AI & Machine Learning Company in New York

A scoped AI proof of concept at NYC market rates runs USD 55,000 to 120,000 across six to ten weeks, covering data audit, a baseline model, and a hosted demo behind SSO. A custom production ML model (fraud scoring, AML alert triage, document extraction, churn or LTV prediction) typically lands at USD 150,000 to 380,000 fully loaded, including MLOps, monitoring, an SR 11-7 style model card, and a Local Law 144 bias review if hiring is in scope. Full production AI platforms with RAG, multiple models, fine-tuning, and enterprise integrations into Snowflake, Salesforce, or a Bloomberg Terminal sit between USD 320,000 and USD 1.6M. NYC rates run roughly 25 to 40 percent above Austin or Toronto because the Manhattan financial talent premium is real. We quote fixed fee against a signed SOW rather than open time and materials.

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, incident reporting within 72 hours, and an annual CISO compliance certification signed by a senior officer. Any AI system that touches nonpublic information falls inside that perimeter. Our discovery includes a Part 500 gap review against your existing controls; build phases ship with KMS-managed encryption at rest, TLS 1.3 in transit, role-based access through SSO with MFA, immutable inference logs, and a 72-hour incident playbook wired to your CISO. We have shipped models for NYDFS-regulated firms where examiners reviewed our model card and access controls during a routine exam without findings.

NYC Local Law 144 took effect July 5 2023 and governs Automated Employment Decision Tools used to screen candidates or employees for positions in NYC. If your AI scores resumes, ranks applicants, recommends promotions, or filters interviews, you must commission an independent bias audit by a qualified auditor within the prior 12 months, publish a summary on the careers page, and notify candidates at least 10 business days before use. Fines start at USD 500 per violation and stack daily. We scope a Local Law 144 review during discovery, work with auditors like BABL AI, Holistic AI, or Eticas, and build the disparate-impact reporting (selection rate, impact ratio across race and sex categories per EEOC four-fifths rule) into the model's evaluation harness so re-audits are a query, not a six-week project.

Yes. We ship RAG systems, internal research copilots, document extraction pipelines, and agent workflows for NYC banks, hedge funds, and fintechs. For SEC, FINRA, and NYDFS-regulated clients we stay inside AWS us-east-1 Bedrock with VPC endpoints, Azure OpenAI East US through Private Link, or self-hosted Llama 3.1 and Mistral on dedicated GPU instances when policy bars hosted endpoints. Controls include SR 11-7 model risk governance, challenger model documentation, strict PII and MNPI redaction with named-entity recognition, prompt-injection defences, human-in-the-loop review gates, deterministic evaluation harnesses that run before every production push, and inference logs exportable to Splunk or your model risk platform. We have patterned deployments after public work from JPMorgan, Goldman, Bloomberg, and Morgan Stanley's GPT-4 wealth advisor rollout.

The NY SHIELD Act (Stop Hacks and Improve Electronic Data Security Act) applies to any business holding the private information of a New York resident and requires reasonable administrative, technical, and physical safeguards plus 60-day breach notification. We map the SHIELD private-information inventory (SSN, financial account, biometric, health, online credentials) during discovery, encrypt at rest and in transit, enforce least-privilege IAM, and wire breach detection into your SIEM. For HIPAA workloads at Mount Sinai, NYP, MSK, NYU Langone, or Northwell we operate under a signed BAA, keep PHI inside us-east-1 with no cross-region replication, log every model inference to an immutable store, and produce the de-identification documentation under HIPAA Safe Harbor or Expert Determination that your privacy office signs off.

Most custom AI development qualifies as Section 174 specified research or experimental expenditures, which under the 2017 TCJA must be capitalised and amortised over 5 years for domestic and 15 years for foreign work, though the 2025 OBBB Act restored immediate domestic R&E expensing for tax years beginning after December 31 2024. We deliver time-tracked logs, technical narratives, and experiment histories aligned with what your tax counsel needs for the Form 6765 R&D credit (20 percent of qualified expenses above the base, or 14 percent under the ASC method). NY State stacks on top: the Excelsior Jobs Program Tax Credit offers refundable credits to qualifying tech firms, the NYC Biotech Tax Credit and Relocation and Employment Assistance Program (REAP) apply for in-city work, and the NY Life Sciences R&D Tax Credit hits 15 to 20 percent of qualified spend for early-stage companies. Final eligibility sits with your CPA.

A typical NYC fintech model (fraud scoring, AML alert triage, KYC document extraction, transaction surveillance, credit decisioning) ships in fourteen to twenty-four weeks from kickoff to production, assuming clean historical data and SR 11-7 model-risk documentation in scope. Weeks 1 to 4 cover data audit, feature engineering, and a baseline. Weeks 5 to 12 are modelling, iteration, and challenger testing against your current rules engine or vendor model. Weeks 13 to 18 cover MLOps, monitoring, shadow-mode deployment behind your existing decision flow, and internal audit review. Week 19 onward is staged rollout starting at 5 percent traffic. If you are pre-data, need labelling, or sit inside a Part 500 examination window, add six to ten weeks. We have shipped to this cadence inside Stash, Ramp-style spend management, and broker-dealer environments.

When a project requires genuine research (novel architectures for sparse financial time series, rare-event detection in trade surveillance, reinforcement learning for execution algos, multimodal clinical models), we scope collaborations with Cornell Tech faculty on Roosevelt Island, NYU CDS researchers at 60 Fifth Avenue, Columbia Engineering's Data Science Institute, or the Flatiron Institute's CCM rather than overselling in-house capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most NYC AI projects actually stall. For standard work (RAG, fine-tuning, classical ML, computer vision on known architectures) no academic partner is needed. We will tell you up front which bucket your problem fits, and we have introduced NYC clients to Cornell Tech's Runway program and NYU CDS's industry partnerships when the roadmap and budget fit.