Your enterprise, working intelligently.
Machine learning, natural language processing, and IBM Watson integration — deployed at enterprise scale.
Deploying machine learning and predictive analytics in enterprise environments since 2004.
From single-model pilots to multi-model production systems running across global enterprises.
AI systems live across Asia Pacific, Europe, the Middle East, and North America.
Aggregate financial impact measured across fraud reduction, efficiency gains, and revenue uplift.
The challenge
Most AI pilots stall before they reach production
Pilots without a path to scale
Proofs of concept succeed in a notebook and die at integration. We design for deployment, monitoring and ownership from week one.
Data that is not model-ready
Fragmented, ungoverned data caps accuracy. We fix lineage, quality and access before tuning a single model.
Risk and regulation catching up
Boards now ask about bias, explainability and the EU AI Act. Governance has to ship with the model, not after it.
VFACE delivers AI solutions that connect directly to your business outcomes — not research projects that stall in proof-of-concept. With 20 years of enterprise integration experience and IBM Watson certification, we deploy AI inside your existing systems, not alongside them.
What we offer
Artificial Intelligence services
Machine Learning Engineering
End-to-end ML pipeline design from feature engineering through production deployment and automated retraining on live data.
Natural Language Processing
Document processing, sentiment analysis, and entity extraction that converts unstructured text into structured business intelligence.
Computer Vision
Real-time visual inspection, defect detection, and video analytics for manufacturing, healthcare imaging, and retail environments.
Generative AI & LLMs
Enterprise LLM integration and RAG pipelines grounded in your proprietary data and secured within your infrastructure perimeter.
IBM Watson Integration
Certified Watson deployment for conversational AI, predictive analytics, and enterprise search on IBM Cloud and on-premise infrastructure.
Predictive Analytics
Demand forecasting, churn prediction, and risk-scoring models in production with automated retraining and drift detection.
AI Strategy & Roadmapping
Use-case prioritisation, data-readiness assessment, and multi-year AI roadmaps anchored to measurable business outcomes.
MLOps & Model Governance
Model versioning, monitoring dashboards, bias detection, and automated retraining pipelines that maintain quality at scale.
Document Intelligence
Automated contract review, invoice processing, and regulatory document extraction with structured output and audit trails.
Conversational AI
Enterprise chatbots and voice assistants integrated with CRM and ticketing systems for measurable deflection rates.
Recommendation Systems
Personalisation engines for e-commerce, content platforms, and B2B procurement built on collaborative filtering and deep learning.
AI Quality & Ethics
Explainability frameworks, fairness audits, and compliance reviews that make AI deployable in regulated industries.
Our solutions
Packaged offerings for real outcomes
AI Fraud Detection Platform
Real-time transaction scoring at sub-50ms latency, deployed across payment rails and digital banking platforms. Reduces false positives by 60–80% versus legacy rules engines.
- 40% average reduction in fraud losses within 6 months of go-live
- False-positive rates below 3% on production systems
- Automated model retraining — fraud patterns updated weekly without engineering intervention
Enterprise Knowledge Assistant
RAG-powered internal knowledge retrieval and document generation grounded in your proprietary data. Deployed within your security perimeter — no data leaves controlled infrastructure.
- 78% faster document retrieval for analyst and legal teams
- Hallucination-free responses through retrieval grounding on your document corpus
- Integrates with SharePoint, Confluence, and custom document repositories
Predictive Maintenance Intelligence
ML models monitoring vibration, temperature, and acoustic signatures across industrial equipment — flagging failure 2–6 weeks ahead and auto-scheduling maintenance in your CMMS.
- 30–42% reduction in unplanned downtime across production lines
- $1.5M–$3M annual maintenance savings per facility
- Mean time between failures extended by 25–35%
Customer Intelligence Suite
Demand forecasting, churn prediction, and next-best-action engines deployed inside your CRM — moving from descriptive reporting to predictive decision support at every customer touchpoint.
- Churn prediction accuracy above 85% on a 90-day horizon
- Demand forecast error reduced by 30% versus statistical baselines
- Personalisation lift of 18–25% on conversion rates
How we build
Engineering principles behind every engagement
Start from the decision, not the model
Every build begins with the business decision it improves and the metric that proves it.
- Value hypothesis signed off before build
- Baseline measured on current process
- Kill criteria agreed up front
Technology we work with
- IBM watsonx
- PyTorch
- TensorFlow
- LangChain
- Hugging Face
- MLflow
- Kubeflow
- Apache Spark
- Azure OpenAI
- AWS SageMaker
- Vertex AI
- Pinecone
- IBM watsonx
- PyTorch
- TensorFlow
- LangChain
- Hugging Face
- MLflow
- Kubeflow
- Apache Spark
- Azure OpenAI
- AWS SageMaker
- Vertex AI
- Pinecone
How we deliver
From first workshop to production in four steps
Frame the use case
Pick the decision or workflow where AI moves a business metric, and agree how success is measured.
Prepare the data
Audit, clean and secure the data the model will learn from, inside your compliance perimeter.
Build and validate
Train, evaluate and red-team models against held-out data before anything reaches production.
Deploy and retrain
Ship behind your existing systems with monitoring, drift alerts and automated retraining.
Your first 90 days
What you can expect, phase by phase
Frame and prove
- Use-case prioritisation workshop
- Data readiness assessment
- Baseline and success metrics
- Working prototype on real data
Build and integrate
- Production pipeline and APIs
- Security and governance review
- Integration with core systems
- Pilot with a live user group
Launch and learn
- Staged rollout with monitoring
- Operations runbooks and training
- Measured results vs baseline
- Scale plan for the next use cases
Insights & research
Latest thinking
Proven results
Client case studies
A Southeast Asian regional bank was losing $4.2M annually to transaction fraud, with a legacy rules engine producing a 12% false-positive rate that frustrated legitimate customers.
- False-positive rate reduced from 12% to 3%, recovering $1.1M in customer lifetime value
- Real-time fraud scoring across 2.3M daily transactions with sub-50ms latency
- Weekly model retraining automated — no engineering intervention required
A private radiology network processing 4,000 scans daily needed radiologists to review AI-assisted findings faster without sacrificing diagnostic accuracy.
- AI triage pre-classified 87% of scans by urgency, eliminating manual sorting queues
- Diagnostic accuracy maintained at 97.4% across three clinical validation sites
- Radiologist throughput increased from 85 to 190 scans per shift
An automotive parts supplier across 14 production lines faced 23% unplanned downtime due to reactive maintenance — each stoppage costing ₹18 lakh per hour.
- ML model detected bearing failure 3–5 weeks before occurrence on critical CNC machinery
- Annual maintenance cost reduced by $1.8M through predictive scheduling
- Mean time between failures extended by 31% within the first six months
Technology partners
Partners that power our delivery
Silver Partner with certified access to IBM watsonx platform, Watson APIs, and IBM Research co-innovation for enterprise AI deployments.
AWS-certified deployment of SageMaker ML pipelines, Bedrock LLM integration, and Rekognition computer vision on enterprise AWS workloads.
Azure OpenAI Service, Azure ML Studio, and Cognitive Services integration for enterprises with existing Microsoft licensing and Azure Active Directory.
GPU infrastructure partnership for large-scale model training and inference — including A100 and H100 cluster configurations for foundation model fine-tuning.
Open-weight model access and fine-tuning infrastructure — enabling on-premise LLM deployment for data-residency-sensitive industries without external API dependencies.
Industries
Sectors we serve with artificial intelligence
Client voices
What our clients say
reduction in fraud losses
“VFACE deployed a fraud detection model that cut our losses by 40% within six months — and the false-positive rate dropped from 12% to under 3%. That combination returned more in recovered customer lifetime value than the entire engagement cost.”
Industry benchmark
How a VFACE-built AI programme compares
Pilots reaching production
Time from idea to pilot
Lower is better
Models monitored for drift
Figures are indicative ranges from typical engagements compared with published industry surveys. Results vary by scope and starting point.
The difference
What changes when the work is done
Before VFACE
- Pilots that never leave the lab
- Manual review of every document and exception
- Models nobody monitors after launch
After VFACE
- Production models tied to a business KPI
- Automated triage with humans on the edge cases
- Drift alerts and scheduled retraining
Ways to work with us
Engagement models that fit how you buy
Fixed-scope project
A defined outcome, a fixed timeline, one accountable team.
- Scoped in a two-week discovery
- Milestone-based delivery and sign-off
- Best for pilots and well-bounded builds
Dedicated team
Senior engineers embedded in your programme for the long run.
- Scales up or down by the sprint
- Works inside your tools and rituals
- Best for multi-quarter roadmaps
Managed service
We run it, monitor it and improve it after go-live.
- 24x7 support with agreed SLAs
- Continuous optimisation and reporting
- Best for production platforms
Security and governance
Responsible AI, built in
Explainability
Model cards and decision explanations for every production model.
Bias and fairness testing
Repeatable tests across protected attributes before and after release.
Data protection
GDPR-aligned data handling, minimisation and retention controls.
Regulatory readiness
Documentation mapped to EU AI Act risk tiers and NIST AI RMF.
Questions answered
Frequently asked questions
Insights and news
What is happening in the industry, and what we think
Industry headlines, last 30 days
Headlines via Google News. Third-party content; VFACE does not endorse the linked articles.
Keep exploring
News, articles and related services
Get started
Ready to put AI to work in your enterprise?
We begin every engagement with a two-week AI readiness assessment — evaluating your data quality, infrastructure, and use-case pipeline before any model is built. No commitment required for the initial assessment.