Project Gambit case study
Strategic AI with governance built in
A decision intelligence platform for a governance advisory firm. It turns the firm's weeks-long advisory process (reading the market, building scenarios with full financials, scoring risk) into an AI pipeline where every number is traceable, so CEOs and boards get options they can trust.
- Industry
- Enterprise AI and governance
- Product
- Decision intelligence platform
- Status
- In progress
The client
Our client is a governance and strategy advisory firm that works with the CEOs and boards of small and mid-sized companies.
Every engagement follows the same pattern: collect the company's data, read the market, build operating scenarios with full financials, assess the risks, and present a recommendation the CEO can act on.
The challenge
That process typically took weeks per client and relied entirely on the senior partners' personal expertise, so it could not grow beyond the partners themselves.
The firm wanted software that performs the same analysis, so partners review and correct the work instead of producing it from scratch. In time, junior team members could run it with clients, and the platform could be licensed to other advisory firms.
Because the output goes to CEOs and boards, it had to meet a governance standard: every figure checkable, the same inputs always giving the same answer, and the final decision left to people, not the AI.
How we approached it
AI reasons, tested tools calculate
The AI weighs market signals, compares trade-offs and writes the narrative. Every figure (P&L, cash flow, break-even, return on capital) comes from deterministic, unit-tested calculation tools, never from the model.
Options come from expert playbooks
The engine does not invent strategies. It matches each situation to a library of decision playbooks curated by the firm's experts, then evaluates the options it finds.
Five validation gates
Every number is traced to its source, tested, format-checked, range-checked and cross-checked so the financials balance. If any check fails, the pipeline stops before anything reaches the user.
Prove it before scaling it
The first milestone was a go/no-go test: could the tools reproduce the numbers from one of the firm's real past engagements? The full build only continued once they did.
Inside the platform
Screens from a demo workspace. Click any screen to see it full size.
What we're building
Two-pass decision pipeline
First "what could we do?": market options with full financials. Then, once the CEO or CFO adds cash, financing and risk appetite, "can we actually do it?": execution paths and the gaps to close.
Five-dimension governance scoring
Each option is scored on market risk, financial risk, operational risk, opportunity cost and strategic fit, side by side.
Board-ready decision brief
Options, trade-offs and a recommendation written for CEO or board review. The platform does not decide; it lays out the trade-offs so people decide well.
Approval chains for every change
Each change to a plan, methodology, forecast or data upload goes through a maker, checker and approver chain with its own signature limits. The AI check is advisory; it never approves anything.
Full audit trail
Any figure in the output traces back to the calculation and the inputs that produced it, and the same inputs always produce the same output.
Industry plugins
A new industry is a new configuration file, not new code. The same engine and the same library serve every sector.
Partner review interface
Partners inspect every step, compare scenarios and submit corrections, and each correction sharpens the playbooks the engine draws on.
The package for a project like this
AI Pilot
Prove one AI use case before you invest in it.
4-12 weeks · Fixed price
- One use case and one success metric, agreed up front
- A working pilot built on your real data
- A go or no-go recommendation with the cost to take it to production
- All code and IP handed over to you
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