AI integration services — what we build and how we deliver

475 Cumulus is an integration partner — not a model vendor, not a chatbot SaaS, and not a team that drops a POC and leaves. We embed LLM-powered features into your existing web applications: middleware, auth boundaries, retrieval, tool-calling, and the operational tooling your eng team expects.

If you are a CTO or VP Engineering evaluating how to ship AI without pausing the roadmap, this is what we actually do.

Integration, not isolation

The work sits at the API and workflow layer inside your product. That means:

• Code lands in your repository — typed, reviewed, and testable
• Model calls run through server-side middleware — never exposed from the client
• Features roll out behind feature flags — incremental, measurable, reversible
• Your databases, identity provider, and observability stack stay in place

We are not asking you to migrate to a new platform or operate a separate AI product beside your own.

In-app copilots

Context from product state, roles, and tenant data — embedded in existing views.

RAG & semantic search

Retrieval over your databases, docs, and APIs with grounded, cited responses.

Tool-calling & agents

Orchestration against your product APIs with permission checks and audit logs.

Classification & extraction

Structured output from unstructured input — summaries, routing, entity extraction.

LLM middleware

Server-side proxy for routing, streaming, caching, rate limits, and failover.

Stack-native integration

React, Next.js, Node, Python — we work within your existing architecture.

Each capability ships as production code in your repo — one workflow boundary at a time.

Core capabilities

Each service area below is a workflow boundary we can scope, ship, and monitor before expanding. Most engagements start with one and grow from there.

In-app copilots

Copilots embedded in the views your users already work in — ticket detail, project dashboard, CRM record, admin console. Context is assembled from product state, roles, and tenant data on the server. Not a floating chat widget bolted onto the corner of the screen.

Typical deliverables: context assembly service, streaming UI integration, permission checks, conversation history scoped per entity.

RAG and semantic search

Retrieval pipelines over your databases, documentation, and internal APIs — with citation support and tenant-scoped filters. We start with structured and hybrid retrieval where possible; add embeddings when your data and query patterns justify the operational cost.

See also: RAG without the platform rewrite.

Tool-calling and agents

When the model needs to act — update a record, trigger a workflow, fetch live data — tool calls go through your product APIs with the same authorization as the rest of your app. Destructive actions get confirmation gates. Everything is audit-logged.

Classification and extraction

Structured output from unstructured input: route tickets to the right queue, extract entities from documents, summarize threads into your schema. Governed by your types and validation rules — not free-form text your downstream systems cannot parse.

LLM middleware

The foundation most features share: a server-side proxy for model routing, streaming, caching, rate limits, token accounting, and provider failover. One place to enforce policy before any feature calls OpenAI, Anthropic, Gemini, or a self-hosted model.

See also: What production-ready LLM integration actually means.

Stack-native integration

We work within your existing architecture — React, Next.js, Node, Python, legacy SPAs — via APIs and service boundaries. No mandate to adopt a specific framework or cloud AI suite.

How engagements work

Engagements are phased. You get clear outputs at each stage before committing to the next. Your core eng team keeps shipping product; we own the AI integration layer for the scoped work.

01

Technical audit

1–2 weeks

Architecture map, integration point, risk assessment

02

Architecture & prototype

2–3 weeks

API contracts, middleware design, proof on your stack

03

Build & deploy

4–8 weeks

Production PRs, staging, canary rollout, runbooks

04

Operate & expand

Ongoing

Monitoring, evals, next workflow boundary

Scoped phases with clear outputs — your eng team stays on the core roadmap throughout.

Phase 1: Technical audit

We map your architecture, API boundaries, data flows, and auth model. The output is an integration plan: recommended starting point, middleware design sketch, effort estimate, rollout strategy, and risks — not a generic AI strategy deck.

You leave with: a decision-ready document your team can evaluate against roadmap priorities.

Phase 2: Architecture and prototype

API contracts, middleware structure, and a working proof against your real stack — staging environment, real auth, representative data. Validates assumptions before full build commitment.

You leave with: something your senior engineers can review in a PR, not a slide demo.

Phase 3: Build and deploy

Production code with tests, staging validation, load testing where appropriate, and canary rollout behind feature flags. Runbooks so your on-call knows what to monitor and how to disable the feature.

✓Typed, tested code in your repository

✓Middleware with auth, rate limits, and logging

✓Dashboards for latency, cost, and quality

✓Eval pipeline for prompt and retrieval changes

✓Runbooks and handoff documentation

✓Feature-flag rollout plan

Designed for your team to operate and extend — not for ongoing black-box dependency.

Phase 4: Operate and expand

Monitor latency, token cost, and output quality. Iterate on evals and prompts. When the first workflow is stable, scope the next boundary — another data source, another surface, tool-calling on top of retrieval.

Where teams usually start

New in-product feature

Signal

Roadmap item needs AI; no integration layer yet

Approach

Audit → thin vertical slice → expand

Example

Support copilot in ticket view

Productionize a POC

Signal

Demo works; lacks auth, observability, or rollout plan

Approach

Harden middleware → evals → feature-flagged GA

Example

Internal chatbot ready for customers

Expand existing AI

Signal

First feature shipped; need next workflow or data source

Approach

Operate current → scope next boundary → iterate

Example

Add tool-calling after RAG search

Most teams fit one of these patterns — we scope from your stack, not a fixed package.

New in-product feature

You have a roadmap item — copilot, smart search, automated triage — and no integration layer yet. We scope a thin vertical slice: one workflow, one data source, one UI surface. Ship it behind a flag, measure, expand.

Productionize a POC

Someone on the team built a demo. It works in happy-path testing but lacks server-side auth, cost controls, observability, or a rollout plan. We harden the path: middleware, evals, fallbacks, and a feature-flagged path to GA.

Expand existing AI

The first feature is live. You need the next capability — tool-calling, additional retrieval sources, a second product surface — without destabilizing what already works. We operate the current integration and scope the next increment.

How we work with your team

• PRs to your repo — same review process, same CI, same standards
• Embedded collaboration — we align with your eng leads on architecture; we do not go dark for months
• Handoff by design — runbooks, dashboards, and eval pipelines your team can run without us
• Optional ongoing iteration — many teams keep us for expansion and prompt/retrieval tuning; none are locked in

What to include in a first conversation

The fastest path to a useful integration plan is specifics:

You do not need answers to everything — but the more context you share, the more precise the architecture and estimate.

Pricing shape

Engagements are scoped by phase — audit, build, operate — with fixed fees based on complexity and timeline. We outline options after the technical assessment so you have a clear estimate before committing to implementation work. No surprise scope creep without an explicit change conversation.

Ready to scope your stack?

If this matches where your team is — demo done, integration unclear, roadmap cannot pause — describe the feature and we will respond with an integration plan: API design, effort estimate, rollout strategy, and what production-ready means for your system.