Applied AI & ML systems engineered for production.
Custom models, LLM agents, and predictive systems built with rigorous evaluation, MLOps discipline, and deployment paths that survive real data drift — not just demo-day accuracy.
We design and deploy machine learning systems for manufacturers, defence labs, HealthTech, and enterprise R&D teams who need models that hold up after go-live. That means clean data pipelines, reproducible training, honest eval harnesses, and inference stacks tuned for latency and cost.
From classical ML on tabular industrial data to multi-modal LLM copilots on private corpora, we own the full chain: problem framing, feature engineering, model selection, deployment, and the monitoring layer that catches drift before your operators do.
Production-grade engineering outcomes
Scoped deliverables with clear acceptance criteria — not open-ended consulting hours.
Custom ML Models
Classification, regression, ranking, anomaly detection trained on your domain data with documented baselines.
LLM Apps & RAG
Private copilots, document Q&A, and agentic workflows with retrieval, guardrails, and audit trails.
Generative AI Pipelines
Fine-tuned diffusion, text, and code generation with brand-safe filters and human-in-the-loop review.
Predictive Analytics
Forecasting, demand planning, churn, and predictive maintenance on time-series and event data.
NLP & Speech
Entity extraction, summarization, sentiment, ASR, and multilingual TTS for operator-facing tools.
Edge AI / TinyML
Quantized models on Jetson, Coral, Hailo, ESP32, and STM32 for sub-100ms on-device inference.
The tools we deploy in production
Battle-tested frameworks, platforms, and infrastructure — chosen for reliability, not hype.
Frameworks
LLM & Agents
Data & Pipelines
Serving & Optimisation
MLOps
Infrastructure
How enterprises buy and scale this capability
Clear engagement models, buyer alignment, and commercial outcomes — so procurement and engineering stay in sync.
Buyer Personas
- CTO / VP Engineering seeking production ML, not POC theatre
- Head of Data Science needing a delivery partner for model deployment
- Product leaders building AI-native features for enterprise buyers
- Manufacturing & operations leaders automating inspection and planning
Engagement Models
- Fixed-scope POC sprint (2–4 weeks) with go/no-go criteria
- Milestone-based product build with weekly demos
- Retained AI engineering pod for continuous model iteration
- Rescue & refactor of stalled ML initiatives with audit-first approach
Commercial Outcomes
- Reduced false-positive rates in automated decision systems
- Faster time-to-inference with documented latency SLAs
- Lower cloud GPU spend via quantisation and batching strategy
- Audit-ready model cards, eval reports, and data lineage for procurement
Procurement Fit
- NDA and IP assignment aligned to enterprise standards
- Full source-code handover — you own weights, code, and pipelines
- India-based senior team with on-site commissioning available
- Milestone invoicing tied to acceptance criteria, not hours logged
Where this lands in the real world
Manufacturing
Predictive maintenance, yield optimisation, and operator-assist copilots on shop-floor data.
Defence & Security
Threat scoring, sensor fusion analytics, and decision-support on classified-adjacent workloads.
HealthTech
Diagnostic assist, triage models, and clinical document intelligence with strict eval protocols.
FinTech & Insurance
Risk scoring, fraud detection, and document extraction with explainability requirements.
Enterprise SaaS
Embedded AI features, recommendation engines, and customer-facing copilots.
AgriTech
Crop yield forecasting, pest detection models, and supply-chain demand planning.
Often paired with these practices
// IN SHORT
AI and machine learning development at TechieYan
TechieYan is an AI and machine learning development company in Hyderabad. We build custom ML models, computer vision, LLM and RAG applications on private data, and predictive analytics, and deploy them on cloud, on-premise, air-gapped or edge devices. Projects start with a 2–4 week proof of concept with agreed success metrics.
// HOW A PROJECT RUNS
From first call to handover
- 1. Free discovery callWe understand the problem, the data you have and how success will be measured.
- 2. Data and feasibility checkA short review of your data and constraints, so we only propose what is achievable.
- 3. Proof of concept (2–4 weeks)A working model on your data with clear metrics and a go/no-go decision.
- 4. Production buildIntegration with your systems, deployment on cloud, on-prem or edge, with monitoring.
- 5. Handover and supportCode, model weights, pipelines, evaluation reports and training for your team.
// WHAT AFFECTS THE COST
What decides the price of your project
- The type of problem: prediction, vision, documents, chat or a mix
- How much data exists and how much labelling or cleaning it needs
- Where it must run: cloud, on-premise, air-gapped or edge devices
- Accuracy, speed and uptime targets
- Integrations with existing software and user interfaces
Every project gets a fixed-scope proposal before work starts. Send your requirement or call +91 70755 75787 for a free estimate.
// RELATED WORK
Projects we have built
// START YOUR AI PROJECT
Tell us what you're building
A senior engineer replies within one business day. NDA-friendly, fixed-scope proposal. Business enquiries only.
Prefer to talk? Call +91 70755 75787 (Mon–Sun, 9am–9pm IST)