BY KARYA

Decision
Engineering.

Decision
Engineering.

Replace narrative-driven strategy with AI-simulated, probability-weighted decision portfolios. Thousands of agents. Hundreds of strategies tested. Evidence, not advice.

See How It Works
THE PROBLEM

Smart People
Guessing.

Companies face decisions worth tens or hundreds of millions. The current options are: debate internally, hire a top consultancy, or do market research. All three share the same fundamental limitation — they're narrative-driven. Someone constructs a story about what might happen, supports it with frameworks and benchmarks, and presents it with confidence.

3–5

The maximum number of strategic options any human team can explore in depth. The strategy space is vast. They're sampling it with a teaspoon.

3 Scenarios

Optimistic, base, pessimistic. That's the methodology behind decisions that move billions. A framework built for a world far less complex than the one we operate in.

Echo Chamber

The consultancy interviews your own people, synthesizes what you already know into a prettier format, and adds some benchmarks. You're paying to hear your own thinking back, structured better.

THE SOLUTION

What Decision Engineering Does.

A client brings a problem — specific or wide open. The system does three things no human team can.

A Living Model of Your World

Thousands of AI agents — competitors, customers, regulators, suppliers, internal stakeholders — each with calibrated personas, incentives, and behavioral profiles. Not a spreadsheet. A living system where actors interact, react, form coalitions, and shift positions.

Autonomous Strategy Generation

The system doesn't wait for you to propose options. It generates candidate strategies, encodes each as a simulation scenario, runs it hundreds of times, and measures outcomes. 100, 200, 500 iterations. Each one informed by everything the previous ones revealed. Directed, compounding, autonomous.

A Decision Portfolio

Not one recommendation. Not three scenarios. A ranked set of strategies that survived simulation — each with its probability distribution, risk profile, key sensitivities, and the reasoning behind why it works or fails. You don't get advice. You get evidence.

ARCHITECTURE

Four Layers.
One System.

Each layer is purpose-built. Together, they do what no human team can.

01

The World

Thousands of AI agents simulating your market — competitors, customers, regulators, suppliers. Each with calibrated behavioral profiles. They reason, adapt, and respond to each other, producing emergent dynamics no spreadsheet can capture.

02

The Operator

Orchestrates the engagement into parallel workstreams — gathering intelligence, calibrating agent profiles, configuring simulations, synthesizing results. Gets more capable with each engagement.

03

The Strategist

Autonomously generates candidate strategies, simulates each one, measures outcomes, and decides: keep, discard, or log for insight. Then generates the next idea informed by everything it's learned. Runs for hundreds of iterations without human input.

04

The Intelligence Layer

Powers agent behavior, strategy generation, interpretation, synthesis, and client communication. The behavioral constraints and domain expertise are proprietary Karya methodology.

THE PROCESS

From Problem
to Portfolio.

A structured engagement that moves from problem definition to stress-tested strategy in weeks, not months.

Phase 0

Intake

You describe the problem. It can be a specific decision or a wide-open question. We determine whether it's simulatable and define the measurable objectives.

Output: Simulation design brief
Phase 1

World Construction

Research agents gather competitive intelligence, financial data, and regulatory filings. We build the simulated world: actors, relationships, incentive structures, behavioral profiles. Agent populations are calibrated against real-world data.

Output: Calibrated agent ensemble
Phase 2

Strategy Exploration

The system generates candidate strategies, simulates each across the ensemble, keeps what works, discards what doesn't. Hundreds of iterations, each informed by everything previous runs revealed. It clusters survivors, identifies key tradeoffs, and maps the sensitivity landscape.

Output: Ranked strategy portfolio
Phase 3

Delivery Workshop

We present the strategy portfolio. You can interrogate the simulation interactively — talk to individual agents, explore scenario branches, ask 'what if we delay by 6 months?' or 'what if the competitor cuts prices first?' The system runs targeted simulations live.

Output: Interactive strategy map
Phase 4

Live Monitoring

Continuous signal tracking — news, regulatory filings, competitive moves, market data — configured for the variables that mattered most in the simulation. When something shifts, you get alerted and optionally get a re-simulation showing how the strategy landscape has changed.

Output: Adaptive signal alerts
USE CASES

Every Decision
Has a Shape.

From billion-dollar acquisitions to market entry calls at a Series C — if the stakes justify the rigor, we can simulate it.

Enterprise

Global pharmaceutical company

Should we acquire a mid-stage biotech competitor or build the capability in-house?

Simulated regulatory approval timelines, competitor responses, integration risks, and talent retention across 200+ scenarios.

Growth-stage

Series C fintech startup

Which of three markets should we enter first — and through which channel?

Modeled customer adoption curves, regulatory friction, and competitive response for each market-channel combination. Identified a non-obvious channel the team hadn't considered.

Enterprise

Industrial conglomerate

How do we restructure our portfolio in response to new tariff policy?

Simulated supply chain shifts, competitor repositioning, and customer switching behavior across multiple tariff scenarios to find resilient allocation strategies.

Mid-market

Regional retail chain (80 locations)

Should we invest in e-commerce or double down on physical expansion?

Ran competitive simulations with local and national players, modeled consumer behavior shifts, and stress-tested both strategies against recession and growth scenarios.

Enterprise

Tier 1 automotive supplier

How should we price our new EV component line without triggering a price war?

Simulated pricing responses from three major competitors, OEM procurement behavior, and margin impact across different volume assumptions.

Startup

Pre-Series A developer tools company

We're losing deals to an open-source alternative. Compete, acquire, or pivot?

Modeled community growth dynamics, enterprise conversion funnels, and acquisition integration risks. Surfaced a partnership strategy that outperformed all three options.

GET IN TOUCH

Start a
Conversation.

We work client by client. Contact us and we'll evaluate if Decision Engineering is the right fit for your problem.