AI Integration & Automation

AI that actually does something. Not an AI checkbox on your marketing page.

The gap between "AI could help with this" and "AI is helping with this" is engineering. We bridge it — integrating LLMs, building ML models, and automating workflows where AI adds genuine value to your product or operations.

Explore AI for Your Business

LLM integration

ChatGPT, Claude, Gemini — integrated into your product, not a standalone chatbot

Custom ML models

Trained on your data for predictions, classification, and recommendations

Document intelligence

Extracting, classifying, and understanding documents automatically

Workflow automation

Replacing manual review, data entry, and routing with intelligent automation

The Problem

AI is everywhere in the news. Nowhere in your operations.

Your board keeps asking about your AI strategy. Your competitors have “AI-powered” everything on their website. Your team has experimented with ChatGPT prompts and maybe built a prototype chatbot during a hackathon. But none of it has made it into production. The gap isn’t ambition — it’s execution.

The challenges are practical. How do you connect an LLM to your proprietary data without exposing it? How do you make AI responses reliable enough that customers can trust them? How do you handle the cases where the AI gets it wrong? How do you control costs when API calls are priced per token? And how do you measure whether any of this is actually worth the investment?

Then there’s the talent problem. AI engineering is a specific skill set. Your web developers — however talented — didn’t train in prompt engineering, RAG architecture, or ML model evaluation. Hiring dedicated AI engineers for what might be a three-month project doesn’t make financial sense.

We take a practical approach to AI. We identify the specific use cases where AI will save time, reduce costs, or improve your product. We build it into your existing systems — not as a separate “AI platform” that nobody uses. And we make sure it works reliably in production, not just in a demo.

What We Deliver

AI features that work in production, not just in demos.

From LLM integration to custom ML models — practical AI that solves specific business problems.

Intelligent Search & RAG

Semantic search across your knowledge base, product catalogue, or documentation. Retrieval-augmented generation that gives accurate, sourced answers from your own data instead of hallucinating.

Conversational AI

Chatbots and virtual assistants grounded in your actual product data, support history, and documentation. They handle routine queries, triage complex issues, and escalate to humans when needed.

Document Processing

Extracting data from invoices, contracts, forms, and emails. Classifying documents automatically. Summarising long reports. Turning unstructured documents into structured data your systems can use.

Prediction & Classification

ML models for lead scoring, churn prediction, demand forecasting, anomaly detection, and content categorisation. Trained on your data, validated rigorously, and deployed with monitoring.

Workflow Automation

Replacing manual review, data entry, approval routing, and content moderation with intelligent automation. Human-in-the-loop where confidence is low, fully automated where it's high.

Product AI Features

Recommendation engines, personalisation, auto-tagging, smart suggestions, and AI-powered content generation. Features that give your product a competitive edge through genuine intelligence.

Tech Stack

AI and automation tools we work with

From foundation model APIs to custom training pipelines — the tools for practical AI.

LLM Providers

O/ OpenAI / GPT

Anthropic Claude

Google Gemini

Hugging Face

OL Ollama

ML & Data

Python

PyTorch

TensorFlow

SC scikit-learn

LangChain

Vector & Search

PC Pinecone

WE Weaviate

CH ChromaDB

Elasticsearch

Infrastructure

AWS SageMaker

Vertex AI

Docker

FastAPI

Who This Is For

Companies ready for practical AI

AI is most valuable when applied to specific, well-defined problems — not as a broad initiative without clear goals.

SaaS & Product Companies

Products that want to embed AI features — smart search, recommendations, content generation — to create competitive differentiation and improve user experience.

Document-Heavy Industries

Legal, insurance, finance, and healthcare companies that process large volumes of documents and need automated extraction, classification, and summarisation.

Customer Support Teams

Companies handling high volumes of support queries that want AI-powered triage, automated responses for common questions, and intelligent escalation.

Data-Rich Businesses

Companies sitting on historical data that could be used for prediction, scoring, or anomaly detection — but haven't built the models yet.

Manual Process Owners

Teams with repetitive, rule-based workflows — data entry, content moderation, approval routing — where intelligent automation would save significant hours.

AI Curious Companies

Organisations that know AI could help but don't know where to start. We help identify the use cases with the clearest ROI before building anything.

FAQs

Common questions about AI integration

What clients ask when considering AI for their product or operations.

Do we need a large dataset to start using AI?

Not necessarily. LLM features like search, document processing, and chatbots work with your existing data through RAG. Custom ML models need training data, but we help evaluate whether yours is sufficient before committing.

Can you add AI features to our existing product?

Yes. Most of our AI work is integration — adding smart search, building an assistant, or automating a manual process within your existing app and infrastructure.

Which LLM providers do you work with?

OpenAI, Anthropic Claude, Google Gemini, and open-source models via Hugging Face or self-hosted. The choice depends on your use case, privacy needs, and cost constraints.

How do you handle data privacy with AI?

For sensitive data, we use self-hosted or cloud-isolated models so data never leaves your infrastructure. With third-party APIs, we implement data redaction and anonymisation per your privacy policies.

Is AI actually worth the investment for our size?

It depends on the use case, not company size. Document automation, intelligent search, and workflow automation often show clear ROI even for smaller companies. We help identify where AI adds genuine value versus where simpler automation would work.

Can you build AI-powered chatbots for customer support?

Yes. We build chatbots grounded in your knowledge base using RAG. They handle common queries, escalate complex issues, and improve over time based on interactions.