Arius Automation

Services > Custom AI Integrations

CUSTOM AI INTEGRATIONS

AI that fits the way your stack works

Off-the-shelf AI tools were not designed for your workflows. We integrate LLMs, vision models, voice AI, and embedding systems directly into your product and internal stack, so intelligence becomes part of how your business operates rather than a separate interface your team has to context-switch into.

IS THIS THE RIGHT SERVICE

When to pick this service

Pick this if

  • You need AI inside an existing product or internal tool, not a standalone chatbot
  • Your workflow has specific data contracts and the AI output needs to match them precisely
  • You are calling multiple AI services as a coordinated system
  • You need cost control, rate limiting, and PII handling baked into the integration layer
  • You have tried a plug-in AI tool and found it does not fit how your process actually works

Different page if

INTEGRATION PATTERNS

Five ways AI plugs into a stack

01

In-product AI

AI that lives inside a SaaS product or web app. Users interact with it directly. Outputs appear in the UI in real time or near real time.

Examples: Chat completions, inline suggestions, generative summaries, scoring widgets

02

Backend AI services

AI that runs as a microservice or serverless function with no user-facing component. Other services call it programmatically and consume the output.

Examples: Classification APIs, enrichment endpoints, scoring services, async processing jobs

03

AI in workflows

AI as a step inside an automation or business process. Triggered by an event, returns structured output, and hands off to the next step.

Examples: Trigger-based LLM calls in n8n or Make, AI decision nodes, conditional routing

04

Generative features

AI that generates content on demand. The output is text, structured data, or a document that gets used by a human or another system.

Examples: Proposal drafts, report generation, email personalization, product descriptions

05

Specialized models

Non-LLM AI: voice transcription, image classification, document OCR, embedding generation. Used as narrow-purpose services inside a larger system.

Examples: Whisper transcription, vision classifiers, embedding pipelines, TTS with ElevenLabs

ARCHITECTURE

Where AI plugs in

Every integration touches one or more of these four layers. We assess which layers are involved, design the integration interface, and build to the data contracts each layer expects.

Frontend / UI

React, Next.js, web apps, mobile, dashboards

API layer

REST, GraphQL, webhooks, custom middleware

Workflow

n8n, Make, Zapier, custom orchestration

Data

Postgres, Supabase, Airtable, CRMs, cloud storage

What AI we attach at each layer

Frontend / UI

Streaming completions, inline suggestions, generative UI components

API layer

LLM microservices, classification endpoints, enrichment APIs

Workflow

AI decision nodes, trigger-based LLM calls, structured output routing

Data

Embedding pipelines, vector indexes, AI-enriched records

CASE STUDIES

Real projects we have shipped

B2B SaaS

Proposal generation inside a deal management tool

A sales SaaS needed AI inside their deal flow. We built a proposal generation service that pulls CRM fields, runs a Claude prompt chain, and returns a structured draft. The draft appears inside the existing UI with no new tab and no copy-paste required.

Property Management

Lease clause extraction and anomaly flagging

A property management platform ingests 50 or more lease documents per week. We built a processing service that extracts key clauses and flags unusual terms with a plain-language explanation before a human reviews.

Manufacturing

Defect image classification and routing

A manufacturer needed defect images classified and routed to the right inspection team. We integrated a vision model into their warehouse management system. Defects are classified, documented, and assigned without manual triage.

Professional Services

Meeting intelligence pipeline

A professional services firm wanted structured notes and action items from every client call. We built a pipeline using Whisper for transcription, Claude for structuring, and a webhook that creates follow-up tasks in HubSpot.

Fintech

Transaction narrative generation

A fintech platform needed natural-language descriptions of transactions for their mobile feed. We integrated GPT-4o Mini into their transaction processing service. Each transaction gets a plain-English narrative generated at ingestion time.

MODELS

Which models we work with

We are model-agnostic. The right model for your integration depends on the task, the latency budget, and the cost at your volume. We assess and recommend during scoping.

Reasoning and instruction following

Claude (Anthropic)GPT-4o (OpenAI)Gemini 1.5 Pro (Google)

Vision and multimodal

GPT-4o VisionClaude (vision)Gemini Vision

Voice and transcription

Whisper (OpenAI)DeepgramAssemblyAIElevenLabs

Embeddings and semantic search

text-embedding-3-large (OpenAI)Voyage AICohere

Fast and low cost

GPT-4o MiniClaude HaikuGemini FlashMistralLlama

BUILD VERSUS BUY

Build, buy, or DIY

What we do

Build Custom

  • Fits your data contracts exactly
  • Lives inside your existing product
  • You own the prompts and the code
  • Cost per call is yours to control
  • No vendor lock-in on the AI layer

Right for teams with an existing product and a specific workflow problem.

Buy a Product

  • Fast to start, limited to the vendor's use cases
  • Output goes to the vendor's interface, not yours
  • Vendor controls the model and the pricing
  • Works until your workflow outgrows it
  • Hard to customize past what the UI allows

Right for early teams exploring before committing to a build.

DIY on API

  • Full flexibility, full responsibility
  • Requires in-house AI engineering time
  • Production hardening is on you
  • Cost monitoring and error handling are on you
  • Works well if you have the engineering capacity

Right for engineering teams who want control and have the bandwidth.

WHAT WE BUILD IN BY DEFAULT

Security, cost, and control

Security

  • PII detection and redaction before sending to external models
  • Input and output content filtering
  • Auth and tenant isolation on every API call
  • Secrets in environment variables, not in code
  • Audit logs for every AI call in regulated environments

Cost

  • Per-user and per-endpoint spend caps
  • Response caching where output is deterministic
  • Prompt compression to reduce token counts
  • Model routing: expensive models only when the task requires it
  • Spend monitoring with alerts before you hit thresholds

Control

  • You own the prompts and all configuration
  • Model-agnostic architecture: swap providers without rewriting
  • Versioned prompts with rollback
  • Feature flags for gradual rollout of AI features
  • Full observability: logs, traces, and evals per request

COMMON QUESTIONS

Questions we hear a lot

Q01

What does 'custom AI integration' mean vs just using an AI API?

Using an AI API means you have a key and can make calls. A custom AI integration means the AI is wired into your data, your auth, your error handling, and your downstream systems. It means the output lands in the right place in your stack without manual copy-paste. It means costs are monitored, PII is handled, rate limits are respected, and the integration does not break when the model provider makes a change. The API is the starting point. The integration is everything around it.

Q02

How do you choose which model to use?

We look at three things: what the task requires, what the latency budget is, and what the cost per call looks like at your volume. A task that needs strong reasoning and nuanced instruction following gets a frontier model (Claude, GPT-4o, Gemini 1.5 Pro). A task that needs to run fast and cheap at high volume gets a small model (GPT-4o Mini, Claude Haiku, Gemini Flash). A task that involves voice gets Whisper, Deepgram, or AssemblyAI. We are model-agnostic. We pick what fits the problem.

Q03

What does a typical engagement look like?

Most integrations run four to ten weeks. Week one is scoping: we map the current workflow, identify the AI touchpoints, and agree on the data contracts. Weeks two through four are the build phase: integration layer, prompts, validation, error handling. The final weeks cover testing against real production data, staging deployment, and handoff. Simpler integrations (a single generative feature inside an existing app) can run two to three weeks. Complex multi-model orchestrations take longer.

Q04

Can this work with our existing codebase?

Yes. We integrate at the API layer and work in whatever language your backend runs. Node, Python, Go, Ruby, and others. We do not require you to switch frameworks. We write clean, documented code that your team can read and maintain. If you use the Vercel AI SDK, we know it well. If you use a custom setup, we work with it.

Q05

What about cost? AI APIs can get expensive.

We model the economics before we build. Every integration gets a cost estimate based on your expected call volume, the average token count per request, and the model pricing. We build in cost guardrails: per-user spend caps, request caching where safe, prompt compression, and model routing that sends simple tasks to cheaper models. After launch we set up spend monitoring with alerts. Surprise bills do not happen when the architecture is designed with cost in mind from the start.

Q06

How do you handle latency?

It depends on the use case. If users are waiting for a response in real time, we use streaming, fast models, and server-side rendering to get time-to-first-token under two seconds for most requests. If the integration is async (the AI runs in the background and the result appears later), latency is not the constraint and we optimize for quality instead. We set expectations during scoping and design around the actual user experience, not just the API response time.

Q07

Who owns the integration after you build it?

You own everything. All code, all prompts, all schemas. We deliver the integration in your repository, with documentation your team can follow. If you want us to stay on for monitoring and tuning, we offer retainers. If you want to hand it to your internal team, we do a proper handoff with a full walkthrough. We do not build integrations that only we can maintain.

Q08

What is the minimum viable first project?

Pick one workflow that has a clear input and a clear desired output, that runs at enough volume to be worth automating, and where a human is currently doing repetitive interpretation or generation work. A good first project is narrow enough to ship in two to four weeks, measurable enough to prove value, and representative enough of your broader stack that it teaches us what a second project would look like. We will help you find it during the scoping call.

GET STARTED

Tell us the system you want AI inside

Book a scoping call. Walk us through your stack and the workflow you want to change. We will tell you what is realistic, what it will cost to run, and how long it takes to build.

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