AI Integration That Works With Your Existing Systems, Not Against Them

Embed AI capabilities into the applications, workflows, and infrastructure you already operate. No rip-and-replace. Practical AI that delivers measurable value within your current technology environment.

Why AI Integration Needs Engineering, Not Just Consulting

Most companies want AI to improve what they already have, not rebuild from scratch. We connect AI to your existing systems through clean APIs and data pipelines - not slide decks.

Traditional AI Consulting

Assessment & Recommendations

Weeks of discovery producing a strategy deck. Use-case prioritization matrices. Roadmaps with no working code attached.

Proof of Concept

Isolated PoC built on clean sample data. Works in a sandbox. Disconnected from your production systems and real data quality.

Custom Build from Scratch

Rip-and-replace approach. Existing systems treated as legacy. Months of development before any integration touches production.

Integration as Afterthought

Connecting the AI model to your systems happens last. Data format mismatches, latency issues, and authentication problems surface late.

Handoff & Documentation

Project delivered. Consultants leave. Your team inherits a system they did not build and cannot easily maintain or optimize.

Engineers Validated

AI-Native Engineering Approach

API-First Integration Design

Start with your existing systems. Map data flows, identify integration points, and design AI capabilities that connect - not replace.

Data Pipeline Engineering

Build the data pipelines that feed models and return results to production systems. Handle data quality, format, and latency from day one.

AI Layer Into Existing Workflows

AI capabilities deployed as services your existing applications call. Your systems stay intact. AI adds intelligence without disruption.

Cost & Performance Monitoring

API costs tracked, model performance measured, and integration health monitored. We watch our own bills — we will watch yours.

Your Team Owns the System

Built to be maintained by your team, not dependent on us. Clean code, documentation, and knowledge transfer from engineers, not consultants.

What We Build

LLM Integration

Connect OpenAI, Claude, or Gemini models to your application for intelligent search, content generation, summarization, and natural language processing.

Predictive Analytics & ML Models

Custom models for demand forecasting, churn prediction, pricing optimization, anomaly detection. Built on your data, deployed in your environment.

Computer Vision

Image classification, object detection, OCR, visual inspection. Applications in infrastructure planning, document processing, quality control.

Data Pipeline Development

ETL pipelines that prepare your data for AI consumption. Cleansing, transformation, enrichment from multiple sources into AI-ready formats.

AI-Powered Search & Recommendations

Semantic search using vector embeddings and recommendation engines that understand intent, not just keywords.

Legacy System AI Augmentation

Add AI to existing applications without rewriting them. API middleware, microservices, progressive enhancement.

Technology We Work With

AI/ML Frameworks

LLM APIs

Vector Databases

Data Processing

Databases

Integration

Our Work

HR Tech / HealthTech🇬🇧 United Kingdom

Built an AI-matched healthtech jobs marketplace with HRFlow.ai

Two-sided marketplace with candidate and employer web apps

HRFlow.ai match scoring on every job-candidate pair

Auth0 authentication and Stripe plan-based subscriptions

SEO-first architecture with Google Jobs structured data

AI-matched

Every job-candidate pair

Google Jobs

Structured data

Plan-based

Subscriptions

View Case Study

Education🇺🇸 USA

Integrated three AI features that reduced compliance effort by 97% for 15,000+ learners

Built Brain AI, a natural language knowledge base that answers student and staff queries instantly

Developed English Master, an adaptive language learning module with real-time pronunciation feedback

Integrated Two-Way Live Translation enabling multilingual communication across student populations

Created compliance document generation engine producing audit-ready reports automatically

97%

Compliance Effort Reduction

25%

Faster English Acquisition

15,000+

Multilingual Support for 15k+ Users

View Case Study

Logistics / B2B🇮🇳 India

Built two WhatsApp AI agents and optimised cloud costs for a B2B logistics operator

A WhatsApp voice agent that answers routine customer enquiries automatically

A WhatsApp B2B ordering agent capturing orders via text and voice with live inventory checks

AWS cloud infrastructure optimised for cost-performance

Both agents integrated into the existing logistics platform and workflows

2 agents

WhatsApp voice + B2B ordering live

AWS

Cloud optimised for cost-performance

Lower

Cost-to-serve on routine enquiries

View Case Study

Healthcare / Pharma🇸🇬 Singapore

Built the enterprise data layer connecting an ERP to an e-commerce marketplace

Xano data layer with REST endpoints and Redis caching between the ERP and the marketplace

GCP managed queue absorbs requests when the ERP is unavailable

Node.js compression service for the large JSON payloads pharma data produces

Per-file audit logging for a defensible pharma compliance posture

ERP to Market

Live integration backbone

Queue-backed

Resilient to ERP downtime

Singapore

Region deployment

View Case Study

Know AI could improve your business but not sure where it fits?

Talk to an Expert

Common Questions About AI Integration

Can you add AI to our existing application without rebuilding it?

Yes. Most of our AI integration work augments existing systems rather than replacing them. We connect AI models through API layers and microservices that sit alongside your current application. Your users get AI-powered features; your existing codebase stays intact.

Which types of AI integration deliver the fastest ROI?

Document processing, intelligent search, and content generation typically deliver the fastest returns because they automate high-volume, repetitive knowledge work. Predictive analytics follows closely when sufficient historical data exists. We assess your specific use case during the AI Adoption Discovery program to identify where AI will have the most measurable impact.

How do you handle data privacy and security?

We follow data minimization principles - only the data required for the AI feature is processed. For sensitive environments, we deploy models in your cloud environment or use private endpoints. Unico Connect is ISO 27001 and ISO 9001 certified, and we map projects to GDPR, HIPAA, and other regulated workload requirements as part of discovery. Data never leaves your control unless explicitly configured to do so.

What if our data is messy or incomplete?

Data quality is the foundation of useful AI. We build data pipelines that clean, transform, and enrich your data before it reaches AI models. We are also candid about when data quality is insufficient for a proposed use case - assessing data readiness is part of our AI Adoption Discovery.

Which AI models and providers do you work with?

We integrate with all major providers: OpenAI, Anthropic Claude, and Google Gemini/Vertex AI. We also build custom ML models using TensorFlow, PyTorch, and scikit-learn when off-the-shelf solutions do not meet accuracy or performance requirements. Provider selection is based on your specific needs - latency, cost, accuracy, data residency - not our preference.

How long does a typical AI integration project take?

A single-feature integration, such as adding semantic search to an existing application, takes 4-6 weeks. Multi-feature AI augmentation of an existing platform runs 8-16 weeks. Our AI Adoption Discovery program (3 weeks) helps scope the right integration points before committing to a full build.