AI & Machine Learning

Enterprise AI That
Actually Works
in Production

From strategy and data engineering to model deployment and continuous optimisation - we take your enterprise from AI pilot to measurable, production-grade impact. No buzzwords. Just results.

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At a Glance
340%
Average ROI Delivered
60+
AI Models in Production
14 wk
Avg Time to Value
87%
Avg Efficiency Gain
2.4M
Events Processed / sec
Core Capabilities

Everything Needed to Deploy
AI at Enterprise Scale

We cover the full AI stack - from raw data and model selection through to serving infrastructure and ongoing monitoring.

LLM Fine-Tuning & Customisation

Adapt foundation models like GPT-4, Llama 3, and Mistral to your domain. We handle data preparation, PEFT/LoRA fine-tuning, RLHF alignment, and evaluation pipelines.

GPT-4 Fine-tuning LoRA / QLoRA RLHF PEFT Llama 3 Mistral

RAG Pipeline Engineering

Build retrieval-augmented generation systems that ground LLMs in your proprietary data. Scalable vector stores, smart chunking, re-ranking, and hybrid search.

Pinecone Weaviate pgvector LangChain LlamaIndex Hybrid Search

Agentic AI & Autonomous Workflows

Design multi-agent systems and autonomous pipelines that plan, reason, use tools, and execute complex enterprise workflows with minimal human oversight.

AutoGen CrewAI Tool Use ReAct LangGraph Function Calling

Computer Vision Systems

Object detection, image classification, OCR, video analytics, and defect detection systems. Deployed on-device, on cloud, or on edge hardware for real-time processing.

YOLOv8 PyTorch Vision OpenCV TensorRT ONNX Edge AI

NLP & Document Intelligence

Extract structure from unstructured documents, automate classification, enable semantic search, and build intelligent document processing workflows at scale.

spaCy Transformers Named Entity Rec. Document AI OCR Classification

MLOps & Model Lifecycle

Productionise AI with robust CI/CD for ML, experiment tracking, automated retraining, model versioning, A/B testing, and drift monitoring for sustained performance.

MLflow Weights & Biases Kubeflow BentoML Evidently Arize AI
Our Process

From Strategy to Scale
in 5 Proven Phases

We follow a structured engagement model that minimises risk, maximises learning, and builds internal AI capability within your organisation.

01

Discovery & Strategy

Audit your data assets, define business objectives, map AI opportunities, and build a prioritised roadmap with clear ROI targets.

Wks 1–2
02

Data Engineering

Clean, label, enrich, and pipeline your data into AI-ready formats. Establish feature stores, data contracts, and governance policies.

Wks 3–5
03

Model Development

Select architectures, run experiments, fine-tune or train models, and evaluate rigorously against your business KPIs.

Wks 6–10
04

Production Deployment

Containerise models, build serving APIs, integrate into your systems, and launch with full observability and rollback safeguards.

Wks 11–14
05

Optimise & Scale

Monitor drift, retrain on new data, A/B test model variants, and scale throughput as usage grows across the organisation.

Ongoing
Industry Use Cases

AI That Solves Real
Enterprise Problems

We've deployed AI across financial services, healthcare, manufacturing, retail, and logistics - here's what we've built.

Financial Services AI

From fraud detection and algorithmic risk scoring to AI-powered compliance monitoring and intelligent document extraction for loan origination.

Capabilities Delivered

  • Real-time fraud detection with <1% false positive rate
  • Automated AML/KYC document extraction and verification
  • AI credit scoring models trained on alternative data
  • LLM-powered regulatory compliance Q&A systems
  • Algorithmic portfolio risk analysis and stress testing
  • Conversational banking assistants with RAG grounding

Typical Outcomes

$4.2M
Annual fraud losses prevented per mid-size bank
78%
Reduction in manual compliance review time
3.1×
Improvement in credit default prediction accuracy
48h→2h
Loan document processing time reduction

Healthcare & Life Sciences AI

Clinical decision support, medical imaging analysis, prior authorisation automation, and patient journey personalisation - all HIPAA-compliant.

Capabilities Delivered

  • Medical imaging AI for radiology triage and anomaly detection
  • Clinical NLP for unstructured EHR note processing
  • Prior auth automation reducing denials and delays
  • Patient risk stratification for proactive outreach
  • Drug interaction and adverse event prediction models
  • AI-assisted coding (ICD-10/CPT) for revenue cycle

Typical Outcomes

94%
Accuracy on radiology anomaly detection
65%
Reduction in prior auth processing time
40%
Decrease in readmission rates via risk models
3.8×
ROI on EHR-to-insight automation projects

Manufacturing & Industrial AI

Predictive maintenance, visual quality control, demand forecasting, and supply chain optimisation for Industry 4.0 operations.

Capabilities Delivered

  • Predictive maintenance using IoT sensor time-series data
  • Computer vision defect detection on production lines
  • Digital twin simulation powered by ML models
  • Demand forecasting and inventory optimisation AI
  • Root cause analysis via multivariate anomaly detection
  • Energy consumption optimisation for plant operations

Typical Outcomes

73%
Reduction in unplanned downtime events
99.2%
Defect detection accuracy on production line
28%
Decrease in inventory holding costs
18%
Energy cost savings via AI optimisation

Retail & E-commerce AI

Personalisation engines, dynamic pricing, visual search, churn prediction, and AI-driven merchandising to lift conversion and retention.

Capabilities Delivered

  • Real-time personalisation and product recommendation engines
  • Visual search and AI-powered catalogue tagging
  • Dynamic pricing models with competitive data feeds
  • Customer lifetime value and churn prediction
  • Inventory demand forecasting across SKUs
  • AI chatbots for customer support and upselling

Typical Outcomes

34%
Increase in average order value via recommendations
22%
Improvement in customer retention rates
$1.8M
Annual revenue uplift for mid-size retailer
4.1×
ROI on first-year AI personalisation deployment

Logistics & Supply Chain AI

Route optimisation, shipment ETA prediction, warehouse automation, and supplier risk modelling to build resilient, intelligent supply networks.

Capabilities Delivered

  • Dynamic route optimisation reducing fleet miles driven
  • Shipment ETA prediction with disruption awareness
  • Warehouse slot and pick-path optimisation AI
  • Supplier risk scoring with multi-source signal fusion
  • Multi-modal demand sensing for supply planning
  • Last-mile delivery time window prediction models

Typical Outcomes

19%
Reduction in total fleet transportation costs
91%
On-time delivery accuracy with AI routing
35%
Decrease in warehouse labour through optimisation
2.7×
Supply chain resilience score improvement
Technology Stack

Battle-Tested Tools,
Not Vendor Lock-In

We're technology-agnostic and select the best tool for each problem - then engineer it to work seamlessly in your existing infrastructure.

Foundation Models

GPT-4 / GPT-4o
Claude 3 Opus
Gemini Ultra
Llama 3 70B
Mistral Large
Phi-3 Medium

ML Frameworks

PyTorch 2.x
TensorFlow 2.x
JAX / Flax
Scikit-learn
XGBoost / LightGBM
ONNX Runtime

RAG & Retrieval

LangChain
LlamaIndex
Pinecone
Weaviate
pgvector
Qdrant

MLOps & Serving

MLflow
Weights & Biases
BentoML
Triton Inference
Kubeflow
Evidently AI

Data Engineering

Apache Spark
dbt
Airflow
Kafka
Great Expectations
Delta Lake

Infrastructure

Kubernetes
AWS SageMaker
GCP Vertex AI
Azure ML
NVIDIA CUDA
Ray Serve

Agentic AI

AutoGen
CrewAI
LangGraph
Semantic Kernel
Tool Use APIs
Function Calling

Observability

Arize AI
WhyLabs
Prometheus
Grafana
OpenTelemetry
Datadog AI
How We Engage

Flexible Models to Match
Your Goals & Stage

Whether you need a rapid proof-of-concept, a full-scale AI transformation programme, or an embedded team to augment your engineers - we have an engagement model for you.

AI Discovery Sprint

A focused 2-week sprint to identify your highest-ROI AI opportunities, audit your data readiness, and deliver an actionable roadmap with business case.

  • Data & systems audit
  • Opportunity scoring matrix
  • Prioritised 12-month roadmap
  • ROI & investment modelling
Get Started
Most Popular

End-to-End AI Programme

Our flagship model: a structured 14–20 week programme taking you from data to deployed AI, with full knowledge transfer and MLOps handoff to your team.

  • Full-stack model development
  • Production deployment & MLOps
  • Team training & documentation
  • 3-month post-launch support
Start Programme

Embedded AI Team

Senior AI engineers embedded within your team on a retainer basis - working alongside your engineers to accelerate roadmap delivery month over month.

  • 2–6 senior engineers
  • Sprint-based delivery cadence
  • Weekly reporting & demos
  • Flexible 3–12 month terms
Build Your Team
FAQs

Common Questions About
AI Transformation

Most end-to-end programmes run 14–20 weeks from kick-off to production deployment. Discovery sprints are 2 weeks. Scope, data readiness, and integration complexity are the main variables - we give a detailed estimate after the initial discovery phase.
No. We work with organisations at every level of AI maturity - from those with no existing data science capability to those with large ML teams looking for specialist augmentation. We provide knowledge transfer and documentation throughout.
We build monitoring and retraining pipelines as part of every deployment. This includes data drift detection, performance dashboards, automated alerts, and scheduled retraining cadences - all managed through your MLOps infrastructure.
It depends on the use case. We conduct a data audit in the discovery phase to assess quality, volume, and labelling requirements. Many enterprises have more usable data than they realise - and we can help you identify it.
We operate under strict NDA and data processing agreements. All work is done in your cloud environment or a dedicated isolated environment. You retain full ownership of all models, code, and IP produced during the engagement.
Yes. We are cloud-agnostic and work across AWS, GCP, Azure, and on-premise environments. We prefer to build within your existing infrastructure to avoid vendor lock-in and reduce long-term operational overhead.
We are engineers first. We build and deploy AI systems - we don't produce strategy decks. Our teams are hands-on, senior, and typically 3–5x faster to production than larger consulting organisations. We also offer transparent fixed-scope pricing.
Yes. All end-to-end programmes include 3 months of post-launch support. Beyond that, we offer retainer-based support packages, embedded team models, and annual MLOps review engagements for production systems.
Ready to Transform?

Take AI From Pilot
to Production This Year

Book a free 45-minute discovery call with one of our senior AI engineers. We'll review your data, identify your best AI opportunities, and outline a roadmap with realistic ROI targets.

Book a Discovery Call