Data & AI Engineering
Build the data foundation AI actually depends on — and the intelligence layer that turns it into real business decisions.
Data & AI Engineering
Build the data foundation AI actually depends on — and the intelligence layer that turns it into real business decisions.
Why Cannyfore Data & AI Engineering
We provide comprehensive Data & AI Engineering services, supporting client data foundations, AI model pipelines, and production intelligence systems — ensuring seamless operation and end-to-end delivery for a reliable, governed, and outcome-driven AI ecosystem.
Most AI projects don't fail because of the model
They fail because the data isn't ready. Incomplete pipelines, inconsistent feature engineering, ungoverned data stores, and no operational backbone for models in production — these are the real reasons AI initiatives stall or quietly degrade after launch.
Cannyfore’s Data & AI Engineering practice builds both layers your AI needs to succeed: the production-grade data foundation underneath, and the predictive and decision intelligence on top. Every capability we build is tied to a measurable business outcome — revenue, cost, risk, or customer experience.
These two practices work together by design. The data engineering foundation in Pillar 1 is what makes the predictive models in Pillar 2 reliable, fast, and maintainable at enterprise scale.
Type of Work
Explore our range of managed services, from application management to infrastructure support, tailored to meet your business needs.
AI Engineering & Data
DATA PIPELINES & LAKEHOUSES
We design and build end-to-end data ingestion, transformation, and orchestration pipelines for structured and unstructured data at enterprise scale. Our lakehouse architectures serve both analytics and AI workloads from a single governed data layer — eliminating the silos that slow down model development and reduce data trust.
FEATURE STORES
We engineer centralised feature stores so ML teams stop recomputing the same features independently across every project. Shared, versioned, and reusable features reduce training time, eliminate training-serving skew, and make it faster to build and deploy new models — because the hard data work is already done.
VECTOR DATABASES & KNOWLEDGE GRAPHS
We build the retrieval and reasoning infrastructure that GenAI and LLM applications depend on. Vector databases for semantic search and RAG-based applications; knowledge graphs for structured reasoning across enterprise data assets. Both are essential components of production-grade GenAI systems grounded in your own data.
MLOPS & LLMOPS PLATFORMS
We set up the operational backbone for ML and LLM workloads at enterprise scale — experiment tracking, model registry, CI/CD pipelines for models, serving infrastructure, cost optimisation for inference, and prompt management for LLM-based applications. So your models ship faster and run cheaper.
DATA QUALITY, LINEAGE & OBSERVABILITY
We embed data quality checks, lineage tracking, and pipeline observability into every data system we build — so teams always know where data comes from, whether it can be trusted, and when something breaks before it breaks a downstream model or business report. Clean, governed, and observable data is the non-negotiable foundation of reliable AI.
Predictive & Decision Intelligence
We deliver forecasting, risk, recommendation, and vision models as decision APIs your applications and teams consume directly — every model tied to a revenue, cost, or risk KPI.
FORECASTING & DEMAND MODELS
Revenue forecasting, demand planning, inventory optimisation, and capacity models for finance, operations, and supply chain teams. We deliver these as APIs your planning systems consume in real time — with confidence intervals, scenario modelling, and explainable outputs your business leads can actually act on.
CHURN, RISK & PROPENSITY MODELS
Customer churn prediction, credit risk scoring, fraud detection, AML models, and propensity-to-buy engines. Each model is tied directly to a revenue or risk KPI and deployed as a real-time decision API into your CRM, core banking, or case management system — so the intelligence reaches the people who need to act on it.
RECOMMENDATION & PERSONALISATION ENGINES
Product recommendation, content personalisation, next-best-action, and next-best-offer engines for digital channels. Context-aware, real-time, and integrated directly into your web, mobile, and CRM touchpoints. Built to drive conversion, retention, and revenue — not just click-through rates.
COMPUTER VISION & DOCUMENT AI
Visual inspection models for manufacturing defect detection, document classification and extraction for back-office automation, invoice and claims processing, and KYC identity verification. We combine computer vision with Intelligent Document Processing (IDP) to automate the high-volume, document-heavy workflows that still consume significant manual effort across BFSI and manufacturing.
REAL-TIME DECISION APIS
Every model we build is delivered as a production-grade decision API — not a notebook or a proof of concept. Low-latency REST or gRPC endpoints with A/B testing, feature flagging, monitoring, and rollback built in. Your applications get the intelligence they need in under 100ms, with full observability on every prediction made.
Where we apply this — by industry
Our Data & AI Engineering work spans four core verticals, each with well-established use cases where the combination of a solid data foundation and predictive intelligence delivers measurable business outcomes.
BANKING & FINANCIAL SERVICES (BFSI)
- Credit risk scoring and loan default prediction delivered as real-time APIs
- Customer churn and next-best-offer models integrated into CRM and mobile banking
- Fraud detection and AML transaction monitoring with sub-second response times
- Document AI for KYC identity verification, account opening, and contract extraction
- Data lakehouses and feature stores for unified customer 360 and regulatory reporting
MANUFACTURING & INDUSTRIAL
- Visual inspection models for automated defect detection on production lines
- Predictive maintenance models to reduce unplanned downtime and maintenance cost
- Demand forecasting and inventory optimisation for supply chain planning
- Energy consumption forecasting and production yield optimisation
- Data pipelines connecting OT/IoT sensor data to enterprise analytics platforms
RETAIL & E-COMMERCE
- Real-time product recommendation and personalisation engines for web and mobile
- Customer churn and propensity-to-buy models integrated with CRM and marketing automation
- Inventory and assortment optimisation using demand forecasting models
- Computer vision for shelf analytics, planogram compliance, and returns processing
HEALTHCARE & INSURANCE
- Patient risk stratification and readmission prediction models
- Claims processing automation using document AI and IDP pipelines
- Fraud detection for insurance claims and prior authorisation workflows
- Data lakehouse and governance infrastructure for clinical and operational data
How Cannyfore Work
Every Data & AI Engineering engagement follows a structured four-phase approach — from understanding your current data landscape to running models reliably in production.
Discover & assess
We audit your current data architecture, pipelines, quality, and governance posture. We identify gaps between where your data is today and what your target AI use cases actually require — giving you a clear, honest baseline before any build begins.
Design architecture
We design the target data and AI architecture — lakehouse structure, pipeline topology, feature store design, model serving approach, and MLOps toolchain — matched to your cloud environment, team capability, and cost constraints.
Build & test
We build pipelines, feature stores, models, and APIs in iterative sprints with continuous testing at every layer — data quality, model accuracy, API performance, and integration testing against your downstream systems.
Deploy & operate
We deploy to production with full monitoring, drift detection, and retraining pipelines in place. Ongoing operations are handed over to your team or transitioned into our Responsible AI & Managed Services practice for 24×7 support and continuous optimisation.
Key Objectives
Provide adequate coverage window for production (24*7 or 8*5 or 8*7)
Provide committed Service Level Response & Resolution Time
Provide measurable service level parameters.
Contact Us
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Solution Offerings
We deliver innovative and sustainable IT solutions that enable intelligent decisions and add value to our clients.
Our Health Check Assessment – Framework
- Setting the expectations
- Discussing the issues
- Filling up a questionnaire
- Interviews
- Meetings
- Code Reviews
- Final Presentation
- Deliverables
- Customer Facing
- Executive Summary
- Engagement detail
- Recommendations
- Prioritization
- Enhance Documentation
