AI Consulting in New York

SDNY and EDNY both have local rules that affect how discovery productions are structured, what metadata must be preserved, and how privilege logs need to be formatted. Any document-review automation we build respects those specifics from the start — privilege log fields match what the court expects, Bates numbering and load-file formats follow standard EDRM specs, and the review workflow flags documents that need attorney eyes before production rather than making those calls automatically. We don't build AI that decides what's privileged; we build AI that organizes, tags, and surfaces documents so a licensed attorney makes that call in a fraction of the time. The attorney stays in the loop on every production decision. We also integrate with the document review platforms most SDNY and EDNY litigators already run — Relativity, Everlaw, Reveal — so the AI layer sits inside the tools your team already knows, not on top of a new system they have to learn.

FINRA-registered environments require that communications, research outputs, and client-facing materials go through review and supervision workflows before distribution. We don't build AI that routes outputs directly to clients or to regulated communication channels without a supervisory step in between. Research synthesis tools we build for portfolio teams are internal — they produce draft memos and data summaries for analyst and PM review, never for direct client delivery. For anything touching client communications or marketing materials, we build the workflow with a review queue baked in, so compliance gets the artifact before it goes anywhere. We also keep model outputs logged with timestamps and input provenance so the firm has an audit trail if an examination requires it. Before any build goes live in a registered environment, we document the workflow map, the data flows, and the human review steps, and we hand that documentation to your CCO to review against your written supervisory procedures.

Brand voice in AI outputs is a prompt engineering and fine-tuning problem, and it's solvable when someone actually takes the time to solve it. The starting point is a voice document we build with your team — specific, example-heavy, covering tone, vocabulary, sentence structure, what the brand never says, and how responses shift between a VIP customer complaint and a press inquiry. That document becomes the backbone of every system prompt across every customer-facing or internal-creative use case we build. We then run sample outputs against your existing brand materials and iterate until the team stops noticing they're reading AI-generated text. For customer service at volume, the AI handles routine inquiries in brand voice and flags anything that needs a human — returns disputes, press contacts, anything with legal exposure. For creative briefs, it drafts the brief structure based on campaign inputs and brand guidelines, and a creative director edits from a strong starting point instead of a blank page. The goal is that your brand voice gets more consistent at scale, not less.

The data-volume question is really two questions: how fast does the system need to process, and what's the cost model when the volume spikes. We architect for both. On throughput, we use asynchronous processing queues for batch workloads — a hedge fund running nightly research synthesis across fifty ticker symbols doesn't need all fifty done in real time, but it does need all fifty done before the morning call. Queued batch jobs handle that cleanly without paying real-time API pricing. For workflows that do need low latency — a customer service bot, a real-time document tagger — we structure the prompts and context windows to keep token counts tight, because in high-volume environments the difference between a 2,000-token and an 800-token prompt is material at the end of the month. We give every client a cost model at the scoping stage: estimated monthly API spend at expected volume, what the ceiling looks like at 3x volume, and where the architecture has levers to pull if costs need to come down. No surprises on the bill.

Most single-capability builds run two to four weeks from signed scope to go-live. The variance is almost always on the integration side — if your practice management system, DMS, or data warehouse has clean API access and your IT team can provision a service account in the first week, two weeks is realistic. If there's an on-prem system, a legacy CRM with no API, or a procurement process that adds two weeks to credential access, four weeks is more accurate. The process is the same either way: week one is audit and integration mapping, where we document every data flow and get read-only access to the systems the build touches. Week two is build and iteration in a staging environment with real but sandboxed data. Week three is UAT with the actual team members who will use the tool daily — not just the partner who approved the project, but the associates or analysts doing the work. Week four is go-live, monitoring, and handoff documentation. After go-live, most New York clients keep a monthly retainer for prompt tuning, integration upkeep, and onboarding new staff. The city moves fast; the tools need to keep up.