AI & Data
AI Development & Data Engineering Services for Production
Production-grade AI systems, ML models, and data infrastructure - built to ship, not to sit in a prototype folder.
What we have done
AI-powered diabetic retinopathy screening at scale.
We built and deployed an AI model that classifies diabetic retinopathy from retinal images, helping clinicians screen patients faster and flag at-risk cases earlier.
Read the case study01 — Applied Machine Learning
Models Built for Production, Not Pilots
- Predictive ModelsForecasting models for demand, churn, risk, and behaviour — trained on your data, tuned for your context.
- Computer Vision & NLPImage recognition, document understanding, and language models that turn unstructured data into structured signal.
- Production-Ready MLModels deployed with monitoring, evaluation, and retraining pipelines — built to perform in real environments, not just on a research notebook.
02 — Data Engineering & Analytics
Pipelines, Warehouses, and Insights at Scale
- Data PipelinesETL and ELT pipelines that move, transform, and validate data across systems — engineered for reliability, not just speed.
- Analytics at ScaleDashboards, real-time reporting, and decision-grade insights built on top of your data — not generic BI templates.
- Data WarehousingModern data lakes and warehouses (Snowflake, BigQuery, Redshift, Postgres) designed to handle scale, complexity, and AI workloads.
03 — AI Consultancy
Strategy and Roadmap Before You Build
- AI Readiness AssessmentA structured review of your data, systems, team, and goals to identify what's actually buildable and what isn't yet ready.
- Proof of Concept4–8 week prototypes that validate technical feasibility and business case before committing to full development.
- End-to-End AI StrategyFrom adoption planning to governance and responsible deployment — we help embed AI in ways your team can actually maintain.
Related Case Studies
Frequently Asked Questions
What does ByteCorp's AI development service include?
Production-grade machine learning (predictive models, computer vision, NLP), data engineering (ETL/ELT pipelines, warehouses, analytics at scale), and AI consultancy — from readiness assessment through deployed, monitored models.
How long does an AI development project take?
Readiness assessments complete in 2–4 weeks; a proof of concept runs 4–8 weeks; full implementations from PoC to deployed system typically finish within our standard four-month transformation cadence.
What's the difference between AI consulting and AI development?
Consulting answers what's buildable and what's worth building — readiness assessment, PoC, and strategy. Development builds and ships it: production ML with monitoring, evaluation, and retraining pipelines.
What AI models and platforms does ByteCorp work with?
Modern ML tooling (PyTorch, TensorFlow, scikit-learn), cloud-native data platforms (AWS, GCP, Azure; Snowflake, BigQuery, Redshift, Postgres), orchestration (Airflow, dbt), and LLM stacks (LangChain, vector databases).
How much does AI development cost?
It depends on data readiness and scope. We start with a readiness assessment and a fixed-scope PoC so you validate technical feasibility and business case before committing to full development spend.
Is my business ready for AI?
Our AI Readiness Assessment reviews your data, systems, team, and goals to identify what's actually buildable today and what needs groundwork first — so you invest where it pays off.
Get AI-Ready before the regulator asks.
One call to map where you are, what applies to you, and the shortest path to both.