AI Engineering · Machine Learning · MLOps
EnterpriseAI thatactuallyships.
We turn fragmented data and scattered AI initiatives into production-grade systems — governed, measurable, and built to scale inside your enterprise.
from orvian import ModelPipeline, DataContract
contract = DataContract(
schema="enterprise_v2",
quality_sla="0.995",
governance=True
)
pipeline = ModelPipeline.deploy(
contract=contract,
safety_eval=True,
canary="0.05"
)
✓ Production live
99.5%Data Quality SLA
3×Faster Releases
FullObservability
⬡ AI Engineering
⬡ Machine Learning
⬡ MLOps
⬡ Decision Intelligence
⬡ Predictive Analytics
⬡ Cloud-Native AI
⬡ Strategic Advisory
⬡ Data Governance
⬡ RAG Systems
⬡ Feature Stores
⬡ AI Engineering
⬡ Machine Learning
⬡ MLOps
⬡ Decision Intelligence
⬡ Predictive Analytics
⬡ Cloud-Native AI
⬡ Strategic Advisory
⬡ Data Governance
⬡ RAG Systems
⬡ Feature Stores
What We Do
Five pillars of enterprise AI
End-to-end capability across the full AI lifecycle — from data architecture to strategic transformation.
🧠
AI Eng & ML Models
Production-ready models, RAG, fine-tuning, safety evaluation.
⚡
Decision Intelligence
Human-in-the-loop orchestration with full observability.
📊
Predictive Analytics
High-fidelity forecasts, anomaly detection, interpretable outputs.
☁️
Cloud-Native + MLOps
CI/CD for models, drift detection, canary releases.
🎯
Strategic Advisory
Strategy to operating model, roadmap, accountability.
Why Orvian
We don't prototype.
We produce.
Most AI projects fail between the notebook and production. We've built the systems, the practices, and the culture to close that gap.
01
Governance First
Every engagement starts with data contracts, quality SLAs, and safety evaluation frameworks — not after the model is built, but before a single line is trained.
02
ROI-Driven Fine-Tuning
We don't fine-tune because it's impressive. We fine-tune only when the cost-benefit analysis justifies it. Inference cost at scale is a design constraint.
03
Auditable Decisions
Every automated decision ships with reason codes and rollback. Full observability isn't an option — it's a requirement.
04
Domain-Aware RAG
Generic RAG fails enterprise. We build retrieval systems anchored to your domain context, feature stores, and validated knowledge boundaries.
05
CI/CD for AI
Models deserve the same deployment rigour as software. Containerized pipelines, drift detection, and canary releases as standard practice.
06
Operating Model, Not Just Code
We leave you with a durable operating model: accountability structures, portfolio controls, and measurable success criteria.
Industries
Where our AI creates impact
🏦Financial Services
Risk scoring, fraud detection, regulatory compliance, automated underwriting.
🏥Healthcare
Clinical decision support, patient analytics, operational forecasting.
🏭Manufacturing
Predictive maintenance, supply chain intelligence, quality control.
🛒Retail & E-Commerce
Demand forecasting, personalisation, inventory optimisation.
🚚Logistics
Route optimisation, delay prediction, warehouse automation.
⚡Energy & Utilities
Grid analytics, consumption forecasting, anomaly detection.
🎓EdTech
Adaptive learning, student analytics, content intelligence.
🏗️Real Estate
Valuation models, market intelligence, document automation.
How We Work
From strategy to production in 5 steps
01 — DiscoverStrategy & Audit
Understand your data landscape, current initiatives, and target business outcomes.
→
02 — DesignArchitecture & Contracts
Define data contracts, feature stores, governance model, and evaluation framework.
→
03 — BuildModel Development
RAG, fine-tuning, ensemble models — only what the ROI justifies, always with safety eval.
→
04 — DeployCI/CD & MLOps
Containerized deployment, canary releases, drift detection, full observability.
→
05 — ScaleOperating Model
Durable accountability, portfolio controls, and measurable success criteria.
Solutions
Production AI across the entire enterprise
Every solution is designed to close the gap between proof-of-concept and production — with governance, observability, and measurable outcomes built in.
01 / 05🧠
AI Engineering & ML Models
The ProblemGeneric AI fails in enterprise because data is fragmented, evaluation is fuzzy, and governance is missing.
Our ApproachData contracts and feature stores; RAG with domain context; fine-tuning/distillation only where ROI justifies; safety evaluation before prod.
Production-ready modelsMeasurable qualityLower inference cost
02 / 05⚡
Decision Intelligence & AI Automation
The ProblemManual decision loops slow work and create inconsistent judgments across teams.
Our ApproachHuman-in-the-loop orchestration with deterministic policy checks and clear rollback; full observability with reason codes.
Cycle times downFewer escalationsAuditable consistency
03 / 05📊
Predictive Analytics & Data Intelligence
The ProblemForecasts drift when data quality and context aren't enforced.
Our ApproachData quality SLAs, robust features, and ensembles for time-series and anomaly detection; interpretable outputs for operators.
Higher signal fidelityEarlier anomaliesBetter decisions
04 / 05☁️
Cloud-Native AI + MLOps Enablement
The ProblemModels stall in notebooks without reliable deployment, versioning, or rollback.
Our ApproachContainerized deploys, IaC, feature stores, and CI/CD for models and data; drift detection and canary releases.
Faster releasesHigher stabilityRepeatable ops
05 / 05🎯
Strategic AI Advisory for Enterprise Transformation
The ProblemScattered initiatives without prioritization or an operating model for scale.
Our ApproachStrategy → roadmap → operating model; risk management, portfolio control, measurable success criteria.
Aligned investmentAccountabilityDurable operating model
Technology
Built on the right stack
About Orvian AiTech
We close the gap between vision and production
Orvian AiTech is an AI engineering company built for the enterprise. We exist because most AI projects fail not due to lack of ambition, but due to lack of governance, deployment discipline, and a clear operating model.
Simple. Smooth.Sovereign.
Founded
Pune
Maharashtra, India
🔬
Engineering Rigour
We treat AI like serious software. Data contracts, typed schemas, reproducible pipelines, and safety evaluation are defaults — not options.
🔎
Radical Observability
Every model in production has drift detection, reason codes, and rollback capability. You see everything. Always.
📐
Constraint-Aware Design
We design for your inference budget, latency requirements, and governance constraints — not against them.
🤝
Partnership, Not Projects
We leave you with capability, not dependency. Our goal is a durable operating model your team can own and evolve.
Core Identity
Three words. One promise.
S
Simple
Enterprise AI shouldn't require a PhD to operate. We design systems your operators, analysts, and decision-makers can actually use and trust.
S
Smooth
No model cold starts, no deployment surprises. Smooth means reliable pipelines, canary releases, and zero-downtime deploys as standard.
S
Sovereign
Your data, your models, your operating model. We don't build lock-in. We build capability your team owns, auditors can validate, and leaders can defend.
Get In Touch
Let's build something that actually ships.
We work with enterprises ready to move from scattered AI experiments to governed, production-grade systems. If that's you, let's talk.
