AI integration for engineering teams
475 Cumulus integrates LLM-powered features so AI lives inside your stack — not beside it as a sidecar chatbot. Middleware, auth boundaries, and observability are built in from day one. 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
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
One workflow boundary at a time — scoped, shipped, and monitored before expanding to the next.
Context assembly from product state, RBAC, and tenant data. Embedded in existing views — not a detached chat widget.
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Retrieval pipelines over your databases, docs, and APIs — with embedding strategy, chunking, and grounded responses.
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Orchestration layer that invokes your product APIs — with permission checks, audit logs, and confirmation gates.
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Structured output from unstructured input — summaries, entity extraction, and routing governed by your schemas.
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Server-side proxy for model routing, streaming, caching, rate limits, and provider failover — never call LLMs from the client.
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React, Next.js, Node, Python, legacy SPA — we work within your existing architecture via APIs and service boundaries.
Delivery model
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 assessmentMap your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.
Why 475 Cumulus
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.
We commit to your codebase — typed, tested, and reviewable. No black-box SaaS layer or vendor lock-in on the integration.
Structured logging, latency dashboards, token cost tracking, fallbacks for provider outages, and eval pipelines — not bolted on later.
Abstraction layer over OpenAI, Anthropic, Gemini, or self-hosted models. Swap providers without rewriting your product features.
We embed alongside your team for the AI layer — so your senior engineers aren't pulled off core product work for months.
Live demo
The chat button in the corner isn't a third-party widget. It's a production-style integration: server-side LLM calls, content-grounded answers, streaming responses, and rate limiting.
The assistant reads from this site's articles, FAQ, and service copy — not a generic model prompt.
Requests go through a Next.js API route. No API keys in the browser, no direct provider calls from the client.
Responses stream token-by-token. Rate limits and scoped system prompts keep usage predictable.
Try asking:
Resources
Practical notes on production AI integration — architecture, rollout, and what to ship before calling a feature GA.
Langfuse covers traces and evals. Here is what else production teams need: structured logging, OpenTelemetry metrics, quality signals, sampling, canaries, and when to add Braintrust, Phoenix, or your existing APM.
A practical guide to LLM cost observability: structured logging, Langfuse dashboards, OpenTelemetry metrics, per-tenant budgets, and the unit economics finance actually needs.
A practical guide to embedded copilots: context from product state, server-side assembly, RBAC, and UI patterns that fit existing workflows instead of a floating chat widget.
Q&A
Straight answers on scope, ownership, security, and how we work with your team. Don't see yours? Ask us directly.
Ready to map an integration path for your stack? Request a technical assessment
Technical assessment
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
