LLM Integration · New York, NY

LLM integration in New York.

New York runs on documents: credit agreements, fund filings, legal briefs, editorial archives, insertion orders. LLM integration here means putting language models inside the products and internal tools that already process that paper, with the audit trail and data controls that financial services and legal compliance teams require.

We embed LLM features into your existing codebase: extraction, summarization, drafting, classification, and search. Prompt design, model benchmarking on your actual data, structured output, caching, and fallback behavior are all part of the build, not extras.

How we work

Tell us which workflow needs a language model inside it.

The technical reality of LLM features in regulated New York industries.

Financial services integrations carry obligations that consumer apps never see. If the feature touches broker-dealer communications, SEC 17a-4 retention applies to the prompts and outputs. If it summarizes research, the compliance team needs to know which model version produced which summary, which means version-pinned deployments and structured logging rather than ad-hoc API calls. None of this is exotic engineering, but it has to be in the architecture from day one.

Legal workflows put accuracy above everything. A redlining assistant that paraphrases a clause incorrectly is worse than no assistant. The integration pattern that works is extraction with span citations: the model points at exact passages, the lawyer verifies in one click, and free-form generation is reserved for first drafts that a human always reviews.

Media and ad tech run the opposite problem: volume. Tagging an archive of two million articles or classifying campaign creative at ingest speed is a unit-economics question. The build centers on small-model routing, prompt caching for repeated context, and batch processing, with a frontier model reserved for the hard cases. Getting the cost per item from four cents to half a cent is normal once routing is in place.

Across all three industries, the failure mode we see in stalled integrations is the same: a prototype built against one model with no evaluation set, no cost instrumentation, and no fallback when the API degrades. The production version needs all three. We build the evaluation harness from your real documents first, then the feature, in that order.

What we build for New York teams.

Six integration patterns we scope most often for financial services, legal, and media products.

Contract and filing extraction

Typed-schema extraction from credit agreements, leases, fund documents, and regulatory filings. Span citations to the source clause, confidence thresholds, and a hand-labeled evaluation set from your own documents.

Compliance-grade logging and retention

Prompt and output logging to WORM storage for 17a-4 and books-and-records obligations. Model version pinning, user attribution, and export formats your compliance archive already accepts.

Research and brief summarization

Summarization inside the tools your analysts and associates already use, with source-grounded output and a refusal path when the document does not support an answer.

Editorial and media tagging

High-volume classification and entity tagging with small-model routing, batch processing, and prompt caching. Cost instrumentation per item from the first deploy.

Model benchmarking and routing

Side-by-side evaluation of Claude, GPT, and open-weight models on your data, not public leaderboards. Routing logic that sends easy traffic to cheap models and hard traffic to strong ones.

Caching, fallbacks, and cost controls

Prompt caching for repeated context, cached responses for identical queries, degraded-mode behavior when the API is slow, and per-feature spend dashboards so finance sees the bill coming.

Built for how New York buys software.

New York's AI adoption is concentrated in industries where the documents are the business: finance, legal, media, and the ad tech layer that monetizes attention. The buyers are not experimenting. They have a workflow with measurable cost, a compliance function with veto power, and existing systems the feature has to live inside. That shapes our work here more than anything technical: integrations get specified in writing, reviewed by security, and deployed into your cloud accounts rather than ours.

Deployment targets follow the industry. Banks and broker-dealers usually require Azure OpenAI or Bedrock inside their own tenancy. Legal and media companies are more often fine with enterprise API endpoints plus a data processing addendum. We support both paths and document the data flow either way.

We work with New York teams remotely. Scope reviews, security questionnaires, and weekly demos happen on video in Eastern hours. Most engagements run two to six weeks from kickoff to a production feature behind a flag.

Questions from New York buyers.

Our records fall under SEC 17a-4 and FINRA retention rules. Can we still add LLM features?: Yes, if the integration is built for it from the start. Every prompt and model output that touches regulated business communications gets written to WORM-compliant storage (S3 Object Lock in compliance mode is the usual choice) with the user identity, timestamp, model version, and full text. The model itself runs through an enterprise endpoint with zero data retention, so nothing persists on the vendor side. We design the logging layer before the feature, because retrofitting 17a-4 retention onto a shipped integration means re-architecting the data path. Broker-dealer compliance teams usually want to review the retention design document; we produce one as a standard deliverable on regulated engagements.
How do you keep a contract-extraction feature from inventing terms that are not in the document?: Three controls, applied together. First, structured output: the model fills a typed schema (party names, dates, renewal terms, liability caps) rather than writing free prose, which removes most of the room for invention. Second, span citations: every extracted field carries the character offsets of the source passage, so reviewers click through to the exact clause. Third, an evaluation set built from 100 to 200 of your real contracts with hand-labeled answers, run before launch and on every prompt or model change. Fields that fail a confidence threshold render as 'not found' for human review instead of a guess. On legal documents, a visible blank is acceptable; a confident fabrication is not.
We process tens of thousands of media items a month. What does tagging cost at that volume?: Small models change the math. A tagging or categorization call on a Haiku-class model costs a fraction of a cent per item, so 100,000 items a month typically lands in the low hundreds of dollars, not thousands. We benchmark the small model against a frontier model on a labeled sample of your content first. If the small model hits the accuracy bar (it usually does for classification and entity tagging), it takes the volume traffic, and the larger model handles only the items the small one flags as ambiguous. Batch API pricing cuts the bill further for non-real-time workloads, often by half.
Our legal team will not allow client data to be used for model training. How is that handled?: Enterprise API agreements from Anthropic, OpenAI, and the cloud providers (Bedrock, Azure OpenAI) exclude your data from training by default, and we configure zero-retention endpoints where they are available. The integration contract includes a data processing addendum that names the model vendor as a subprocessor with those terms attached. For firms that want stronger isolation, Azure OpenAI and Bedrock keep traffic inside your cloud tenancy with no calls to the model vendor's public API at all. We document the full data path (what leaves your systems, where it goes, what is logged) in a one-page diagram your legal team can review before any production traffic flows.

Ready to scope your integration?

Tell us the workflow, the data sensitivity, and the systems it has to live inside. We reply within one business day with a rough scope and a fixed price range.

LLM integration in New York.

The technical reality of LLM features in regulated New York industries.

What we build for New York teams.

Six integration patterns we scope most often for financial services, legal, and media products.

Contract and filing extraction

Compliance-grade logging and retention

Prompt and output logging to WORM storage for 17a-4 and books-and-records obligations. Model version pinning, user attribution, and export formats your compliance archive already accepts.

Research and brief summarization

Summarization inside the tools your analysts and associates already use, with source-grounded output and a refusal path when the document does not support an answer.

Editorial and media tagging

High-volume classification and entity tagging with small-model routing, batch processing, and prompt caching. Cost instrumentation per item from the first deploy.

Model benchmarking and routing

Side-by-side evaluation of Claude, GPT, and open-weight models on your data, not public leaderboards. Routing logic that sends easy traffic to cheap models and hard traffic to strong ones.

Caching, fallbacks, and cost controls

Prompt caching for repeated context, cached responses for identical queries, degraded-mode behavior when the API is slow, and per-feature spend dashboards so finance sees the bill coming.

Built for how New York buys software.

Questions from New York buyers.

Our records fall under SEC 17a-4 and FINRA retention rules. Can we still add LLM features?

Yes, if the integration is built for it from the start. Every prompt and model output that touches regulated business communications gets written to WORM-compliant storage (S3 Object Lock in compliance mode is the usual choice) with the user identity, timestamp, model version, and full text. The model itself runs through an enterprise endpoint with zero data retention, so nothing persists on the vendor side. We design the logging layer before the feature, because retrofitting 17a-4 retention onto a shipped integration means re-architecting the data path. Broker-dealer compliance teams usually want to review the retention design document; we produce one as a standard deliverable on regulated engagements.

How do you keep a contract-extraction feature from inventing terms that are not in the document?

Three controls, applied together. First, structured output: the model fills a typed schema (party names, dates, renewal terms, liability caps) rather than writing free prose, which removes most of the room for invention. Second, span citations: every extracted field carries the character offsets of the source passage, so reviewers click through to the exact clause. Third, an evaluation set built from 100 to 200 of your real contracts with hand-labeled answers, run before launch and on every prompt or model change. Fields that fail a confidence threshold render as 'not found' for human review instead of a guess. On legal documents, a visible blank is acceptable; a confident fabrication is not.

We process tens of thousands of media items a month. What does tagging cost at that volume?

Small models change the math. A tagging or categorization call on a Haiku-class model costs a fraction of a cent per item, so 100,000 items a month typically lands in the low hundreds of dollars, not thousands. We benchmark the small model against a frontier model on a labeled sample of your content first. If the small model hits the accuracy bar (it usually does for classification and entity tagging), it takes the volume traffic, and the larger model handles only the items the small one flags as ambiguous. Batch API pricing cuts the bill further for non-real-time workloads, often by half.

Our legal team will not allow client data to be used for model training. How is that handled?

Enterprise API agreements from Anthropic, OpenAI, and the cloud providers (Bedrock, Azure OpenAI) exclude your data from training by default, and we configure zero-retention endpoints where they are available. The integration contract includes a data processing addendum that names the model vendor as a subprocessor with those terms attached. For firms that want stronger isolation, Azure OpenAI and Bedrock keep traffic inside your cloud tenancy with no calls to the model vendor's public API at all. We document the full data path (what leaves your systems, where it goes, what is logged) in a one-page diagram your legal team can review before any production traffic flows.

Ready to scope your integration?

Tell us the workflow, the data sensitivity, and the systems it has to live inside. We reply within one business day with a rough scope and a fixed price range.