AI Consulting in Manhattan

New York follows the ABA Model Rules with state-specific modifications, and Rules 1.1, 1.6, and 5.3 are the three that come up in every law-firm engagement. Competence under 1.1 means the attorney supervising the AI output has to understand what it can and can't do — we provide written documentation of model limitations and scope boundaries that is designed to survive ethics-committee review. Confidentiality under 1.6 means data handling has to be locked down before anything goes near client matters: we use enterprise API endpoints with signed zero-retention DPAs, matter-level access controls that mirror what's already configured in the firm's DMS, and no third-party model training on firm content. Supervision under 5.3 means a licensed attorney stays in the loop on every output that touches client work — the build produces a work product the associate refines, not a final deliverable that goes out without review. For SDNY and EDNY matters specifically, we flag any workflow that touches sealed filings or grand jury material before scoping the build, because those require additional controls the firm's general counsel needs to sign off on.

Hedge fund workflows touch FINRA and SEC territory the moment they involve investment research, trade rationale, or client communications. We don't build tools that generate investment advice or replace the human analyst in the investment process — that's a regulatory line we stay well clear of. What we do build: research intake tools that ingest earnings transcripts, 10-K/10-Q filings, and news feeds and surface structured summaries for analyst review; workflow automation for back-office processes like LP reporting packet assembly or compliance questionnaire drafting; and internal knowledge tools that let analysts query the firm's own prior research without hunting through shared drives. Data handling for fund clients uses isolated infrastructure, no shared model context across clients, and API providers with SOC 2 Type II certification and financial-services DPAs. We'll ask for the fund's CCO or outside compliance counsel to review the build documentation before go-live — that's not optional on our side.

Yes, and Manhattan luxury retail is where the brand-voice problem is most acute because the gap between a well-trained clienteling associate and an off-tone automated message is immediately visible to a client who spends at that level. The way we approach it: voice training starts with the brand's own archive — clienteling correspondence, approved marketing copy, house style guides — and we build a reference library the model is constrained to before any output is generated. Outputs are templated at the structure level (opening, product reference, closing) but variable at the language level so they don't read as form letters. Every communication that goes out over a sales associate's name stays in a human-review queue; the automation handles drafting and routing, not final send. We also build in brand-voice drift detection so if outputs start diverging from the reference library over time, the system flags it for editorial review rather than quietly degrading.

Ad agencies operate under NDAs with clients that often restrict how client briefs, creative concepts, and campaign data can be handled by third parties. Before scoping any build for an agency, we review the relevant client NDA language — most restrict training-data use and require data residency controls, both of which we address by default. We use no-training API contracts with model providers, so agency client content is never used for model improvement. If a client NDA requires data residency in the US, we route through US-region endpoints and document that in the build spec. For creative workflow automation specifically — brief summarization, competitive research, status-update drafting — we scope access to the minimum data the workflow actually needs, which is typically a single client folder or project board rather than the whole agency server. The agency's account lead signs off on the data-access map before any integration credential is created.

Most Manhattan engagements start with the $99 AI readiness audit because operators here have been through enough vendor pitches to be skeptical of anyone who wants to scope a build before they've seen the actual workflow. The audit is a structured review of where time is leaking in the current operation — which tasks are eating senior-level hours, where handoffs break down, what the intake or intake-equivalent process looks like at the edges. The output is a written report that ranks two to four automation candidates by ROI potential, implementation complexity, and compliance risk. That report is usually the first document that makes the conversation concrete enough for a decision. From there, firms either move to a fixed-price build on the highest-ranked candidate — two to four weeks, one workflow, defined deliverable — or book a $497 Founder Review Call for a ninety-minute working session with written prioritization memo if they want to pressure-test the audit findings before committing. We don't do retainers as a first engagement. One thing done right first.