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
- Source-open delivery — 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.