"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringCustom AI Development
Ship AI That Actually Works in Production
We design, train, and deploy custom AI models — LLMs, computer vision, NLP, and predictive engines — built on your data, owned by you, and integrated into your existing systems from day one.
Our Services
The Full Spectrum of Custom AI
From raw data to production-grade models — we cover every discipline needed to turn AI ambition into shipped, revenue-generating product.
Custom LLM Development
Build and fine-tune large language models on your proprietary data for domain-specific accuracy that off-the-shelf models can't match.
AI-Powered Applications
End-to-end development of intelligent web and mobile applications with embedded AI capabilities, from prototyping to production.
Computer Vision Systems
Custom image recognition, object detection, and visual inspection systems that automate quality control and operational workflows.
Natural Language Processing
Domain-specific NLP pipelines for text classification, entity extraction, document intelligence, and semantic search at scale.
Predictive Analytics Engines
Custom ML models that forecast demand, detect anomalies, and surface actionable intelligence from your structured and unstructured data.
AI API & Integration Layer
Secure, scalable AI microservices and REST/GraphQL APIs that connect seamlessly to your existing ERP, CRM, and enterprise stack.
Why Choose Us
Built for Production, Not Just Demos
Four principles that separate genuine AI engineering from AI theatre.
Purpose-Built for Your Business
Off-the-shelf AI ignores your unique data. Custom models train on your history, domain vocabulary, and context — delivering 3× better accuracy out of the box.
Full IP Ownership
Every model, pipeline, and dataset we build belongs entirely to you. No vendor lock-in, no recurring licence fees, no shared model risk.
Production-Grade from Day One
We build for scale, reliability, and observability. CI/CD pipelines, monitoring dashboards, and automated drift detection are standard — not add-ons.
Cross-Stack Integration
Our AI layers connect to your ERP, CRM, data warehouse, or legacy systems via clean APIs — no rip-and-replace, no disruption to existing workflows.
Our Capabilities
Deep Tech, End-to-End Ownership
Six core engineering competencies that ensure your AI project ships on time, at scale, and continues to improve after go-live.
Fine-Tuning & RLHF
Adapt foundation models (GPT-4, Llama 3, Mistral) using your proprietary data with reinforcement learning from human feedback.
MLOps & Model Lifecycle
End-to-end model training, versioning, deployment, monitoring, and automated retraining pipelines on AWS, GCP, or Azure.
Data Engineering
Feature engineering, labelling pipelines, vector databases, and data lake architecture designed specifically for AI workloads.
Neural Architecture Design
Custom model architectures for structured data, time-series, NLP, and multimodal use cases beyond generic transformer defaults.
Model Evaluation & Safety
Red-teaming, bias testing, hallucination mitigation, and responsible AI frameworks for regulated and high-stakes environments.
Edge AI Deployment
Optimised models for on-device inference: ONNX export, INT8 quantisation, and deployment to IoT and edge hardware.
Our ApproachHow We Build
How We Build
Your Custom AI
A rigorous six-phase engineering process that takes you from problem definition to a self-improving production system.
01
Requirements & Discovery
02
Data Strategy & Preparation
03
Model Design & Prototyping
04
Training & Fine-Tuning
05
Deployment & MLOps
06
Optimise & Iterate
Step 01
Requirements & Discovery
We map your business problem to AI primitives — data availability, model type, accuracy thresholds, latency requirements, and integration touchpoints — before writing a single line of code.
Problem-to-AI MappingData Feasibility ReportTechnical Specification
Step 02
Data Strategy & Preparation
We audit, clean, label, and engineer features from your raw data sources — building the training foundation that directly determines model quality and business performance.
Data Audit ReportLabelling PipelineFeature Engineering Plan
Step 03
Model Design & Prototyping
Architecture selection, baseline modelling, and rapid iteration to prove feasibility and establish accuracy benchmarks before committing to full-scale training.
Baseline ModelAccuracy BenchmarksGo/No-Go Decision
Step 04
Training & Fine-Tuning
Full-scale model training with hyperparameter optimisation, RLHF where applicable, and rigorous evaluation against held-out test sets to ensure generalisation.
Trained Model ArtefactsEvaluation MetricsRLHF Alignment Report
Step 05
Deployment & MLOps
Containerised model serving via Docker/Kubernetes, API gateway integration, automated CI/CD, real-time monitoring dashboards, and alerting for production stability.
Production API EndpointCI/CD PipelineMonitoring Dashboard
Step 06
Optimise & Iterate
Continuous improvement through A/B testing, automated retraining triggers, feedback loops from production signals, and planned capability expansion.
A/B Testing FrameworkRetraining AutomationExpansion Roadmap
Our Approach
How We Build Your Custom AI
A rigorous six-phase engineering process that takes you from problem definition to a self-improving production system.
Step 01
Requirements & Discovery
We map your business problem to AI primitives — data availability, model type, accuracy thresholds, latency requirements, and integration touchpoints — before writing a single line of code.
Problem-to-AI MappingData Feasibility ReportTechnical Specification
Step 02
Data Strategy & Preparation
We audit, clean, label, and engineer features from your raw data sources — building the training foundation that directly determines model quality and business performance.
Data Audit ReportLabelling PipelineFeature Engineering Plan
Step 03
Model Design & Prototyping
Architecture selection, baseline modelling, and rapid iteration to prove feasibility and establish accuracy benchmarks before committing to full-scale training.
Baseline ModelAccuracy BenchmarksGo/No-Go Decision
Step 04
Training & Fine-Tuning
Full-scale model training with hyperparameter optimisation, RLHF where applicable, and rigorous evaluation against held-out test sets to ensure generalisation.
Trained Model ArtefactsEvaluation MetricsRLHF Alignment Report
Step 05
Deployment & MLOps
Containerised model serving via Docker/Kubernetes, API gateway integration, automated CI/CD, real-time monitoring dashboards, and alerting for production stability.
Production API EndpointCI/CD PipelineMonitoring Dashboard
Step 06
Optimise & Iterate
Continuous improvement through A/B testing, automated retraining triggers, feedback loops from production signals, and planned capability expansion.
A/B Testing FrameworkRetraining AutomationExpansion Roadmap
Use Cases
Custom AI Solving Real Business Problems
How organisations across industries used bespoke AI to unlock accuracy, speed, and revenue that off-the-shelf tools could never deliver.
E-
E-CommerceAI-Driven Product Discovery Engine
The Challenge
A 50M-SKU marketplace with poor search relevance losing 28% of users to competitor platforms within the first session.
The Outcome
Custom semantic search + recommendation model. Search-to-purchase conversion up 41%. Average basket value increased 22% within 60 days.
Semantic SearchRecommendation AIConversion
HE
HealthcareClinical Document Intelligence
The Challenge
A hospital network manually reviewing 14,000 patient records monthly — 3 staff-days per discharge summary with significant coding errors.
The Outcome
Custom NLP extraction pipeline reduced processing time by 94%. Clinical coding accuracy improved to 98.6%. Full ROI in 5 months.
NLPDocument AIClinical
MA
ManufacturingVisual Quality Inspection
The Challenge
A PCB manufacturer with a 6% defect escape rate generating £2.1M in annual warranty claims from manual visual QC.
The Outcome
Computer vision inspection system achieved 99.2% defect detection. Warranty claims reduced 78%. Line throughput increased 35%.
Computer VisionQuality ControlIoT
FI
Financial ServicesReal-Time Credit Decisioning
The Challenge
A lender using rule-based credit scoring with a 31% approval rate and high default exposure, with 48-hour manual decisioning cycles.
The Outcome
Custom gradient boosting model with 140 behavioural signals. Approvals up 18%, defaults down 24%, decisioning time: 4 seconds.
Predictive MLCredit RiskReal-Time
Industries
AI Solutions Across Key Verticals
Deep domain expertise meets cutting-edge AI — delivering results where they matter most.
Client Stories
Built With Trust. Proven in Production.
Hear directly from the leaders who partnered with us to ship AI-powered products, modernize platforms, and move faster than they thought possible.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
Book a call or drop us a message. Our team will respond within 24 hours.
Schedule a Discovery Call
30-minute consultation · Free
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