LLM Integration · Seattle, WA

LLM integration in Seattle.

Seattle builds software with enterprise expectations baked in: identity, audit, region policy, and a cloud bill someone actually reads. LLM integration here means adding language-model features to products and internal platforms in a way that survives security review, runs in the cloud tenancy you already govern, and holds its unit economics at enterprise volume.

We embed LLM features into your existing systems: drafting, extraction, summarization, classification, and grounded search. In-tenancy deployment, SSO and audit integration, model benchmarking, and cost instrumentation are part of the standard build.

How we work

Tell us the feature and the cloud it has to live in.

The technical reality of LLM features in Seattle software.

Enterprise-grade here is a checklist, not a vibe: SSO-carried identity on every request, source-system ACLs enforced at retrieval, structured audit events into the SIEM, model versions pinned and logged. Features built without those properties stall in security review no matter how good the demo was. We build them in from the first commit because retrofitting identity is the most expensive way to add it.

Cloud alignment shapes procurement speed. In-tenancy inference through Bedrock or Azure OpenAI turns a new-vendor review into a new-service review and keeps traffic under network policy you already wrote. We default there for Seattle enterprise work and benchmark the in-tenancy model lineup against your task before anyone argues about model loyalty.

Aerospace and defense-adjacent teams add export control to the picture. ITAR technical data needs GovCloud or certified self-hosted infrastructure and hard walls between controlled and general corpora. The engineering is straightforward; the discipline is in scoping it with your compliance officer up front and accepting that some documents stay out of scope.

At Seattle's volumes, cost design is part of the architecture. Small-model routing for the volume tier, prompt caching on repeated context, batch processing for non-interactive work, and per-feature spend dashboards. The goal is a cost curve that stays boring as usage grows.

What we build for Seattle teams.

Six integration patterns we scope most often for enterprise software, cloud platforms, and logistics.

In-tenancy inference deployment

Bedrock or Azure OpenAI inside your account with private networking, IAM-governed access, and billing on the invoice your finance team already reconciles.

SSO, ACL, and audit integration

Identity carried from your IdP, source-system permissions enforced at retrieval, and structured audit events into your SIEM for every request.

Export-control-aware architecture

GovCloud or certified self-hosted inference for controlled technical data, hard corpus walls, and scoping done jointly with your compliance officer.

Operational language features

Exception narratives, status summaries, and inbox triage for high-volume platforms, with structured systems doing the math and models doing the words.

Model benchmarking in your cloud

The in-tenancy model lineup measured against your real tasks before commitment, re-run on every model or prompt change so quality drift gets caught.

Cost architecture and dashboards

Small-model routing, prompt caching, batch tiers, and per-feature spend tracking designed so the cost curve stays flat while usage climbs.

Built for Seattle's enterprise standards.

Seattle's enterprise software ecosystem, shaped by decades of large-scale infrastructure, expects production AI to behave like any other production service: observable, governed, and accountable to a budget. That expectation works in your favor. The integration patterns that pass review here (in-tenancy inference, inherited identity, SIEM-visible audit) are exactly the ones that keep working at year two.

The local mix sets the menu: enterprise software and cloud platforms want governed features inside existing products; logistics and e-commerce operations want language at the edges of high-volume event systems; aerospace-adjacent teams need export-control walls before anything else.

We work with Seattle teams remotely, with architecture reviews and weekly demos on video in Pacific hours. Typical engagements run two to six weeks from kickoff to a production feature behind a flag.

Questions from Seattle buyers.

We are already deep in AWS (or Azure). Should the integration use Bedrock / Azure OpenAI or a vendor API?: If you are deep in one cloud, in-tenancy inference is usually the right call: traffic stays inside your account, IAM and existing network policy apply, billing lands on the invoice you already reconcile, and your security review shrinks from a new-vendor assessment to a new-service review. Bedrock gives you Claude and open-weight options on AWS; Azure OpenAI covers the GPT family under the Microsoft umbrella. The trade-offs are model availability lag (new models sometimes arrive on direct APIs first) and region constraints. We benchmark the in-tenancy model lineup against your use case first; in most enterprise feature work, the difference is negligible and the procurement speed is not.
Some of our documentation is export-controlled. Can an LLM feature touch it at all?: Carefully, and sometimes the honest answer is no. ITAR technical data cannot transit infrastructure accessible to non-U.S. persons, which rules out standard commercial endpoints. The workable architectures are AWS GovCloud with Bedrock where available, or self-hosted open-weight models on infrastructure your compliance team already certifies, with access restricted to authorized persons and full request logging. EAR-controlled material has more flexibility depending on classification. We scope export-controlled work jointly with your empowered official, and we wall the feature so controlled documents are physically excluded from any general-purpose path. If the compliance overhead exceeds the feature's value, we say that in discovery rather than after the invoice.
How does an LLM feature plug into enterprise SSO, permissions, and audit requirements?: The feature inherits your identity stack rather than inventing one. Requests carry the user's identity from your IdP (Entra ID and Okta are the usual suspects), document-grounded features enforce the source system's ACLs at retrieval time so the model can only see what the requesting user could open, and every request writes a structured audit event (user, inputs, retrieved sources, output, model version) to your existing SIEM or log pipeline. That last part matters in enterprise review: a feature that emits CloudTrail-style events gets approved; a black box does not. We treat identity, authorization, and audit as core scope on every Seattle enterprise build, not change orders.
Our logistics platform processes millions of events. Where do LLMs help without melting the budget?: Not on the event stream itself; on the language at its edges. Exception narratives (why is this shipment late, in one paragraph a CSR can paste), customer-facing status summaries, inbound email and document triage, and natural-language queries over operational data. The pattern that controls cost: structured data does the math, the model does the language, and small models handle the volume tier. A per-exception narrative on a Haiku-class model costs a fraction of a cent; reserving a frontier model for the gnarly multi-leg disputes keeps the blended cost flat. We instrument cost per workflow from day one so finance sees the curve before it bends.

Ready to scope your integration?

Tell us the feature, the cloud it must live in, and the review it has to pass. We reply within one business day with a rough scope and a fixed price range.

LLM integration in Seattle.

The technical reality of LLM features in Seattle software.

What we build for Seattle teams.

Six integration patterns we scope most often for enterprise software, cloud platforms, and logistics.

In-tenancy inference deployment

Bedrock or Azure OpenAI inside your account with private networking, IAM-governed access, and billing on the invoice your finance team already reconciles.

SSO, ACL, and audit integration

Identity carried from your IdP, source-system permissions enforced at retrieval, and structured audit events into your SIEM for every request.

Export-control-aware architecture

GovCloud or certified self-hosted inference for controlled technical data, hard corpus walls, and scoping done jointly with your compliance officer.

Operational language features

Exception narratives, status summaries, and inbox triage for high-volume platforms, with structured systems doing the math and models doing the words.

Model benchmarking in your cloud

The in-tenancy model lineup measured against your real tasks before commitment, re-run on every model or prompt change so quality drift gets caught.

Cost architecture and dashboards

Small-model routing, prompt caching, batch tiers, and per-feature spend tracking designed so the cost curve stays flat while usage climbs.

Built for Seattle's enterprise standards.

We work with Seattle teams remotely, with architecture reviews and weekly demos on video in Pacific hours. Typical engagements run two to six weeks from kickoff to a production feature behind a flag.

Questions from Seattle buyers.

We are already deep in AWS (or Azure). Should the integration use Bedrock / Azure OpenAI or a vendor API?

If you are deep in one cloud, in-tenancy inference is usually the right call: traffic stays inside your account, IAM and existing network policy apply, billing lands on the invoice you already reconcile, and your security review shrinks from a new-vendor assessment to a new-service review. Bedrock gives you Claude and open-weight options on AWS; Azure OpenAI covers the GPT family under the Microsoft umbrella. The trade-offs are model availability lag (new models sometimes arrive on direct APIs first) and region constraints. We benchmark the in-tenancy model lineup against your use case first; in most enterprise feature work, the difference is negligible and the procurement speed is not.

Some of our documentation is export-controlled. Can an LLM feature touch it at all?

Carefully, and sometimes the honest answer is no. ITAR technical data cannot transit infrastructure accessible to non-U.S. persons, which rules out standard commercial endpoints. The workable architectures are AWS GovCloud with Bedrock where available, or self-hosted open-weight models on infrastructure your compliance team already certifies, with access restricted to authorized persons and full request logging. EAR-controlled material has more flexibility depending on classification. We scope export-controlled work jointly with your empowered official, and we wall the feature so controlled documents are physically excluded from any general-purpose path. If the compliance overhead exceeds the feature's value, we say that in discovery rather than after the invoice.

How does an LLM feature plug into enterprise SSO, permissions, and audit requirements?

The feature inherits your identity stack rather than inventing one. Requests carry the user's identity from your IdP (Entra ID and Okta are the usual suspects), document-grounded features enforce the source system's ACLs at retrieval time so the model can only see what the requesting user could open, and every request writes a structured audit event (user, inputs, retrieved sources, output, model version) to your existing SIEM or log pipeline. That last part matters in enterprise review: a feature that emits CloudTrail-style events gets approved; a black box does not. We treat identity, authorization, and audit as core scope on every Seattle enterprise build, not change orders.

Our logistics platform processes millions of events. Where do LLMs help without melting the budget?

Not on the event stream itself; on the language at its edges. Exception narratives (why is this shipment late, in one paragraph a CSR can paste), customer-facing status summaries, inbound email and document triage, and natural-language queries over operational data. The pattern that controls cost: structured data does the math, the model does the language, and small models handle the volume tier. A per-exception narrative on a Haiku-class model costs a fraction of a cent; reserving a frontier model for the gnarly multi-leg disputes keeps the blended cost flat. We instrument cost per workflow from day one so finance sees the curve before it bends.

Ready to scope your integration?

Tell us the feature, the cloud it must live in, and the review it has to pass. We reply within one business day with a rough scope and a fixed price range.