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Aixo LabAixo Lab

OpenAI Integration

We integrate OpenAI models into enterprise software — AI assistants, retrieval-augmented search, and document intelligence architected for accuracy, cost control, and production reliability.

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  • RAG
  • Function Calling
  • Structured Outputs
  • Vision & Voice
  • Enterprise Security
Overview

Our approach

We integrate OpenAI models into enterprise software — AI assistants, retrieval-augmented search, and document intelligence architected for accuracy, cost control, and production reliability.

  • State-of-the-Art Language Models

    Access to OpenAI's model family for reasoning, generation, and multimodal tasks, applied where they solve a real problem.

  • Retrieval-Augmented Accuracy

    Responses grounded in your own data instead of the model's training data alone, which is what actually reduces hallucination.

  • Structured, Reliable Outputs

    Schema-conformant responses your application can parse and act on directly, not free text you have to guess at.

  • Seamless Tool & System Integration

    Function calling that lets a model take real action in your existing systems, not just describe what it would do.

  • Multimodal Capabilities

    Text, vision, and voice combined where a product genuinely needs more than one modality, not by default.

  • Enterprise Security & Governance

    Data handling, access control, and audit logging built around the same standards as the rest of your system.

What's Included

Everything under one roof

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

Enterprise AI Assistants

Internal assistants that can answer questions and take action against your own systems, not a generic chat widget.

Customer Support AI

Support automation grounded in your actual documentation and policies, with a clear handoff to a human when it matters.

Knowledge Base Search

Semantic search over internal documentation and data that returns relevant answers, not just keyword matches.

Document Processing

Extraction and structuring of data from contracts, invoices, and forms that used to require manual review.

AI Automation

AI-assisted steps embedded in existing operational workflows, not a separate tool your team has to remember to use.

Internal Copilots

Tools that help your own team move faster on real tasks, integrated with the systems they already work in.

Content Generation

Drafting and generation workflows with a human review step built in, not fully automated publishing.

Voice AI

Speech-to-text and text-to-speech integrated into workflows where voice is genuinely the right interface.

Vision AI

Image understanding for document scans, product photos, and visual quality checks.

Workflow Automation

Multi-step processes that combine AI reasoning with deterministic logic where each is actually the right tool.
Why Aixo Lab

Why companies choose Aixo Lab

  1. We Architect for Accuracy, Not Just Demos

    Retrieval grounding and evaluation are designed from the start, because a model that looks impressive in a demo and hallucinates in production isn't a solution.

  2. We Integrate AI Into Real Systems, Not Sandboxes

    Function calling connects a model to your actual databases and APIs, so it can take real action instead of just describing what it would do.

  3. We Design for Cost and Latency From Day One

    Model selection, caching, and prompt design are architecture decisions made during planning, not a bill you're surprised by after launch.

  4. We Build Guardrails Before We Ship

    Input validation, output checks, and fallback behavior are part of the initial build, not a patch added after something goes wrong.

  5. We Hand Off Code Your Team Can Own

    Clear architecture and documentation mean your own engineers — or ours, later — can extend this system without archaeology.

Our Capabilities

Our OpenAI Capabilities

The specific technical capabilities behind every OpenAI integration — not a generic feature list, the actual engineering surface we work in daily.

GPT-4o

OpenAI's multimodal flagship model, applied to reasoning, generation, and vision tasks where it's the right fit.

GPT-5

OpenAI's newer model family, evaluated and adopted where it measurably improves a specific task — not assumed to be a universal upgrade.

AI Assistants

Assistants with defined scope, tool access, and escalation paths, built for a specific job rather than open-ended chat.

AI Chatbots

Conversational interfaces grounded in real data sources, with clear boundaries on what they can and can't do.

Enterprise Copilots

AI tools embedded directly in the software your team already uses, not a separate app competing for attention.

Retrieval-Augmented Generation (RAG)

Responses grounded in retrieved, relevant context from your own data instead of the model's training data alone.

Semantic Search

Search that matches meaning and intent, not just keywords, built on vector embeddings of your content.

Document Intelligence

Structured extraction from unstructured documents — contracts, invoices, forms — with confidence scoring.

Function Calling

Models that call real functions in your codebase, turning a conversation into an action with actual consequences.

Structured Outputs

Schema-enforced JSON responses your application can trust and parse directly, without brittle text parsing.

Vision Models

Image understanding for document scans, diagrams, and visual data your product needs to interpret.

Speech-to-Text

Reliable transcription for voice interfaces, call analysis, and accessibility features.

Text-to-Speech

Natural-sounding voice output for interfaces where reading isn't the right interaction model.

Image Generation

Generated imagery for workflows where it adds real product value, with the licensing and quality tradeoffs made explicit.

AI Workflow Automation

Multi-step pipelines combining model calls, tool use, and deterministic logic into a single reliable process.
Process

How we work

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

  1. 01
    Discovery

    Understand the business problem and its real constraints.

    Output:
    Scope and goals document
    Your involvement:
    Initial workshop
  2. 02
    Product definition

    Translate the problem into concrete product requirements.

    Output:
    Feature spec and priorities
    Your involvement:
    Requirements review
  3. 03
    UX/UI design

    Design user flows and interface before development starts.

    Output:
    Wireframes and design system
    Your involvement:
    Design feedback
  4. 04
    Technical architecture

    Define system structure, data flow, and technology stack.

    Output:
    Architecture document
    Your involvement:
    Technical review (optional)
  5. 05
    Iterative development

    Build in short cycles with visible, regular progress.

    Output:
    Regularly shipped working versions
    Your involvement:
    Sprint review participation
  6. 06
    Quality assurance

    Test functionality, performance, and security before release.

    Output:
    Test results and fixes
    Your involvement:
    Acceptance sign-off
  7. 07
    Launch

    Deploy to production with a rollback plan in place.

    Output:
    Product deployed to production
    Your involvement:
    Launch approval
  8. 08
    Continuous improvement

    Monitor, maintain, and evolve the product after launch.

    Output:
    Maintenance and improvement roadmap
    Your involvement:
    Regular check-in meetings
Technology Stack

Built on a modern, production-grade stack

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

OpenAI API

The core model access layer for reasoning, generation, vision, and voice capabilities.

React

The frontend layer for AI-powered interfaces that need a rich, interactive client experience.

Next.js

A full-stack framework for AI features that need both a frontend and server-side model orchestration.

Node.js

A backend runtime well suited to streaming AI responses and orchestrating tool calls.

Laravel

An enterprise backend framework for AI features integrated into existing business applications.

Python

The language of choice for data-heavy AI workloads, evaluation pipelines, and custom model tooling.

PostgreSQL

The relational database of choice, often paired with pgvector for combined relational and vector storage.

Redis

Caching for model responses and embeddings, reducing cost and latency for repeated queries.

Vector Databases

Purpose-built storage and indexing for embedding-based semantic search at scale.

Pinecone

A managed vector database for retrieval-augmented generation at production scale.

pgvector

Vector similarity search directly inside PostgreSQL, when a separate vector database isn't justified.

Docker

Containerized builds for consistent environments across development, staging, and production.

AWS

Cloud infrastructure for applications that need more control than a managed platform alone provides.
Enterprise AI Features

Enterprise AI Features

The architecture decisions that determine whether an OpenAI integration is accurate, affordable, and safe to run in production, not just a working prototype.

Prompt Engineering

Prompts treated as a versioned, tested part of the codebase, not a string tweaked until a demo looks good.

RAG Architecture

A retrieval pipeline designed around your actual data — chunking strategy, retrieval ranking, and context assembly.

Embeddings

Vector representations of your content chosen and maintained deliberately, not a one-time export nobody revisits.

Vector Search

Similarity search infrastructure sized and indexed for your actual data volume and latency requirements.

Tool Calling

Function definitions and execution boundaries designed so a model can act safely within defined limits.

Structured Outputs

Schema validation on every model response, so malformed output fails loudly instead of breaking downstream silently.

Guardrails

Input and output filtering, scope limits, and fallback behavior designed before launch, not added after an incident.

Caching

Response and embedding caching that cuts cost and latency for the requests that don't need a fresh model call.

Monitoring

Visibility into what the model is actually doing in production — inputs, outputs, latency, and failure modes.

Evaluation

Systematic testing against real examples, so quality regressions are caught before users see them, not after.

Cost Optimisation

Model selection, prompt length, and caching strategy tuned deliberately against actual usage patterns.

Security

Data handling, access control, and prompt injection defenses built to the same standard as the rest of the system.
Representative Solutions

Where this technology fits

Reference architectures from our Representative Solutions collection that could plausibly be built on this stack.

AI Operations Platform
Representative Solution

AI Operations Platform

Operations & AI
Customer Support AI
Representative Solution

Customer Support AI

Customer Experience
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