Embed AI into SaaS · New York, NY

Ship AI features inside your New York SaaS.

Bloomberg added GPT-style summarization to the Terminal. Better.com shipped an AI mortgage assistant. Squarespace generates listing copy with one click. Etsy ranks search with embeddings. Lemonade handles a third of claims with model-assisted triage. The story is the same in every category: the New York SaaS product that ships AI features first wins the next renewal cycle.

Your product is not a greenfield AI startup. It is an existing React or Rails or Node application with real customers, real contracts, and real SOC 2 obligations. The question is not whether to add AI. The question is how to add it without breaking what works.

Pricing for an AI embedding engagement is scoped per feature, with a fixed price agreed before work begins.

How we work

Tell us which feature you want to add. We will scope it this week.

The pressure on a New York SaaS in 2026.

Every category leader in New York has either shipped an AI feature or announced one. FinTech: Bloomberg Terminal's document Q&A, Datadog's Bits AI, Better.com's underwriting assistant. MarTech: Squarespace AI for landing pages, Yotpo's review summaries, the entire ad-tech stack adding generative creative pipelines. InsurTech: Lemonade's claim triage, Hippo's inspection AI, Trōv-style usage analysis.

Your sales team is hearing it on every call. "What is your AI roadmap?" is on every RFP, including the ones from banks that two years ago would not touch a model. Procurement now asks for an AI governance document before they will sign. If you do not have an answer by the next renewal cycle, you lose the expansion revenue.

At the same time, the regulators are awake. New York DFS Part 500 now expects controls around AI in cybersecurity workflows. The SEC has filed enforcement actions over AI claims (the "AI washing" cases). The FTC keeps reminding ad-tech that generated content must be substantiated and clearly disclosed. NYAG has signaled scrutiny on biased model outputs in lending and insurance.

The result is a narrow lane: ship AI features fast enough to win renewals, slow enough to survive an exam. That requires audit trails, model versioning, prompt provenance, and a human-in-the-loop surface for anything customer-facing in regulated domains. It is buildable, but not with a weekend OpenAI integration.

What we build inside your existing SaaS.

AI architecture inside your existing stack

We add an AI service layer to your Rails, Node, or Django codebase that wraps every model call in a single gateway. One place to swap models, log prompts, enforce rate limits, and add caching. No rewrite of your existing business logic.

LLM integration with your data model

Retrieval-aware features that pull from your Postgres, your Elasticsearch, your existing search index. Tenant isolation enforced at the retrieval layer so one customer never sees another customer's data in a model prompt.

AI UI components your team can extend

Streaming chat sidebar, AI command palette, inline 'write for me' field, smart autocomplete. Built with the Vercel AI SDK useChat hook or your existing React component patterns. We use your design system, not ours.

Evaluation harness with regression testing

Golden dataset of 200+ production-style prompts with expected outputs. Promptfoo or Braintrust running on every prompt change. Your engineers see a regression number before they merge a prompt edit.

Model versioning and traffic routing

Every model call goes through a gateway with the model ID as config. New model? Route 5% of traffic, compare against the golden set, decide. Rollback is a flag flip, not a deploy.

Cost guardrails per user and per tenant

Per-user token caps, per-tenant monthly budgets, alerting before the budget burns, prompt caching with Anthropic, batch API for non-realtime jobs, tiered routing where Haiku handles 80% of calls.

Why this matters in New York specifically.

Four industry pockets in New York are racing to embed AI right now. Each has its own compliance edge and its own customer expectation.

FinTech and trading platforms

Bloomberg, Datadog (for financial-services telemetry), trading platforms like Tradeweb, FactSet competitors. The pattern is the same: customers want natural-language interfaces over financial data, summarization of long filings or research notes, and automated alerting from unstructured sources. The hard part is audit. Every model output that touches a trade rationale or a research note has to be reproducible 7 years later.

InsurTech and RegTech

Lemonade, Hippo, and the next wave of insurance SaaS need AI for claims triage, fraud detection, and policy-document summarization for brokers. RegTech platforms (Behavox, Compliance.ai-style) are running model-assisted surveillance over communications and trade data. NY DFS expects model risk governance that mirrors SR 11-7 even if the carrier is not a bank.

MarTech and ad-tech

Squarespace, Etsy, the seller-tools ecosystem, programmatic ad-tech platforms. Generative creative is the new table-stakes feature. The compliance edge here is FTC disclosure rules on AI-generated content, brand-safety filtering, and the small but growing list of state laws on synthetic-media labeling. Every generated asset needs provenance metadata.

Health and consumer SaaS

Peloton-style consumer SaaS with personalization engines, health-adjacent platforms with coaching surfaces, content platforms generating recommendations. The line to watch is anywhere a model output crosses into medical, legal, or financial advice. We design the surface so that line is explicit in the product.

Questions from New York SaaS engineering teams.

We are a New York FinTech SaaS on a Rails monolith. Can you embed AI features without us pausing the product roadmap?: Yes. We start by reading the codebase and mapping the seams where AI calls fit: a service object layer, a background job for non-realtime work, or a thin Node sidecar if streaming responses are required. The first feature usually ships behind a feature flag on a single tenant in 4 to 6 weeks. Your engineers keep building. We work in feature branches, write tests against your existing RSpec suite, and submit PRs your team reviews. Nothing gets merged without your lead engineer signing off. For larger features, we run pair-programming sessions so knowledge transfers as we build.
Our customers include three of the top ten US banks. How do you handle DFS Part 500 and SEC scrutiny on AI?: We design the AI surface so it produces an audit trail by default: every prompt, every model response, every retrieval source, timestamped per user and per tenant, written to your existing observability stack or a dedicated table with a 7-year retention policy. Model versions are pinned and tracked, so when a bank examiner asks which model handled a specific 2024 transaction summary, you have the answer. For SEC-facing claims, we keep AI outputs labeled as model-generated and require human review before they hit a customer-visible document. We do not make legal or compliance attestations, but we build the technical scaffolding so your compliance team can.
What does AI feature cost look like at scale for a New York SaaS with 50,000 monthly active users?: Without cost engineering, a naive integration of GPT-4 class models across 50k MAU can run $60,000 to $200,000 per month in API costs. With the patterns we install (prompt caching with Anthropic, tiered routing where Haiku handles classification and Sonnet handles generation, per-user token budgets, batch API for nightly summarization jobs, and aggressive prompt compaction), the same workload usually lands at $8,000 to $30,000 per month. We instrument the cost per feature per tenant so your CFO can price the feature accurately, and we set hard ceilings per user so a single power user cannot blow the budget.
How do you handle model versioning when OpenAI or Anthropic releases a new model and we need to test before switching?: Every model call goes through an internal gateway with the model identifier as a configuration value, not a hardcoded string. When a new model ships, you point a small percentage of traffic at it through the same gateway and compare outputs against a golden dataset of 200 to 500 production prompts using Promptfoo or Braintrust. You see regression metrics on accuracy, latency, and cost before any customer touches the new model. Rollback is a config flip, not a deploy. We build the dataset and the comparison harness during the engagement so your team can run these tests after we are gone.

Ready to ship the AI feature your renewals depend on?

Tell us your stack, the feature you want, and the regulatory edge you are working with. We reply within one business day with a rough scope, a price range, and a first-feature timeline.

Ship AI features inside your New York SaaS.

Pricing for an AI embedding engagement is scoped per feature, with a fixed price agreed before work begins.

The pressure on a New York SaaS in 2026.

What we build inside your existing SaaS.

AI architecture inside your existing stack

LLM integration with your data model

AI UI components your team can extend

Evaluation harness with regression testing

Golden dataset of 200+ production-style prompts with expected outputs. Promptfoo or Braintrust running on every prompt change. Your engineers see a regression number before they merge a prompt edit.

Model versioning and traffic routing

Every model call goes through a gateway with the model ID as config. New model? Route 5% of traffic, compare against the golden set, decide. Rollback is a flag flip, not a deploy.

Cost guardrails per user and per tenant

Per-user token caps, per-tenant monthly budgets, alerting before the budget burns, prompt caching with Anthropic, batch API for non-realtime jobs, tiered routing where Haiku handles 80% of calls.

Why this matters in New York specifically.

Four industry pockets in New York are racing to embed AI right now. Each has its own compliance edge and its own customer expectation.

FinTech and trading platforms

InsurTech and RegTech

MarTech and ad-tech

Health and consumer SaaS

Questions from New York SaaS engineering teams.

We are a New York FinTech SaaS on a Rails monolith. Can you embed AI features without us pausing the product roadmap?

Yes. We start by reading the codebase and mapping the seams where AI calls fit: a service object layer, a background job for non-realtime work, or a thin Node sidecar if streaming responses are required. The first feature usually ships behind a feature flag on a single tenant in 4 to 6 weeks. Your engineers keep building. We work in feature branches, write tests against your existing RSpec suite, and submit PRs your team reviews. Nothing gets merged without your lead engineer signing off. For larger features, we run pair-programming sessions so knowledge transfers as we build.

Our customers include three of the top ten US banks. How do you handle DFS Part 500 and SEC scrutiny on AI?

We design the AI surface so it produces an audit trail by default: every prompt, every model response, every retrieval source, timestamped per user and per tenant, written to your existing observability stack or a dedicated table with a 7-year retention policy. Model versions are pinned and tracked, so when a bank examiner asks which model handled a specific 2024 transaction summary, you have the answer. For SEC-facing claims, we keep AI outputs labeled as model-generated and require human review before they hit a customer-visible document. We do not make legal or compliance attestations, but we build the technical scaffolding so your compliance team can.

What does AI feature cost look like at scale for a New York SaaS with 50,000 monthly active users?

Without cost engineering, a naive integration of GPT-4 class models across 50k MAU can run $60,000 to $200,000 per month in API costs. With the patterns we install (prompt caching with Anthropic, tiered routing where Haiku handles classification and Sonnet handles generation, per-user token budgets, batch API for nightly summarization jobs, and aggressive prompt compaction), the same workload usually lands at $8,000 to $30,000 per month. We instrument the cost per feature per tenant so your CFO can price the feature accurately, and we set hard ceilings per user so a single power user cannot blow the budget.

How do you handle model versioning when OpenAI or Anthropic releases a new model and we need to test before switching?

Every model call goes through an internal gateway with the model identifier as a configuration value, not a hardcoded string. When a new model ships, you point a small percentage of traffic at it through the same gateway and compare outputs against a golden dataset of 200 to 500 production prompts using Promptfoo or Braintrust. You see regression metrics on accuracy, latency, and cost before any customer touches the new model. Rollback is a config flip, not a deploy. We build the dataset and the comparison harness during the engagement so your team can run these tests after we are gone.

Ready to ship the AI feature your renewals depend on?

Tell us your stack, the feature you want, and the regulatory edge you are working with. We reply within one business day with a rough scope, a price range, and a first-feature timeline.