What's the difference between using ChatGPT and building custom AI?

ChatGPT is a general consumer product with no access to your data or systems. Custom AI is built on your documents, integrated into your workflows, and controlled by your business rules — it does something specific for your company, not everything for everyone.

Retrieval-Augmented Generation grounds a model's answers in your actual documents and data instead of relying on what it memorized during training, so responses are accurate and traceable to a real source.

Can AI use our company documents?

Yes. We build retrieval systems that index your documents so the AI can search and cite them directly, without retraining the underlying model.

Can AI access our ERP system?

Yes, through an API integration. We connect AI features to ERP, CRM, and other internal systems using the access controls and permissions your data already requires.

Can AI automate our workflows?

Yes. We build automation that triggers real actions — creating records, routing requests, sending notifications — not just generating text for a person to act on manually.

How secure are AI systems?

Security is part of the architecture from the start — data isolation, access controls, and prompt-injection defenses, not something added after a review.

How long does AI implementation take?

A focused RAG or single-agent system typically takes 6–10 weeks. Multi-system integrations or several coordinated agents take longer, depending on scope.

What does AI development cost?

Cost depends on model usage, integration complexity, and how many systems the AI needs to connect to. We provide a detailed estimate after an initial scoping conversation.

Can AI answer customer questions directly?

Yes, when the use case fits — grounded in your documentation, with clear escalation to a human for anything outside its confidence.

Can AI replace employees?

AI is best at removing repetitive, well-defined work so your team can focus on judgment calls it can't make. We don't position AI as employee replacement — we design it around what it's actually reliable at.

Which LLM should we choose?

It depends on your latency, cost, context length, and data residency requirements. We recommend a specific model after reviewing your use case, and architect the system so switching providers later is a configuration change, not a rewrite.

Can AI be integrated into our existing software?

Yes. We integrate AI features into your existing product through APIs, rather than requiring a separate standalone tool your team has to switch between.

AI Development

We design and build production-ready AI systems, LLM integrations, AI agents, RAG platforms, workflow automation and intelligent business software.

Start a Project See how we work

OpenAI
Claude
RAG
AI Agents
Prompt Engineering

Estimated timeline: Typically 6–10 weeks for a production RAG or single-agent system, longer for multi-system integrations
Platforms: Cloud · On-Premise · Hybrid
Tech stack: OpenAI · Claude · LangChain · Python

Overview

Our approach

We design and build production-ready AI systems, LLM integrations, AI agents, RAG platforms, workflow automation and intelligent business software.

Production AI
Real AI systems integrated into business workflows, not a chatbot demo that never ships.
LLM Architecture
Model selection, orchestration, and retrieval designed before implementation begins.
Scalable Deployment
Monitoring, evaluation, cost optimization, and continuous improvement built in from launch.

What's Included

Everything under one roof

Everything included in this engagement, from architecture to long-term support — one team, one system.

LLM Integrations

Connecting OpenAI, Claude, Gemini, or any model provider into your existing product and internal tools.

AI Agents

Autonomous agents that plan, call tools, and complete multi-step tasks, not just answer a single question.

Retrieval-Augmented Generation

Grounding model responses in your own documents and data, so answers are accurate, not hallucinated.

Prompt Engineering

Structured, tested, and versioned prompts — treated as production code, not throwaway text.

Workflow Automation

Replacing manual, repetitive processes with AI-driven pipelines that trigger real actions in your systems.

Internal AI Assistants

Purpose-built assistants for your own team — support, sales, or operations — trained on your internal knowledge.

Document Intelligence

Extracting, classifying, and summarizing information from contracts, forms, and unstructured documents.

Vector Databases

Embedding and indexing your data for fast, accurate semantic search at scale.

Model Evaluation

Systematic testing of model outputs against real scenarios, before and after every change.

Fine-Tuning Strategy

Deciding when fine-tuning is worth it versus prompting or retrieval — and building it correctly when it is.

Monitoring

Real-time visibility into cost, latency, and output quality once an AI feature is live.

AI Maintenance

Ongoing prompt updates, model migrations, and performance tuning as providers and requirements change.

Why Aixo Lab

Why companies choose Aixo Lab

AI Architecture First
Model selection, retrieval strategy, and system boundaries are designed before a single prompt is written — not bolted onto an existing product as an afterthought.
Production Reliability
AI features are built with fallbacks, evaluation, and monitoring from day one, so a model hiccup doesn't become a customer-facing outage.
Vendor Independence
Architected so you're never locked into a single model provider — swapping OpenAI for Claude or Gemini is a configuration change, not a rewrite.
Security by Design
Prompt injection defenses, data isolation, and access controls are part of the architecture, not a patch applied after a security review.

Technology Stack

Built on a modern, production-grade stack

Every technology here is a deliberate choice, not a default.

OpenAI

GPT models for general-purpose reasoning, generation, and function calling.

Claude

Anthropic's models for longer-context reasoning and careful, structured output.

Gemini

Google's multimodal models, useful when a project needs native image or video understanding.

LangChain

Framework for chaining LLM calls, tools, and memory into a coherent agent or pipeline.

LlamaIndex

Data framework for connecting LLMs to your own documents and structured data sources.

Pinecone

Managed vector database for high-scale semantic search in production.

Qdrant

Open-source vector database, self-hosted when data residency requirements apply.

PostgreSQL

Primary relational database, often paired with pgvector for smaller-scale retrieval.

Node.js

Runtime for the APIs and services that connect AI features to the rest of the product.

Python

The default language for model orchestration, evaluation, and data pipelines.

FastAPI

Python framework for building the internal APIs AI services run behind.

Docker

Containerizes AI services so they deploy identically across environments.

AWS

Cloud infrastructure for hosting, storage, and scaling AI workloads.

Azure OpenAI

Enterprise-grade OpenAI access with the compliance and data controls Azure customers require.

Redis

Caching and session storage to keep AI-powered features fast under load.

Process

How we work

The same disciplined process behind every engagement, from the first architecture decision to launch.

01
Discovery
Understand the business problem and its real constraints.
Output:
Scope and goals document
Your involvement:
Initial workshop
02
Product definition
Translate the problem into concrete product requirements.
Output:
Feature spec and priorities
Your involvement:
Requirements review
03
UX/UI design
Design user flows and interface before development starts.
Output:
Wireframes and design system
Your involvement:
Design feedback
04
Technical architecture
Define system structure, data flow, and technology stack.
Output:
Architecture document
Your involvement:
Technical review (optional)
05
Iterative development
Build in short cycles with visible, regular progress.
Output:
Regularly shipped working versions
Your involvement:
Sprint review participation
06
Quality assurance
Test functionality, performance, and security before release.
Output:
Test results and fixes
Your involvement:
Acceptance sign-off
07
Launch
Deploy to production with a rollback plan in place.
Output:
Product deployed to production
Your involvement:
Launch approval
08
Continuous improvement
Monitor, maintain, and evolve the product after launch.
Output:
Maintenance and improvement roadmap
Your involvement:
Regular check-in meetings

Industries

Built for real operating constraints

Every industry has its own compliance, integration, and scale requirements — we design for the ones that apply to you.

Featured Project

See it in practice

AI Operations Platform

An AI platform combining OpenAI, Claude, RAG, and internal APIs to automate document processing, semantic search, and decision support for a company managing thousands of operational requests.

Challenge

The company managed thousands of operational documents, emails, and customer requests manually, with no way to search across them or triage incoming requests without a person reading every one.

Solution

We built an AI platform combining OpenAI, Claude, RAG, and internal APIs to automate document processing, semantic search, and decision support — routing requests, surfacing relevant documents, and drafting responses for human review.

Results

70% reduction in manual work
95% faster document retrieval
Thousands of requests processed automatically

Technologies

OpenAI
Claude
Node.js
Python
PostgreSQL
Vector Database

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FAQ

Frequently asked questions

Resources

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The stack pieces most relevant to a project like this one.

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AI Agent Development

We build enterprise AI agents — systems that plan, call tools, and take real action in your existing software — architected for reliability, oversight, and production use, not chat demos.

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REST API Development

We design and build enterprise REST APIs — resource modeling, authentication, versioning, and rate limiting architected as long-term contracts, not endpoints assembled to make one client work.

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