Independent Decision Review
Important decisions are expensive to get wrong, and the information around them is almost never complete when the deadline arrives.
Evidence is partial. Assumptions have not been challenged. The specialist experience you would want is either not in the company or already committed to something else. Meanwhile, everyone close to the question shares the same context, incentives, and blind spots.
You bring one consequential decision. IQ256 designs and runs the intelligence process around what is missing — and delivers one founder-reviewed result.
- 01
Input, optimised
- 02
Adaptive workflow
- 03
Output, designed
What stays yours
The decision, the responsibility, and the approval of every material change to scope, cost, timing, confidentiality boundaries, or effort asked of your team.
The decision is yours. Our job is to increase the information available before you commit.
You do not have to formulate the perfect request
A few honest paragraphs are enough. Turning that into a reviewable question is part of the work, not a prerequisite for starting it.
- We identify which of the material you sent actually matters, and which does not.
- We ask one high-value question rather than sending a questionnaire.
- We complement your input with public research where that is cheaper than asking you.
- Independent human experts can supply outside context and relevant practice you would otherwise have to discover.
- We do not request information whose collection cost exceeds its expected value.
Even excellent internal teams share organisational context, incentives, and assumptions. Independent information can be valuable precisely because it comes from outside the existing decision system.
The decision is decomposed before anything is commissioned
This is not one input, several experts in parallel, and a summary. The question is broken into subproblems, each subproblem gets the source that can actually close it, and the plan changes as evidence arrives.
- 01
Input optimisation
- 02
Problem decomposition
- 03
Subproblems
- 04
Source selection
- 05
Adaptive execution
- 06
Verification
- 07
Synthesis
- 08
Output design
Four words we use precisely
- Problem
- The information you are missing around a decision you already own — not the decision itself.
- Subproblem
- One answerable part of it: a cost question, an adoption question, a procurement question. Each can be closed, and each can open others.
- Intelligence source
- Whatever can actually close that subproblem: Fusion AI, targeted research, an independent human expert, or one permissioned question inside your company.
- Task
- A single piece of work assigned to a source, with the context it needs and nothing more. Tasks are added, reshaped, or cancelled as evidence arrives.
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.
Sometimes the right configuration is the smallest one
For a narrow, well-evidenced question, Fusion AI plus verification may be the adequate configuration. Invoking additional models, research lanes, or independent human experts that cannot add new information is waste — yours, not ours to spend.
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.
We optimise inside an envelope you approve
IQ256 does not decide alone what counts as “sufficient”. We recommend a process; you set the boundaries it is optimised inside, before any work begins.
Find the highest-value intelligence process for the problem, within customer-approved constraints.
Consequence of being wrong
What is genuinely at stake if the choice is wrong.
Desired thoroughness
A quick sanity check, or a deep review of every alternative.
Deadline
When the decision must be made, not when it would be nice to know.
Budget
The envelope the process is optimised inside.
Confidentiality and providers
What may be shared, abstracted, or excluded — and from which AI providers.
Independent human involvement
Whether, and for which topics, external human experts may be commissioned.
Internal-team involvement
Whether we may ask your people, who, and about what.
Output preferences
The form the result should take to be usable.
IQ256 recommends the process. You approve the envelope — and any material change to scope, cost, timing, confidentiality boundaries, or effort asked of your team.
One review can produce more than one deliverable
The result is designed around how you will use it. Often that means a short executive decision review for choosing, plus one execution path per credible option.
You use the short review to decide, then keep the selected path as operational material for the team that has to deliver it.
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.
Fuzzy preference in, explicit trade-offs out
A review does not remove uncertainty. It converts an intuitive preference into a comparison you can defend — including the conditions under which the other option is the right one.
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
Conditional triggers
- Option B becomes the stronger choice only above a defined weekly-adoption threshold.
- Option B depends on an integration that is not currently on the roadmap.
The decision is still yours. The trade-offs are no longer hidden.
What an Independent Decision Review is not
Being clear about the boundaries is part of the service. If a different instrument fits your situation better, that is worth knowing before you spend anything.
Not a talent marketplace
You are not given profiles to screen, brief, schedule, and manage. Independent human experts are selected, briefed, and reconciled by IQ256.
Not a standing consulting team
A standing team is effective for recurring work. This is built around one question, and the composition changes with the next one.
Not a one-pass AI wrapper or an agent panel
Fusion AI decomposes the problem and tests reasoning. It does not produce the answer in a single generation, and simulated advisors are not a substitute for human experience.
Not formal assurance
This is not legal, tax, accounting, security, or regulatory assurance. Where such assurance is required, we say so and stop.
What a good request looks like
- A decision, not a topic: “build, buy, partner, or stop” rather than “our AI strategy”.
- A named person who will own the outcome.
- A date by which the decision must be made.
- At least two alternatives that are genuinely still open.
- A clear statement of what being wrong would cost.
Typical inputs
- A written description of the decision and its alternatives.
- Existing internal analysis, however incomplete.
- Relevant numbers: usage, cost, pricing, pipeline, or capacity data.
- Vendor quotes, contracts, or proposals under consideration.
- A map of what is sensitive and how it may be handled.
- Who on your team may be asked something, and about what.
- What determines review depth
- The consequence of being wrong, the thoroughness you ask for, how much genuine uncertainty remains, and the budget and deadline you set. Depth is a consequence of the decision and your constraints, not a package tier.
Spend according to the consequence of being wrong.
A tightly scoped question can be highly valuable when it sits inside a consequential decision. Larger scope is not inherently better — it is simply more expensive when the missing information does not justify it.
Payment and correction model
Independent human experts are commissioned with real money before findings exist, so the payment model reflects that moment.
50% at commissioning
Paid once scope, price, and boundaries are agreed and before external human experts are commissioned.
50% after delivery
Invoiced when the result has been delivered and reviewed with you.
One correction included
For factual or material process mistakes on our side. This is not unlimited revisions, and no decision outcome is guaranteed.
When IQ256 is a poor fit
We would rather decline than deliver a review that cannot help. These are the common cases.
Selected expert capabilities
B2B SaaS Product & Pricing
- Useful for
- Packaging structure, willingness to pay, feature-to-value mapping, adoption realism for a proposed capability.
- Likely blind spots exposed
- Value captured by the wrong unit of pricing; adoption assumed at a frequency the buyer's workflow does not support.
- Not the right source for
- Legal contract interpretation, tax treatment, or engineering feasibility of a specific architecture.
Capabilities are selected per decision. Selected capabilities are shown without turning the human-expert network into a public directory.
Capabilities are described by what they are useful for and what they are not. Selection is based on measured contribution to previous reviews — what an expert added that nothing else in the process could — rather than titles, employers, or a CV.