AssembleAI

Customisedagenticdecisionharnessesforyourmostconsequentialcalls.

The decisions that demand multi-disciplinary expertise and deep deliberation remain beyond the reach of conventional AI. We built the technology to change that.

01The Technology

How we build your harness

Each harness is built through three layers — capturing the right expertise, algorithmically selecting the optimal combination, and deploying it within a secure orchestration framework tailored to your decision context.

01

Intelligence Capture

Proprietary agents build living representations of human expertise — your decision-makers, domain specialists, and curated archetypes. Not profiles. Not fine-tuning. Knowledge architectures that encode reasoning patterns, decision heuristics, and domain intuition.

02

Algorithmic Assembly

Our algorithms analyse model behaviour at the probability level to determine which combination of captured intelligence produces the most reliable deliberation for your specific decision context. The layer no frontier lab can replicate.

03

Secure Orchestration

Assembled expertise deploys into a bespoke decision harness within your infrastructure, using your existing LLM agreements. It fuses perspectives, synthesises across viewpoints, and produces transparent outputs with full provenance — auditable, stress-testable, and defensible. We never access your data.

02In Practice

Example harness configurations

GAP

Assemble a synthetic version of an existing decision body from twinned members and domain archetypes, and run it before the live meeting. The gap between synthetic and live decisions becomes the diagnostic: where the committee is strong, where it’s blind, and where expertise is missing.

Financial Services

Investment Committee

Synthetic IC run ahead of live meetings to identify blind spots and misalignment before capital is committed.

Boards & Governance

Board Replication

Synthetic board discussion before live meetings — chairs receive the summary to sharpen the agenda.

Financial Services

ALCO Replication

Replicate asset-liability deliberations integrating longevity, climate, and macroeconomic perspectives.

Public Sector

Regulatory Panel

Replicate key decision-makers for high-profile interventions with cross-jurisdiction perspectives.

03Getting Started

Two phases to deployment

Phase 01

Discovery

A focused sprint. Our decision scientists map your highest-value decision processes, identify where assembled intelligence creates the greatest leverage, and align on priority use cases with your team.

Phase 02

Build

We configure our capture, combine, and deploy stack for your agreed use cases — calibrated to your people, your decision structures, your security requirements, and your existing LLM agreements.

04The Problem

The decisions AI still can't reach

Every day, organisations convene committees, boards, and advisory panels for decisions that demand diverse expertise and rigorous challenge. These are the decisions that matter most — and they remain almost entirely unaugmented.

Unaugmented and expensive

Investment committees, board deliberations, policy reviews — your most consequential decisions still rely entirely on costly, time-constrained human processes.

Single models fall short

Enterprise AI agreements deliver one perspective from one model. For decisions requiring multi-disciplinary deliberation, this produces unreliable and unchallenged outputs.

Expertise is scarce and siloed

The right combination of knowledge, experience, and perspective is rarely available when and where critical decisions are made.

Decisions go unchallenged

Without structured adversarial review, cognitive biases, blind spots, and groupthink persist through approval processes unchecked.

05The Research

Grounded in decision science

Our approach is built on decades of research into collective intelligence and structured disagreement.

Structured aggregation of diverse opinions reduces decision error by ~49% vs individual or crowd averages.

Navajas et al., 2018·Nature Human Behaviour

Aggregating intuitive and analytical judgments improves estimation accuracy beyond homogeneous judgments.

Keck & Tang, 2020·Journal of Experimental Psychology

Cognitively diverse groups consistently outperform homogeneous ‘best-expert’ groups in complex decision spaces.

Hong & Page, 2004·PNAS

Disagreeing perspectives boost aggregate accuracy on difficult tasks.

Inner-crowd Research, 2025·Cognitive Science

All leading models supported

Deployed within your infrastructure, using your existing API agreements

OpenAI
Anthropic
Google DeepMind
Meta AI
DeepSeek
Grok
Qwen
OpenAI
Anthropic
Google DeepMind
Meta AI
DeepSeek
Grok
Qwen

Ready to build your
first harness?

See assembled intelligence in action — explore a live instance of our orchestration engine.