NextGen's AI consulting is scoped narrowly by design: workflow automation, internal copilots for partners and analysts, and integration of hosted or self-hosted models into existing practice-management, CRM, and books-and-records systems. Two-to-four-week engagements produce a written recommendation, a working pilot, and a fixed-price implementation plan. Not strategy decks, not AI transformation.
Three workflows account for the majority of shippable AI ROI in NYC financial services and professional services firms today. Everything else is either not ready or not worth the integration cost.
Contract review, disclosure diffing, KYC/AML file triage, and matter-file summarization. A trained pipeline reads 200-page filings in seconds, flags material terms against a rubric, and returns a reviewer-ready summary. A senior associate or paralegal still signs off — throughput on first-pass review commonly rises 4–8x with 15–20 hours per matter clawed back.
Structured intake forms that read uploaded documents (organizational chart, prior tax return, brokerage statement, matter brief), pre-fill the practice-management or CRM record, flag missing fields, and route to the correct partner or advisor. Cuts intake-to-engaged-file time from 5–10 business days to under 48 hours across the firms we've shipped for.
Natural-language queries over the firm's book of business — matter P&L, realization by partner, AUM by advisor, engagement letter status. Backed by governed access controls (partner sees firm-wide, associate sees own matters). Replaces the weekly manually-assembled Excel deck without giving anyone unfiltered database access.
Every firm asks this eventually: hire a senior AI engineer for $280K–$420K all-in, or engage outside consulting. The correct answer depends on what work exists after the first pilot ships.
The four line items that separate the two paths: time-to-first-shipped-workflow — a senior AI hire takes 5–9 months to recruit and onboard in NYC before the first evaluation harness exists; consulting starts producing a recommendation in week two. Model-selection risk — a single in-house engineer is one opinion; a consulting engagement benchmarks 3–5 models against your data before committing, which routinely changes the answer between GPT-4-class, Claude, and self-hosted Llama by 30–60% on cost or accuracy for a given workflow. Governance ceiling — writing an AI usage policy, evaluation rubric, and regulatory position (SEC AI marketing rule, FINRA Reg BI implications, ABA opinions on AI in legal practice) is 40–80 hours of work no single engineer wants to own; consulting includes it. Steady-state fit — after the first three workflows ship, most firms need 0.5–1.0 FTE of ongoing AI engineering — not 1.0 permanent. Consulting plus a fractional retainer or a staff-augmented engineer sizes correctly.
Hire in-house when the firm has a permanent AI roadmap spanning 6+ workflows, an existing head of engineering to manage the hire, and compensation packages competitive with quant funds and top labs. Engage AI consulting for everything else — including as the intake step before the in-house hire.
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