Custom AI Development
AI products and features designed around your data, users and business rules.
- Discovery and solution design
- Data pipelines and feature stores
- Model and application development
- Production deployment
AI Capabilities
End-to-end AI services, from first model to full platform.
We buildforecasting models, AI assistants, visual inspection, language understanding, recommendation engines
Strategy, data, model development, deployment and support under one team — machine learning, generative AI, computer vision and natural language processing built for production.
- Machine Learning
- Generative AI
- AI Agents
- Computer Vision
- NLP
- Deep Learning
- Explainable AI
Trusted by enterprises and fast-growing startups
Our core capabilities
One partner across the whole AI lifecycle
From the first data pipeline to the model running in your app, we design, build and operate AI systems — choosing proven techniques over hype and measuring everything against your business metric.
Custom machine learning
Forecasting, scoring, classification and optimisation models.
Generative AI applications
Assistants, content and code generation grounded in your data.
Computer vision
Detection, inspection, OCR and video analytics.
Natural language processing
Classification, extraction, sentiment and conversational AI.
Predictive analytics
Demand, churn and risk predictions people can act on.
MLOps and managed AI
Pipelines, monitoring and retraining after launch.
Services
AI services, built to ship
Drag, swipe or use the arrows to browse the services.
Generative AI Development
Assistants, content engines and agents built on leading models, grounded in your knowledge.
- RAG and fine-tuning
- Agent and tool design
- Evaluation and safety testing
- Cost and latency optimisation
Machine Learning Development
Predictive models that improve decisions in pricing, risk, demand and operations.
- Feature engineering
- Model training and tuning
- Explainability reports
- Batch and real-time scoring
Computer Vision Solutions
Visual AI for inspection, safety, identity and media — on cloud or edge devices.
- Object detection and segmentation
- Defect and anomaly detection
- OCR and ID verification
- Edge deployment
NLP and Conversational AI
Understand and act on text and speech — from chatbots to document classification.
- Chatbots and voice bots
- Intent and entity extraction
- Sentiment and topic analysis
- Multilingual support including Indian languages
MLOps and AI Support
Keep models accurate and reliable after launch.
- CI/CD for models
- Drift and quality monitoring
- Scheduled retraining
- SLA-backed support
Have a use case but no AI team?
Our engineers and data scientists can start with a scoped pilot and grow into a dedicated team.
Industries we serve
AI use cases, sector by sector
Hover or tap a sector to turn the card and see where AI earns its keep.
Healthcare
See use casesHealthcare
Imaging assistance, risk prediction and clinical NLP.
BFSI
See use casesBFSI
Credit scoring, fraud detection and customer assistants.
Retail and eCommerce
See use casesRetail and eCommerce
Recommendations, visual search and demand forecasting.
Logistics
See use casesLogistics
Route optimisation, load planning and ETA models.
Manufacturing
See use casesManufacturing
Visual inspection, predictive maintenance and yield models.
Agriculture and Climate
See use casesAgriculture and Climate
Crop, weather and satellite image analytics.
Education
See use casesEducation
Adaptive learning and automated assessment.
Media and Telecom
See use casesMedia and Telecom
Content tagging, churn models and network analytics.
Our success stories
Problems solved, in production
Real projects across AI, data, healthcare and commerce — from the first workshop to a live system.
Healthcare tech
Patient Data Migration
Secure enterprise migration of legacy patient health records to modern cloud infrastructure under strict compliance.
Read case study
Medical AI research
Breast Cancer Detection AI
Clinical-grade diagnostic and imaging assistance for cancer research — CNN classification combined with U-Net segmentation.
Read case study
E-commerce innovation
E-Commerce Website
Multi-vendor digital storefront and catalogue system for high-value designer commerce.
Read case study
Industrial IoT
IIoT Data Pipeline & Dashboard
Predictive anomaly detection and telemetry aggregation pipelines optimised for industrial manufacturing environments.
Read case study
Climate tech
Climate Data Classification
Large-scale climate intelligence and environmental classification platform for advanced data processing.
Read case study
Insurtech solutions
Health Data Visualization
Secure analytics suite and health data visualisation platform for high-volume enterprise tracking.
Read case studyWhy Pyzen
The habits behind every model we ship
Hover a line to read how it shows up in your project.
Opportunity before technology
We start from the business outcome and the cost of the process today, then choose the AI approach — not the other way round.
Architecture before model selection
Data flows, security and integrations are designed first, so models can be swapped as better ones arrive.
Proof before full build
A working pilot on your own data proves value and accuracy before the larger investment is committed.
Built around your systems
We extend the CRM, ERP, cloud and data platforms you already run instead of asking you to replace them.
Human oversight by design
Approval steps, confidence thresholds and audit trails keep people in control of the decisions that matter.
From strategy to support
The same senior team takes you from roadmap to production, then monitors, retrains and improves what we built.
Our roadmap
The AI lifecycle, on repeat
Models are never finished — each loop through the cycle makes them more accurate and cheaper to run. Pick a stage, or let it play.
Discovery
Goals, users, data sources and the business metric the model must move are defined.
Data preparation
Data is collected, cleaned, labelled and made available through reliable pipelines.
Model development
Approaches are prototyped and compared; the best candidate is trained and tuned.
Evaluation
Accuracy, fairness, robustness and cost are tested against agreed thresholds.
Deployment
The model is packaged, integrated into your app or workflow and rolled out.
Support and improvement
Performance is monitored, drift is caught early and the model is retrained.
Engagement options
Scalable ways to work together
Pick the model that matches your budget, timeline and how much of the work you want to own.
Fixed-scope project
A defined outcome, timeline and price — ideal for assessments, pilots and well-understood builds.
Best for pilots and auditsDedicated AI team
Engineers, data scientists and a delivery lead working as an extension of your own team.
Best for ongoing roadmapsMilestone-based engagement
Budget released stage by stage, each stage tied to a measurable result you sign off.
Best for phased programmesSupport and optimisation
Monitoring, retraining, cost tuning and new use cases on a monthly retainer.
Best for live AI systemsTechnology
The stack behind our models
Proven frameworks, chosen for your environment — never locked to one vendor.
PyTorchTensorFlowscikit-learnXGBoostHugging Face
OpenAIClaudeGeminiLlamaMistralLangChain
OpenCVYOLODetectron2MediaPipe
MLflowKubeflowAmazon SageMakerAzure MLVertex AIDockerKubernetes
Compliance
Responsible AI, by design
We design to the frameworks your legal, risk and IT teams rely on.
NIST AI RMF
Risk identification, measurement and management across the AI lifecycle.
ISO/IEC 42001
Management-system controls for responsible AI development and use.
EU AI Act
Risk classification, transparency and documentation for systems used in the EU.
DPDP Act 2023
Consent, purpose limitation and data-principal rights under India's data protection law.
GDPR
Lawful basis, data minimisation and data-subject rights for EU personal data.
HIPAA
Safeguards for protected health information in healthcare workflows.
SOC 2
Security, availability and confidentiality controls for hosted systems.
PCI DSS
Protection of cardholder data wherever payments are touched.
Move AI from experiment to everyday system.
Share your use case and we will propose an approach, team and phased plan.
FAQ
Straight answers to common questions
Still unsure? Talk to an AI engineer →
What kinds of AI projects do you take on?
Machine learning models, generative AI applications and agents, computer vision, NLP and conversational AI, plus the data engineering and MLOps around them.
Do you use off-the-shelf models or build custom ones?
Whichever fits. Many problems are best solved with existing foundation models plus your data; others need custom-trained models. We compare both on cost, accuracy and control.
Who owns the models and code?
You do. Code, trained models and documentation are handed over under the contract, and every engagement is covered by an NDA.
How do you measure whether a model is good enough?
Success criteria are agreed before development — accuracy on your data, latency, cost per prediction and the business KPI — and every release is tested against them.
Can you support models after launch?
Yes. We offer monitoring, retraining and SLA-backed support so models stay accurate as data and behaviour change.
Related capabilities
Explore more AI capabilities
Most programmes combine several of these. We help you choose the right mix.
AI AutomationWorkflows that read, decide and update your systems on their own.Explore →AI ConsultingStrategy, readiness and a roadmap your leadership can fund.Explore →AI IntegrationModels and agents embedded in the systems you already run.Explore →Document AIInvoices, claims and contracts turned into validated data.Explore →Enterprise AI CopilotsSecure assistants grounded in your knowledge and tools.Explore →
Start a conversation
Let's talk about Artificial Intelligence Services.
Emailsales@pyzenlabs.com
Phone+91 99718 38777
NoidaSector 62, Uttar Pradesh
GurugramUdyog Vihar, Haryana