LLM Integration Company in New York

Ask what system of record the model has to touch and the New York number mostly answers itself. A scoped LLM integration proof of concept at NYC rates touches none. A custom production integration (Wall Street analyst copilot, AML alert-triage assistant, ISDA contract reviewer, clinical summarisation tool, or wealth-advisor assistant) touches one. Enterprise platforms with multi-tenant retrieval, fine-tuning, agent layers, and integration into Bloomberg Terminal, Snowflake, Salesforce, Epic, or a core banking system touch several at once. Scope drivers include a single retrieval source (a research archive, a contract repository, or a customer-service knowledge base), a baseline RAG pipeline on Pinecone or MongoDB Atlas Vector Search, and evaluation against Ragas and TruLens. NYC rates carry the Manhattan financial talent premium of 25 to 40 percent over Austin or Toronto. We quote fixed fee against a signed SOW.

It depends on residency, existing platform commitments, and scale. Pinecone (NYC HQ, founded by Edo Liberty in 2019) is our default for fast greenfield work because it is serverless on AWS us-east-1, NYDFS-friendly, and the operational story is one less platform to run. MongoDB Atlas Vector Search is our default when the client already runs Atlas, because keeping operational data and vectors in one cluster simplifies the data-residency narrative for NYDFS Part 500 and removes a moving part from incident response; MongoDB is headquartered in NYC and the support relationship is local. Weaviate Enterprise wins for hybrid keyword-plus-vector search at scale, particularly when the buyer wants self-hosted GPU acceleration. pgvector on Aurora PostgreSQL is the right call when the client refuses a new platform and the corpus is under 10 million vectors. We have shipped on all four. We do not ship on a vector store the buyer's platform team cannot operate.

Part 500 applies to every NYDFS covered entity (banks, insurers, money transmitters, virtual currency businesses, mortgage originators) and the November 2023 second amendment added explicit obligations around access controls, MFA, encryption at rest and in transit, incident reporting within 72 hours, an annual CISO compliance certification, and a written program for nonpublic information. Any LLM that retrieves or processes nonpublic information falls inside that perimeter. Our discovery includes a Part 500 control gap review against your existing program; build phases ship with KMS-managed encryption at rest, TLS 1.3 in transit, role-based access through SSO with MFA, immutable inference logs streamed to your SIEM, and a 72-hour incident playbook wired to your CISO. We deliver every LLM build with a Part 500 control map cross-referenced to the inference log fields, which is exactly what a DFS examiner asks for during a routine exam.

SR 11-7, the Federal Reserve and OCC guidance on model risk management, predates LLMs but every NYC money-center bank applies it to language models anyway. The three pillars are development soundness, validation and challenger testing, and ongoing monitoring. We deliver an SR 11-7 model card that documents data sources, retrieval evaluation, prompt design, evaluation methodology (Ragas faithfulness, TruLens groundedness, BLEU and ROUGE where applicable, plus task-specific human eval), a challenger comparison against a second model family (e.g., Claude 3.5 Sonnet vs Llama 3.1 70B vs GPT-4o), drift monitoring on retrieval precision and answer faithfulness, and a documented retraining and re-validation schedule. The package is sized for the model risk management team at a JPMorgan, Goldman, BlackRock, or Citi review without a second vendor on the line.

Yes, and we are careful about the licensing and redistribution layer. Bloomberg Terminal data carries strict licensing terms that generally restrict bulk export, retraining, and unattended access; we work with the client's Bloomberg account team to scope an authorised data feed (BPipe, Bloomberg Server API, or Data License) for the LLM's retrieval index, and we always default to per-user authentication rather than a shared service account. FactSet, S&P CapIQ, and Refinitiv Eikon carry parallel restrictions and we apply the same pattern. The retrieval layer logs every document access per user for licensing reconciliation. Generated outputs carry footnote-style citations back to the original source. We have shipped this pattern for Wall Street research desks and asset managers where Bloomberg, FactSet, and internal proprietary research are all in the same index, with the licensing posture passing a vendor review at the client's procurement and information-security teams.

FINRA Rule 3110 requires member firms to supervise the activities of registered representatives, including correspondence and communications with clients, and the 2024 FINRA Regulatory Notice 24-09 reiterated that AI-generated communications are squarely inside that obligation. For any LLM that drafts or co-drafts client-facing communications (Morgan Stanley's wealth assistant pattern, broker copilots, RIA tools), we build a supervision sample queue that scores every generated communication for suitability, fair-balance disclosure, and prohibited claims, routes a configurable percentage to a designated supervisor for review, and stores everything for the SEA Rule 17a-4 books-and-records retention period of three to six years on a WORM-compliant medium. SEC Marketing Rule 206(4)-1 obligations are wired into the same pipeline for RIA buyers.

Prompt injection is the OWASP LLM01 top risk for production LLMs, and the attack surface in an enterprise RAG system is the document corpus itself: an attacker can plant instructions in a PDF, an email, or a contract and the retrieval layer will pull them straight into the model context. Our defence stack runs at four layers. First, input sanitisation strips and flags markdown-style injection patterns, hidden Unicode, and known jailbreak templates. Second, retrieval-side filtering scores every retrieved chunk for instruction-like content and quarantines suspicious chunks. Third, a separate guardrail model (NeMo Guardrails, Lakera Guard, or a custom Llama Guard 3 deployment) classifies every prompt and response. Fourth, tool-use is gated behind explicit allow-lists with human-in-the-loop confirmation for any state-changing call. We run a continuous red team using PyRIT and Garak against every release.

A production LLM integration for an NYC enterprise typically takes fourteen to twenty-four weeks from kickoff. Weeks 1 to 4 are discovery: NYDFS Part 500 review, SR 11-7 risk tiering, retrieval-source residency and licensing audit, and an evaluation rubric agreed with the model risk team. Weeks 5 to 12 are build: retrieval pipeline, vector index on Pinecone or MongoDB Atlas Vector Search, prompt design, evaluation harness on Ragas and TruLens, prompt-injection defences, and the SSO and audit-log integration. Weeks 13 to 18 are validation: challenger-model testing, shadow-mode deployment against historical workload, model risk and internal audit review, and a closed pilot with one business unit. Weeks 19 to 24 are rollout, FINRA Rule 3110 or HIPAA supervision wiring, and a documented retraining and re-validation schedule. We have shipped to this cadence inside NYDFS-regulated stacks.