AI integration for engineering teams
AI that lives inside your stack — not beside it
475 Cumulus integrates LLM-powered features into your existing web apps — with the middleware, auth boundaries, and observability your eng team expects. No platform rewrite. No pausing the roadmap.
We ship production code to your repo — API middleware, rate limits, fallbacks, evals, and cost tracking included. Not another POC your team has to untangle.
middleware.ts
Server-side LLM proxy with auth, rate limiting, prompt injection defense, and token cost tracking per tenant.
rag-pipeline.ts
Retrieval layer over your databases, docs, and APIs — with tenant-scoped context assembly and citation support.
tools.ts
Tool-calling layer wired to your product APIs — RBAC-enforced, audit-logged, with human confirmation on destructive actions.
The engineering gap
Calling an API is easy. Integrating AI into your architecture is not.
Most eng teams have shipped a chatbot demo. Few have the middleware, security boundaries, and operational tooling to run AI features reliably at scale. That's an integration problem — not a model selection problem.
LLM calls wired directly into the frontend — no middleware, no cost controls, no audit trail
POCs land in production without fallbacks, evals, or observability. Your team inherits the mess
Hiring ML engineers takes quarters. Your roadmap can't wait while you spin up an AI platform from scratch
We integrate at the API and workflow layer — behind feature flags, with full observability — so your team keeps shipping while AI capabilities roll out incrementally.
Capabilities
Production AI features, integrated into your codebase
One workflow boundary at a time — scoped, shipped, and monitored before expanding to the next.
In-app copilots
Context assembly from product state, RBAC, and tenant data. Embedded in existing views — not a detached chat widget.
RAG & semantic search
Retrieval pipelines over your databases, docs, and APIs — with embedding strategy, chunking, and grounded responses.
Tool-calling & agents
Orchestration layer that invokes your product APIs — with permission checks, audit logs, and confirmation gates.
Classification & extraction
Structured output from unstructured input — summaries, entity extraction, and routing governed by your schemas.
LLM middleware
Server-side proxy for model routing, streaming, caching, rate limits, and provider failover — never call LLMs from the client.
Stack-native integration
React, Next.js, Node, Python, legacy SPA — we work within your existing architecture via APIs and service boundaries.
Delivery model
Incremental integration, zero big-bang deploys
Your eng team stays on the roadmap. We handle the AI integration layer — scoped sprints, PRs to your repo, and handoff docs so your team can operate what we ship.
Request a technical assessmentTechnical audit
Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
Architecture & prototype
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
Build & deploy
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
Operate & expand
Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.
Why 475 Cumulus
Built for eng leaders who need to ship — and sleep at night
You need AI in production, not another initiative that stalls in a sandbox. We treat integration like any other critical system — observable, secure, and maintainable.
Code in your repo
We commit to your codebase — typed, tested, and reviewable. No black-box SaaS layer or vendor lock-in on the integration.
Ops-ready from day one
Structured logging, latency dashboards, token cost tracking, fallbacks for provider outages, and eval pipelines — not bolted on later.
Provider-agnostic design
Abstraction layer over OpenAI, Anthropic, Gemini, or self-hosted models. Swap providers without rewriting your product features.
Eng team stays focused
We embed alongside your team for the AI layer — so your senior engineers aren't pulled off core product work for months.
Technical assessment
Describe the feature. We'll map the integration architecture.
Share your stack, auth model, and target workflow. We'll respond with an integration plan — API design, effort estimate, rollout strategy, and what production-ready means for your system.
Or email us directly at hello@475cumulus.com