Most AI tools in financial services have no memory of yesterday's conversation, let alone your client's history, so every request starts from zero, and the real work of pulling data together, drafting the document, checking it against policy is still yours to do, every single time.
Unique AI Conduct changes that equation. It remembers: building a living memory of your clients, your firm's knowledge, and every task it's done before, so context compounds instead of resetting.
Conduct works proactively, watching for what needs attention and acting on it before it becomes urgent, then delivering a finished, audit-ready result, autonomously and within the guardrails your firm requires.
Conduct is built around three pillars — Grow, Invest, and Operate — mapped directly onto where knowledge work piles up across a financial institution.
Grow: give relationship teams back their client time
Advisors and relationship managers lose hours to manual prep — pulling together client context, building proposals, chasing follow-ups — time that isn't spent in front of clients, where the revenue actually is. Conduct gives that time back. By compounding a living client memory and delegating tasks like meeting preparation and proposal generation, it means more deals out the door without adding headcount.
This is the job of the RM Agent, Conduct's AI teammate for relationship managers. It runs a set of underlying agents — lead generation, meeting preparation, investment proposals, portfolio rebalancing, and follow-up drafts — that turn scattered, stale information across CRM, email, and internal knowledge into one current, living view of the client.
Its Meeting Preparation Agent is a good example of how this plays out in practice: instead of an RM manually compiling correspondence, transcripts, and open CRM tasks before every meeting, Conduct searches and compiles it automatically, selects the right template, and produces a compliance-ready briefing note — turning hours of prep into minutes, with nothing falling through the cracks.
Invest: control overhead without cutting corners
Building pitch decks, proposals, and investment materials by hand consumes staff time and drives up cost. Conduct runs on enterprise-grade financial skills to produce branded output autonomously, around the clock, and in step with market moves and house views.
This is where the IR Agent comes in — the AI teammate for investment teams, covering research briefs, fund selection, portfolio construction, RFP responses, deal room analysis, and branded report drafting.
Its RFP and DDQ Agents ingest a questionnaire, select the right data room, draft sourced answers with citations and a hallucination score, and populate the required template — turning what used to be slow, inconsistent, manual extraction into an automated, submission-ready draft with a full audit trail.
The Reporting Agent works the same way for investor communications: pulling current holdings, performance, and commentary from internal systems into a fully formatted first draft, ready for the team to review and circulate — not rebuilt from scratch every cycle.
Operate: reduce risk by staying ahead of it
Client onboarding, source-of-wealth reviews, and account monitoring are document-heavy and still largely manual — and getting them wrong means regulatory exposure, not just lost time. Conduct is proactive rather than reactive: on watch around the clock, spotting concerns before they become problems, and freeing compliance teams to focus on the judgment calls that actually need a human.
The KYC Agent puts this to work across client research, client onboarding, source-of-wealth review, account remediation, and AML checks.
Its Client Research Agent blends internal and public data to profile clients, reveal concerns, and deliver personalized communication.
Its Client Onboarding Agent turns a KYC questionnaire and a client's documents into a defensible, audit-ready file — compressing weeks of onboarding into days. The SOW Agent runs source-of-wealth write-ups through a firm's regulatory framework in a single pass, cutting review time by 30–40% while flagging concrete remediation steps rather than a bare pass/fail.
The Account Remediation and AML Agents go further still, screening the back book continuously rather than on a fixed annual cycle, running triggered checks the moment an unusual inflow appears, and drafting a regulator-ready suspicious activity report when a genuine risk surfaces — all with the reasoning and evidence attached.
How it works and why you can trust the output
Every Conduct request follows the same path: understand the intent, plan and route it, select the right agents and tools, execute, and then synthesize and validate the result before it's delivered as a report, dashboard, presentation, data export, or communication draft.
Governance is built into every stage, with security and privacy controls, role- and data-level permissions, policy guardrails, full audit logging, and human-in-the-loop escalation running alongside the work itself.
That governance is also what makes Conduct genuinely enterprise-grade rather than a clever demo. Each user works in an isolated per-tenant sandbox, allow-listed egress, PII redaction, and prompt-injection guardrails.
Results are saved to persistent memory in that sandbox, so Conduct can work on tasks that need 15 to 50 output pages — well beyond what fits in a single context window.
Skills — the structured instructions that encode how your firm runs a KYC review or builds a branded proposal — can be admin-managed or user-created, with full traceability of what ran and why.
And because Conduct isn't tied to a single model, you can swap the underlying harness — Claude, Codex, Cursor, PI-Coder — per space, on-prem, in your own cloud, or fully air-gapped, without losing your skills, CLIs, or workflows.
Beyond Chat: How Unique AI Conduct Turns Memory Into Real Work
Most AI tools in financial services have no memory of yesterday's conversation, let alone your client's history, so every request starts from zero, and the real work of pulling data together, drafting the document, checking it against policy is still yours to do, every single time.
Unique AI Conduct changes that equation. It remembers: building a living memory of your clients, your firm's knowledge, and every task it's done before, so context compounds instead of resetting.
Conduct works proactively, watching for what needs attention and acting on it before it becomes urgent, then delivering a finished, audit-ready result, autonomously and within the guardrails your firm requires.
Conduct is built around three pillars — Grow, Invest, and Operate — mapped directly onto where knowledge work piles up across a financial institution.
Grow: give relationship teams back their client time
Advisors and relationship managers lose hours to manual prep — pulling together client context, building proposals, chasing follow-ups — time that isn't spent in front of clients, where the revenue actually is. Conduct gives that time back. By compounding a living client memory and delegating tasks like meeting preparation and proposal generation, it means more deals out the door without adding headcount.
This is the job of the RM Agent, Conduct's AI teammate for relationship managers. It runs a set of underlying agents — lead generation, meeting preparation, investment proposals, portfolio rebalancing, and follow-up drafts — that turn scattered, stale information across CRM, email, and internal knowledge into one current, living view of the client.
Its Meeting Preparation Agent is a good example of how this plays out in practice: instead of an RM manually compiling correspondence, transcripts, and open CRM tasks before every meeting, Conduct searches and compiles it automatically, selects the right template, and produces a compliance-ready briefing note — turning hours of prep into minutes, with nothing falling through the cracks.
Invest: control overhead without cutting corners
Building pitch decks, proposals, and investment materials by hand consumes staff time and drives up cost. Conduct runs on enterprise-grade financial skills to produce branded output autonomously, around the clock, and in step with market moves and house views.
This is where the IR Agent comes in — the AI teammate for investment teams, covering research briefs, fund selection, portfolio construction, RFP responses, deal room analysis, and branded report drafting.
Its RFP and DDQ Agents ingest a questionnaire, select the right data room, draft sourced answers with citations and a hallucination score, and populate the required template — turning what used to be slow, inconsistent, manual extraction into an automated, submission-ready draft with a full audit trail.
The Reporting Agent works the same way for investor communications: pulling current holdings, performance, and commentary from internal systems into a fully formatted first draft, ready for the team to review and circulate — not rebuilt from scratch every cycle.
Operate: reduce risk by staying ahead of it
Client onboarding, source-of-wealth reviews, and account monitoring are document-heavy and still largely manual — and getting them wrong means regulatory exposure, not just lost time. Conduct is proactive rather than reactive: on watch around the clock, spotting concerns before they become problems, and freeing compliance teams to focus on the judgment calls that actually need a human.
The KYC Agent puts this to work across client research, client onboarding, source-of-wealth review, account remediation, and AML checks.
Its Client Research Agent blends internal and public data to profile clients, reveal concerns, and deliver personalized communication.
Its Client Onboarding Agent turns a KYC questionnaire and a client's documents into a defensible, audit-ready file — compressing weeks of onboarding into days. The SOW Agent runs source-of-wealth write-ups through a firm's regulatory framework in a single pass, cutting review time by 30–40% while flagging concrete remediation steps rather than a bare pass/fail.
The Account Remediation and AML Agents go further still, screening the back book continuously rather than on a fixed annual cycle, running triggered checks the moment an unusual inflow appears, and drafting a regulator-ready suspicious activity report when a genuine risk surfaces — all with the reasoning and evidence attached.
How it works and why you can trust the output
Every Conduct request follows the same path: understand the intent, plan and route it, select the right agents and tools, execute, and then synthesize and validate the result before it's delivered as a report, dashboard, presentation, data export, or communication draft.
Governance is built into every stage, with security and privacy controls, role- and data-level permissions, policy guardrails, full audit logging, and human-in-the-loop escalation running alongside the work itself.
That governance is also what makes Conduct genuinely enterprise-grade rather than a clever demo. Each user works in an isolated per-tenant sandbox, allow-listed egress, PII redaction, and prompt-injection guardrails.
Results are saved to persistent memory in that sandbox, so Conduct can work on tasks that need 15 to 50 output pages — well beyond what fits in a single context window.
Skills — the structured instructions that encode how your firm runs a KYC review or builds a branded proposal — can be admin-managed or user-created, with full traceability of what ran and why.
And because Conduct isn't tied to a single model, you can swap the underlying harness — Claude, Codex, Cursor, PI-Coder — per space, on-prem, in your own cloud, or fully air-gapped, without losing your skills, CLIs, or workflows.