AI integration services — what we build and how we deliver
A practical overview of 475 Cumulus capabilities, engagement phases, and how we integrate LLM features into existing products without a platform rewrite.
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.
your-app/dashboard
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 assessment01
Technical audit
Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
02
Architecture & prototype
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
03
Build & deploy
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
04
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.
Resources
Guides for engineering leaders
Practical notes on production AI integration — architecture, rollout, and what to ship before calling a feature GA.
RAG without the platform rewrite
How to add retrieval over your existing data without standing up a separate vector platform or pausing the product roadmap.
What production-ready LLM integration actually means
A practical checklist for engineering leaders — beyond the demo and before you call an AI feature shipped.
Q&A
Questions eng leaders ask before engaging
Straight answers on scope, ownership, security, and how we work with your team. Don't see yours? Ask us directly.
What kinds of products do you integrate AI into?
Existing B2B SaaS, internal tools, and customer-facing web apps — anywhere your team already has APIs, auth, and a deployment pipeline. We focus on in-product features: copilots, RAG over your data, workflow automation, and tool-calling against your product APIs.
Do you replace our stack or integrate into it?
We integrate into it — AI inside your product, not a sidecar tool or platform rewrite. We add middleware, services, and UI in your repo and deploy through your existing CI/CD. No platform migration, no separate vendor console your team has to operate. Your databases, identity provider, and observability stack stay in place.
How long does a typical engagement take?
A technical audit and architecture proposal usually takes one to two weeks. A first production feature often ships in four to eight weeks depending on scope, data readiness, and review cycles. Larger rollouts are broken into incremental milestones behind feature flags.
Who owns the code after you ship?
You do. Everything lands in your repository with tests, runbooks, and handoff documentation. We design for your team to operate, extend, and review changes — not for ongoing dependency on us, though we can stay on for iteration and expansion.
How do you handle security and data privacy?
Auth boundaries match your existing RBAC, prompts and context are scoped per tenant where needed, and we design for audit logging on sensitive actions. Data handling follows your policies — we don't train models on your customer data unless you explicitly require it.
Which LLM providers do you support?
OpenAI, Anthropic, Google Gemini, and self-hosted models via an abstraction layer in your codebase. That lets you swap or route providers without rewriting product features — useful for cost control, compliance, or failover.
We already built a POC — can you productionize it?
Yes. That's a common starting point. We assess what's there, harden the integration path (rate limits, observability, evals, fallbacks), and get it behind proper auth and deployment practices so it survives real traffic and your eng team's review bar.
How is pricing structured?
Scoped engagements — typically a fixed fee per phase (audit, build, operate) based on complexity and timeline. We'll outline options after the technical assessment so you have a clear estimate before committing to implementation work.
Ready to map an integration path for your stack? Request a technical assessment
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