"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
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AI EngineeringCustom AI Development
Custom AI Development Services For Sustainable Impact
Embed leading-edge AI/ML development capabilities into your business and push cognitive efficiency to the edge. Our full-suite AI services in the USA seamlessly augment human intelligence, delivering the flexibility your core business demands. Our AI engineers help realign your AI strategy, build a strong strategic foundation, and ensure rapid delivery of data-driven insights and innovative solutions. Partner with the best AI/ML development company to unlock enterprise-wide intelligent capabilities—and stay ahead of the curve.
Our Services
The Full Spectrum of Custom AI
Custom AI development services and generative AI development support spanning data preparation, model training, integration, deployment, and ongoing optimisation for production use.
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.
Software + Models
Custom AI & AI Software Development
Where models, product engineering, and production operations meet.
Custom AI development is more than model training. It is AI software development that connects data pipelines, model logic, evaluation criteria, APIs, user workflows, and production operations into a usable system. The output is not a demo notebook; it is a working capability designed around your accuracy targets, latency constraints, and integration requirements.
That is also why custom AI solutions often outperform generic tools in higher-stakes environments. Whether the need is generative AI development for assistants and search, NLP for document intelligence, or predictive models for decisioning, the engineering work has to cover ownership, deployment, drift detection, and operational change from the start.
Built for Production, Not Just Demos
Four principles that separate genuine AI engineering from AI theatre.
Our Capabilities
Deep Tech, End-to-End Ownership
Six engineering capabilities covering custom LLM development, AI model development, deployment, monitoring, and iteration after launch.
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 Built for Every Industry
Every industry re-imagined with our enterprise AI services. We are reinventing every industry and creating a unique solution that moves our clients forward and disrupts the market.
Before You Build
Planning Your Custom AI Build
What teams weigh before commissioning custom AI — when to build, which approach fits, data needs, cost, accuracy, and keeping your data secure.
8 questions answered
01
When does it make sense to build custom AI instead of using an off-the-shelf tool?
Build custom AI when your edge depends on proprietary data, domain accuracy, or workflows generic tools can't match — and when off-the-shelf models would leak data or cap differentiation. If a problem is common and non-core, integrating an existing AI model is faster and cheaper. We help you make that call honestly.
02
Should we train a custom model, fine-tune one, or use RAG?
It depends on the job. Fine-tuning teaches a model your tone, format, and domain tasks; retrieval-augmented generation grounds answers in your live documents without retraining; a fully custom model suits unique data types or strict latency. Most production systems blend them, and we benchmark each against your accuracy and cost targets.
03
How much of our own data do we need to build a custom AI model?
Less than most teams expect. Fine-tuning a strong foundation model can work with a few hundred to a few thousand well-labelled examples; RAG needs your documents, not training data at all. When data is thin, solid data engineering — cleaning, labelling, and augmentation — closes the gap before training begins.
04
How much does custom AI development cost, and how is it priced?
Cost scales with scope, not a fixed sticker — data readiness, model complexity, integrations, and accuracy targets drive it most. A focused pilot proving one use case costs far less than a full platform, so most teams start there. Many control spend with dedicated AI/ML engineers sized to the roadmap.
05
How do you make sure a custom model is accurate and doesn’t hallucinate?
Accuracy is engineered, not assumed. We define evaluation sets up front, measure against held-out data, and add guardrails, retrieval grounding, and human review for high-stakes outputs. After launch, production monitoring and drift detection catch quality decay early and trigger retraining — so the model stays reliable as real-world data shifts.
06
How do you keep our proprietary data secure while building a custom model?
Your data stays yours and stays protected. We train inside your cloud or a private VPC, never on shared model endpoints, with encryption, access controls, and audit logging throughout. Secure DevOps and infrastructure practices — isolated environments, secrets management, and compliance alignment — keep sensitive data contained from training through production.
07
What's the difference between generative AI development and custom AI development?
Generative AI development is one subset of custom AI development focused on systems that create or transform content, such as copilots, assistants, search, and document generation. Custom AI development is broader and also includes prediction, classification, computer vision, and decisioning. We help teams choose the right pattern based on use case and risk through AI strategy consulting.
08
Do you offer custom LLM development and fine-tuning?
Yes. We offer custom LLM development, fine-tuning, evaluation, guardrails, deployment, and ongoing monitoring for organisations that need models adapted to proprietary language, workflows, or compliance needs. That can include supervised fine-tuning, retrieval grounding, or hybrid systems depending on the job, often alongside broader AI solutions planning and rollout.
Client Stories
From Vision to Verified Results
Behind every number - 40% faster deliveries, 60% less admin workload, 50% quicker data processing - is a client who trusted us with a real business challenge. These aren't just demos. They're live products, running at scale sustainably, delivering results clients can measure.
"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 message us with your project specs, and we will get back to you within 24 hours!
Schedule a Discovery Call
30-minute consultation · Free
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