not every important question deserves a permanent team. Not every capable team has time to investigate every important question. The relationship can persist; what gets assembled changes with each problem.
Decision-first, not talent-first
You are not buying access to a network, a subscription to a model, or a founder's opinion. You are buying a designed outcome: the highest-value intelligence process for the problem, within the constraints you approved.
The decision is decomposed first
The question is broken into subproblems before anything is commissioned. Each subproblem gets the source that can actually close it — AI, research, an independent human expert, or one precise question to you.
Marginal information, not expert count
An independent human expert is added only for the distinct information they may contribute. A planned lane that would repeat what is already known is cancelled before execution.
Independent work, then reconciliation
Human experts receive purpose-specific sanitized packets and form their view before seeing anyone else's. Disagreement is reported and located, not smoothed away.
Founder accountability, output by design
One person owns the synthesis, the reasoning, and the stated limits — and shapes the deliverable around the action you take next, not around a document template.
Expert quantity is not the value metric. Marginal information is.
Adding perspectives has a cost. Adding information has a value. IQ256 should be rewarded for the intelligence it avoids as much as the intelligence it adds.
When execution becomes cheaper, deciding what deserves execution becomes more valuable.
AI is making more ideas buildable, faster and at lower cost. The scarce resource moves from building to choosing. An Independent Decision Review is how you keep decision quality while increasing decision velocity.
New insight changes the next question
Below is an illustrative workflow — not a customer case — for the decision: “Should a B2B SaaS company build, buy, partner, or stop an AI assistant initiative?” The backbone is predictable; what happens inside it is not.
Illustrative orchestration
Decision context arrives
A B2B SaaS company is deciding whether to build, buy, partner, or stop an AI assistant initiative. Roadmap notes, a vendor quote, and a usage export arrive with the request. The CPO owns the decision.
Illustrative workflow constructed for explanation. It is not a customer engagement, and no client data is represented.
Product insight exposes infrastructure economics
A new subproblem opens, and cloud architecture and FinOps expertise is justified.
Only internal context can answer it
Ask one precise question of the decision owner, or of a team member you approved for that topic.
A planned research lane would repeat known information
Cancel it before execution. Intelligence avoided is worth as much as intelligence added.
Disagreement is factual
Verify it against evidence and close the subproblem with a resolved answer.
Disagreement is about risk tolerance
Preserve both positions, with the conditions under which each holds.
The most useful output changes
Redesign the deliverable — for example a short review plus one execution path per option.
Intelligence is allocated where it can add new information.
Complex machinery, simple interface
You do not manage experts, prompts, or workflows. Email is the interface, and the orchestration is our responsibility.
Fromceo@company.example
Toreview@iq256.com
SubjectAI pricing decision
“We are deciding whether to move from seat-based to usage-based pricing before Q1. Two of us disagree about what it does to enterprise renewals, and nobody here has priced a usage model before. The decision is mine and I need it settled in six weeks.”
- pricing-model.xlsx
- customer-research.pdf
- roadmap.pdf
Illustrative example. Company names and data are invented.
What comes back
Fit confirmed, or one question
A short reply either confirms a review fits, or asks the single question needed to judge it.
NDA before sensitive disclosure
Nothing confidential needs to move before the NDA and the sensitive-data map are agreed.
Scope, fixed price, boundaries, date
Agreed in writing before any independent human expert is commissioned.
Clarifications during the review are batched, specific, and asked only when the answer would materially change the analysis. Your time cost is measured in minutes, not days.
Sources chosen per subproblem, not per package
New evidence can open a subproblem, close one, justify an independent human expert, cancel a planned lane, or change the most useful form of the result.
Your own team is a possible source — never the default. When internal context matters, we ask rather than guess, and only within the participation boundaries you approved.
Fusion AI
Reframes the question, decomposes it into subproblems, and maps what is actually uncertain.
Targeted research
Closes factual and evidential gaps that opinion cannot close.
Independent human experts
Receive purpose-specific sanitized packets and contribute experience where it can add genuinely new information.
Permissioned internal context
When only your team holds the answer, a named person you approved is asked one precise question — never a default, never a workshop.
Founder-led synthesis
Reconciles findings, disagreement, and limitations into a deliverable designed around your next action.
A deliverable designed around your next action
Not every review should end in one long document. A review can produce a short executive decision review for choosing, plus one execution path per credible option — so the material you keep is the material you will actually use.
For choosing
Executive Decision Review
- Recommendation and the reasoning behind it
- The decisive trade-offs between alternatives
- Assumptions the answer rests on
- Remaining uncertainty, stated plainly
For executing, if chosen
Option A path
- Expected value and cost profile
- Milestones and sequencing
- Resources and dependencies
- Risks and decision triggers
For executing, if chosen
Option B path
- The corresponding execution path
- Where it diverges from Option A
- What must be true for it to win
- Early signals to watch
You use the short review to choose, then keep the selected path as operational material. This is a delivery structure, not necessarily more analysis.
What we can state plainly, before you send anything
- An NDA is available before any sensitive disclosure.
- Independent human experts receive sanitized, purpose-specific problem packets rather than your original confidential files.
- Selected material may be processed by disclosed third-party AI providers, according to a processing plan agreed with you.
- Human experts reason independently before any synthesis is attempted.
- Your team is contacted only within the participation boundaries you set during onboarding.
- Material changes to scope, cost, timing, confidentiality boundaries, or effort asked of your team remain yours to approve.
- 50% at commissioning, 50% after delivery.
- Every final report is reviewed by the founder before it reaches you.
A synthetic sample, so you can judge the output before buying
AI assistant: build, buy, partner, or stop
A 90-person B2B SaaS company has committed roadmap capacity to an in-house AI assistant. A vendor alternative exists at a lower initial cost. The decision affects two quarters of engineering capacity and the next pricing revision.
What decision would become more valuable if one material blind spot were found before commitment?
You already own the decision. Our job is to increase the information available before you commit. If a review is not the right instrument, we will say so.