Where expertise becomes a cognitive environment.
Adara is the platform underneath every Cognitive Digital Twin we build. It elicits how your experts reason and structures that reasoning into a knowledge graph your team can work inside. A pedagogical policy engine decides when to answer and when to challenge. Every output traces back to its verified ground, whether a document, a dataset, or a model.
Pick the type that matches what you're trying to transfer.
The category is broad; the choice is specific. Each type can be built for verbal domains or quantitative ones, anything from regulatory reasoning to signal diagnosis. Each has its own architecture, governance, and validation path. We work through the selection before we build anything.
Domain Twin
Represents a domain rather than a person. Built from sources, cases, decision logs, and the structured reasoning of a community of practice. The most scalable starting point.
e.g. Restructuring advisory · Threat modeling · Clinical workflow analysis
Composite Expert Twin
Synthesizes the reasoning of multiple experts and primary sources. Reduces idolatry of any single person and bias of any single school. Often the strongest fit for senior judgment.
e.g. Investment committee thinking · Cross-border compliance reasoning
Authorized Expert Twin
Built on a real, consenting expert's method, clearly framed as 'a system that simulates aspects of this person's reasoning, within these limits.' Governance is strict and licensing is explicit. The Twin never claims to be the person themselves.
e.g. A retiring partner's diligence method · A founder's product instinct
Institutional Twin
Captures how your organization decides: how you score risk, approve projects, run audits, develop people. The codified version of your house style of thinking.
e.g. Your DD methodology · Your incident response playbook
Corpus Twin
Grounded in public, historical, or authorial corpora. Useful for fields with deep written traditions or codified bodies of knowledge. Avoids individual impersonation entirely.
e.g. CCII reasoning · AI Act interpretation · Engineering standards
Four layers, one coherent system.
Elicit reasoning, not just content.
Through structured conversations, observation of real cases, and cognitive task analysis, we surface the tacit reasoning patterns that make your experts extraordinary, including the misconceptions they coach junior staff out of. This is the work documents cannot do.
Build the knowledge graph.
Concepts, relationships, paradigmatic cases, common errors, decision thresholds, all organized as a structured graph instead of a document pile. Connections are traceable; sources are declared. The platform gets richer with every interaction.
Apply pedagogical policy.
The policy engine decides whether to answer or ask, when to challenge the user's reasoning, and where support should be reduced as competence grows. These decisions are what separate a knowledge base from an environment for building competence.
Reason transparently.
Each query is routed to the right specialist model, every response checked against verified sources, every inference logged with its reasoning chain. No hallucination without justification. Every answer traceable.
One Twin. Four ways to interact with it.
Cognitive apprenticeship moves through stages. Modeling, coaching, challenge, and fading support are different experiences. The four modes below are how those stages work in practice.
Mentor
SCAFFOLDINGGuides a developing practitioner through unfamiliar territory step by step, asking orienting questions and offering hints rather than handing over the reasoning. The expert's full thinking is revealed at the moment the user needs it.
Sparring partner
ADVERSARIALChallenges the user's thinking with adversarial cases, edge conditions, and counter-arguments an experienced colleague would raise. Productive friction by design. The system you turn to when you want to be wrong faster.
Oracle
EXECUTEDirect, grounded answers for operational tasks. Every output carries full citations, declared uncertainty, and an audit trail. Used in Execute mode for the work where the user already understands the domain and needs a result.
Examiner
VALIDATES UNDERSTANDINGTests transfer. Asks the user to teach back what they understood, with intentional errors planted in the explanation to be caught. Probes whether the principle was actually internalized. This is the mode that closes the apprenticeship loop.
The moves that build cognitive autonomy.
The pedagogical policy engine orchestrates six specific moves. Each one is designed to keep your people doing the thinking at the moment when it's easiest to hand that thinking over.
First-you
Before the Twin gives its answer, it asks for yours: your hypothesis, the assumptions behind it, your confidence in the call. Protects the cognitive operations that disappear first under AI assistance: framing the problem, generating the next move, calibrating against the evidence.
Hint ladder
Help arrives in graded steps: a Socratic question, then a counter-example, then a principle, then a partial example. The full answer only when it has to. Designed to keep the user doing the thinking.
Adversarial cases
After a concept lands, the Twin introduces a realistic case that bends two of its conditions. Surfaces shallow understanding instantly. Builds the robustness no smooth explanation can.
Teach-back
The user explains the principle to a hypothetical junior, or to a deliberately-fallible Twin. Catching the planted error is itself a learning signal. Teaching is the strongest test of having understood.
Transfer tasks
The same principle applied in a different domain, or with different stakeholders and different stakes. If a reasoning move only works in the case where it was learned, it wasn't really learned. Transfer is how we know.
Fading support
As your team's reasoning improves, the Twin does less. Hints arrive less often and cases get harder, until the system is actively working to make itself less needed. Anything else would be selling dependence.
Built for your domain.
Your cognitive assets. Your infrastructure. Your control.
On-premise by default
By default, models run on your own systems and your cognitive assets stay inside your infrastructure. We support any GPU-capable server.
Privacy Shield
For the rare cases where you choose frontier cloud inference, identifying data (names, IDs, account numbers) is pseudonymized before anything leaves your perimeter.
Air-gap ready
Internet-optional operation for classified or air-gapped environments. You decide exactly what, if anything, may reach an external system.
Audit everything
Complete inference logging. Every query, every reasoning chain, every source consulted, all logged and reviewable. Explainability by architecture.
See it with your data.
Bring a real challenge from your domain. We'll walk through how Adara would approach it, live, with your team.