Embed AI into SaaS · Seattle

Embed AI into your SaaS in Seattle.

Most Seattle SaaS teams are .NET on Azure or TypeScript on AWS, not the SF SaaS stack of Next.js plus pgvector plus Vercel. The integration story is different. Adding AI to a Blazor + ASP.NET Core product means Semantic Kernel and Azure OpenAI through Private Endpoint. Adding AI to a multi-tenant Java or Node.js SaaS on EKS means Bedrock through PrivateLink and OpenSearch with vector engine.

We embed AI features inside Microsoft-stack and AWS-native SaaS products without asking you to rewrite the product. Streaming UI through SignalR or SSE into your existing frontend. Per-tenant prompt isolation and rate limiting. BYOK support for enterprise customers who want their own model spend. Eval harness gated in CI on every prompt or model change.

Every engagement is fixed-scope, priced to the integration boundary rather than a generic rate card. Discovery includes a codebase walkthrough and an integration boundary memo before any code lands.

How we work

Tell us about the SaaS and the feature.

How we embed AI inside Microsoft and AWS-native stacks.

For Microsoft-shop SaaS the integration looks like this. Semantic Kernel becomes the orchestration layer, injected through your existing DI container as a scoped service. Azure OpenAI is the model layer, accessed through Private Endpoint so traffic stays on the Microsoft backbone. New endpoints live in a dedicated controller or minimal-API group, isolated from the existing surface so the feature can be feature-flagged off if needed. UI streaming flows through SignalR for Blazor Server or SSE for React frontends.

Multi-tenant isolation lives in a tenant resolver middleware that runs before the AI handler. It loads the tenant's prompt overrides, rate-limit configuration, BYOK credentials if configured, and audit-log destination. Spend tracking is per-tenant from day one, written to Cosmos DB or your existing tenant-metadata store. We have shipped this pattern for Microsoft-stack SaaS at Smartsheet- and Tableau-style scale.

For AWS-native SaaS the pattern is similar but the components shift. The orchestration layer is Vercel AI SDK or LangChain in TypeScript, or LangChain in Python for Django and FastAPI services. The model layer is Bedrock Claude Sonnet 5 or Haiku 4.5, reached through a VPC endpoint (com.amazonaws.region.bedrock-runtime) so traffic never leaves your account's network. Vector storage is OpenSearch with vector engine for high-scale workloads, pgvector inside Aurora for smaller ones.

Cost engineering becomes a first-class concern at AWS scale. Prompt caching with Anthropic on Bedrock cuts inference cost 30 to 90 percent on repeated-context features. Tiered model routing sends cheap calls to Haiku and complex ones to Sonnet. Per-tenant token caps prevent a single noisy customer from blowing the budget. The Batch API handles non-realtime workloads at half the on-demand price.

What we build for Seattle SaaS teams.

Six components that show up in every AI feature we embed into a Microsoft-stack or AWS-native product.

Semantic Kernel or Vercel AI SDK orchestration

Orchestration layer injected through your existing DI container or import path. Streaming, tool calling, and conversation state. No separate service, no framework migration required.

PrivateLink or Private Endpoint routing

Bedrock through VPC endpoints on AWS, Azure OpenAI through Private Endpoint on Microsoft. Model traffic never crosses the public internet. Terraform or Bicep ships with the build for your security review.

Per-tenant isolation and BYOK

Tenant resolver middleware that loads per-tenant prompt overrides, rate limits, audit log destinations, and optional BYOK credentials. Enterprise customers can route to their own Azure OpenAI or Bedrock account.

Streaming UI components

SignalR or SSE streaming into your existing Blazor or React frontend. Token-by-token rendering, partial response handling, retry semantics. Matches your existing design system rather than introducing new patterns.

Cost engineering

Prompt caching on Bedrock Anthropic, tiered model routing between Haiku and Sonnet, per-tenant token caps, batch API for non-realtime workloads. Dashboards per tenant for spend, latency, and quality.

Eval harness gated in CI

Braintrust or Promptfoo against a labeled set of real customer use cases. Quality regression has to pass before any prompt or model version change reaches production. Pinned model versions in config, not code.

Why this work fits Seattle SaaS.

Seattle is the densest concentration of cloud-native SaaS teams in the country. Microsoft ships Azure OpenAI and Azure AI Foundry from Redmond. Amazon ships Bedrock from South Lake Union. Smartsheet, Tableau, Auth0 (now Okta), and Zillow all run sizable engineering organizations with the stack and security maturity to ship AI features that hold up under enterprise procurement. Many of them are now shipping AI into their products in 2026 and the question is not whether to ship but how to ship it without breaking the multi-tenancy and the enterprise audit trail.

The enterprise procurement question matters here more than it does in SF. Seattle SaaS sells to the Fortune 500. Fortune 500 security review asks about model providers, data residency, audit logs, BAA coverage when PHI is in scope, and BYOK options for tenants who want control of the spend and the model configuration. A SaaS that ships an AI feature without these answers loses six-figure deals to a competitor that has them.

We work remotely with Seattle clients on Pacific time, with codebase access through a deploy key or repo collaborator invite. Discovery covers the integration boundary and the security model before any code lands.

Industries where we see strongest fit: enterprise B2B SaaS, cloud-native developer tooling, real-estate and travel platforms, marketplace and e-commerce platforms, and .NET-stack vertical SaaS that needs to ship AI into a Microsoft ecosystem deployment.

Stack and security questions from Seattle SaaS teams.

How do you ship AI features inside a .NET or Azure-native SaaS without rewriting the product?: We work inside the existing codebase. For .NET shops the integration point is Semantic Kernel as the orchestration layer with Azure OpenAI as the model layer, both injected as services through your existing DI container. New AI endpoints live in a dedicated controller or minimal-API group, separate from your existing surface, so the feature can be feature-flagged off if an issue surfaces. UI streaming uses SignalR or Server-Sent Events into your existing Blazor or React frontend. We do not ask you to stand up a separate service or migrate to a new framework. We do ask for code-review access and a few hours of your senior engineering team in discovery to map the integration boundary.
Can the AI feature deploy entirely inside our AWS account with no public internet egress?: Yes. For AWS-native SaaS the pattern is Bedrock through PrivateLink (com.amazonaws.region.bedrock-runtime VPC endpoint) so model traffic never crosses the public internet. Vector storage runs on OpenSearch with vector engine or on pgvector inside your RDS Aurora cluster. Embedding generation routes through Bedrock Titan or Cohere Embed v4 on Bedrock, also through PrivateLink. The application talks to Bedrock through the AWS SDK with VPC endpoint configuration, no API key in transit. We ship the Terraform or CDK for the topology so your security team can review and approve the architecture before any code merges.
What does BYOK (bring-your-own-key) look like for enterprise customers of our SaaS?: Enterprise customers increasingly demand control of the model spend and the audit trail. We build BYOK by adding a per-tenant configuration layer that holds the customer's own Azure OpenAI deployment ID and access credentials (or their own Bedrock cross-account role). The application's LLM-routing code checks the active tenant's configuration on every request and either routes to your default deployment or to the customer's. Token usage and call traces still flow into your observability, but the spend hits the customer's Azure or AWS bill and the model is provisioned inside their tenant. This is table-stakes for selling to Fortune 500 enterprise customers, especially in regulated industries.
How do you handle model versioning when Azure OpenAI or Bedrock rotates the underlying model?: We pin deployments to specific model versions in configuration, not in code. On Azure OpenAI that means a named deployment pinned to gpt-5-2025-08-07 or claude-sonnet-4-5-20250929, not a floating alias. On Bedrock that means inference profiles referencing specific model IDs. Every change to a model version is treated as a code change: the eval harness (Braintrust or Promptfoo) runs against the new version, and the regression suite has to pass before the production config rolls forward. We document the active version in the runtime metadata so a support engineer can answer 'what model did we use for this request' from the trace.

Ship the AI feature your enterprise customers can buy.

Describe the SaaS, the stack (Microsoft, AWS, or mixed), the feature, and your enterprise procurement constraints (BYOK, private networking, audit trail). We'll reply within one business day with a rough scope and a price range.

Embed AI into your SaaS in Seattle.

Every engagement is fixed-scope, priced to the integration boundary rather than a generic rate card. Discovery includes a codebase walkthrough and an integration boundary memo before any code lands.

How we embed AI inside Microsoft and AWS-native stacks.

What we build for Seattle SaaS teams.

Six components that show up in every AI feature we embed into a Microsoft-stack or AWS-native product.

Semantic Kernel or Vercel AI SDK orchestration

Orchestration layer injected through your existing DI container or import path. Streaming, tool calling, and conversation state. No separate service, no framework migration required.

PrivateLink or Private Endpoint routing

Per-tenant isolation and BYOK

Streaming UI components

Cost engineering

Prompt caching on Bedrock Anthropic, tiered model routing between Haiku and Sonnet, per-tenant token caps, batch API for non-realtime workloads. Dashboards per tenant for spend, latency, and quality.

Eval harness gated in CI

Why this work fits Seattle SaaS.

Stack and security questions from Seattle SaaS teams.

How do you ship AI features inside a .NET or Azure-native SaaS without rewriting the product?

We work inside the existing codebase. For .NET shops the integration point is Semantic Kernel as the orchestration layer with Azure OpenAI as the model layer, both injected as services through your existing DI container. New AI endpoints live in a dedicated controller or minimal-API group, separate from your existing surface, so the feature can be feature-flagged off if an issue surfaces. UI streaming uses SignalR or Server-Sent Events into your existing Blazor or React frontend. We do not ask you to stand up a separate service or migrate to a new framework. We do ask for code-review access and a few hours of your senior engineering team in discovery to map the integration boundary.

Can the AI feature deploy entirely inside our AWS account with no public internet egress?

Yes. For AWS-native SaaS the pattern is Bedrock through PrivateLink (com.amazonaws.region.bedrock-runtime VPC endpoint) so model traffic never crosses the public internet. Vector storage runs on OpenSearch with vector engine or on pgvector inside your RDS Aurora cluster. Embedding generation routes through Bedrock Titan or Cohere Embed v4 on Bedrock, also through PrivateLink. The application talks to Bedrock through the AWS SDK with VPC endpoint configuration, no API key in transit. We ship the Terraform or CDK for the topology so your security team can review and approve the architecture before any code merges.

What does BYOK (bring-your-own-key) look like for enterprise customers of our SaaS?

Enterprise customers increasingly demand control of the model spend and the audit trail. We build BYOK by adding a per-tenant configuration layer that holds the customer's own Azure OpenAI deployment ID and access credentials (or their own Bedrock cross-account role). The application's LLM-routing code checks the active tenant's configuration on every request and either routes to your default deployment or to the customer's. Token usage and call traces still flow into your observability, but the spend hits the customer's Azure or AWS bill and the model is provisioned inside their tenant. This is table-stakes for selling to Fortune 500 enterprise customers, especially in regulated industries.

How do you handle model versioning when Azure OpenAI or Bedrock rotates the underlying model?

We pin deployments to specific model versions in configuration, not in code. On Azure OpenAI that means a named deployment pinned to gpt-5-2025-08-07 or claude-sonnet-4-5-20250929, not a floating alias. On Bedrock that means inference profiles referencing specific model IDs. Every change to a model version is treated as a code change: the eval harness (Braintrust or Promptfoo) runs against the new version, and the regression suite has to pass before the production config rolls forward. We document the active version in the runtime metadata so a support engineer can answer 'what model did we use for this request' from the trace.

Ship the AI feature your enterprise customers can buy.