HomeServicesReal AI in real products

Real AI in real products — not wrappers, not demos.

We build production AI systems that solve actual business problems. LLM integrations, custom models, intelligent automation — embedded into your product, monitored in production, improving over time.

What we build

Software for every layer of your business.

LLM Integrations

ChatGPT, Claude, and open-source LLMs integrated into your product with proper context management, fallbacks, and cost controls.

Custom ML Models

Domain-specific machine learning trained on your data — computer vision, NLP, classification, and forecasting.

AI Agents

Autonomous agents that complete multi-step tasks — research, data extraction, workflow automation, and decision support.

Recommendation Systems

Personalisation and recommendation engines for e-commerce, content platforms, and SaaS products.

AI-Powered Search

Semantic search, RAG pipelines, and intelligent document processing built for your specific data.

Intelligent Automation

AI that replaces repetitive manual work — document processing, data extraction, triage, and smart routing.

Who we work with

Built for founders, CTOs, and product leaders.

SaaS companies

Add AI features to existing products — copilots, smart summaries, semantic search, and intelligent routing.

E-commerce businesses

Product recommendations, demand forecasting, fraud detection, and customer service automation.

Healthcare companies

Clinical decision support, document processing, patient intake automation, and anomaly detection.

Fintech companies

Fraud detection, credit scoring, AML pattern recognition, and document verification.

Enterprise teams

Internal knowledge management, process automation, and intelligent document workflows.

Non-technical founders

We evaluate where AI genuinely adds value and build it properly — no hype, no wasted budget.

Our process

How we deliver. Step by step.

01

Assessment

We evaluate where AI genuinely adds value vs. where it's hype. Not every problem needs ML. We identify the use cases with real ROI for your specific product.

AI opportunity auditUse case prioritisationData readiness assessmentBuild vs. buy recommendation
02

Data Strategy

Good models need good data. We design your data pipeline, handle labeling strategies, and ensure you're building on a foundation that scales.

Data pipeline architectureLabeling & annotation strategyData quality frameworkTraining dataset preparation
03

Model Development

We build, train, and fine-tune models for your specific domain — whether LLM integration, computer vision, or custom ML. Evaluated on real metrics, not toy benchmarks.

Trained model with evaluationPrompt engineering docsModel performance reportInference optimisation
04

Integration

The model goes into your product — not a separate tool. We embed AI where it creates real workflow value, with graceful fallbacks for when it's uncertain.

Production AI integrationFallback logic designAPI endpoints for AI featuresEnd-user testing report
05

Monitoring

AI in production drifts. We set up monitoring for model performance, data distribution shifts, and user feedback loops so quality holds over time.

Model monitoring dashboardDrift detection setupFeedback loop implementationRetraining runbook

Investment

An investment, not a line item.

AI is judged on return, not novelty. The right use case pays back in hours automated, decisions improved, or revenue unlocked — the wrong one burns budget on a demo. We scope for ROI first, then build. What an AI project takes depends on these factors.

How we work together

Fixed Scope

Well-defined projects

We agree the deliverables and price upfront. You get budget certainty; we carry the estimation risk.

Most Popular

Time & Materials

Evolving requirements

You pay for time spent, see working software every sprint, and change direction without renegotiating a contract.

Dedicated Team

Ongoing product work

A committed team works as an extension of yours, billed monthly, with full flexibility on what gets built.

What shapes your estimate

Use case complexity

A single classification feature is very different from an autonomous multi-step agent.

Data readiness

Clean, labelled data lowers cost; messy or missing data means pipeline work first.

Model approach

Using a hosted LLM API differs sharply from training a custom model on your data.

Accuracy requirements

Higher stakes mean more evaluation, guardrails, and human-in-the-loop design.

Integration depth

Embedding AI into your product's core workflow versus running it as a standalone tool.

Monitoring & retraining

Production AI drifts — ongoing monitoring is part of the real cost, not an extra.

The only way to a real number is a real conversation. Tell us what you're building — we'll scope it and send a clear, itemised estimate. No obligation.

Get your estimate

Why DualTech Labs

Three things clients tell us matter most.

You own the code. Always.

Full source code ownership transfers on delivery. No lock-in, no licensing fees. Take it to any team at any point.

One team, start to finish.

Design, engineering, and QA under one roof. No handoffs, no miscommunication between agencies.

Real demos every two weeks.

You see working software from sprint one. No black-box delivery. No surprises at the end of a long engagement.

FAQ

Common questions. Direct answers.

Start with a conversation.

Tell us what you're building. We'll scope it, tell you exactly how we'd approach it, and give you a clear estimate — no commitment, no agency fluff.