Capability
Enterprise AI Platform across India
A governed internal platform so every team ships AI safely, shared models, guardrails, costs and audit in one place.
- Industries
- 30
- Stack options
- 10
- Typical first release
- 6 weeks
What enterprise ai platform means when we build it
Orqent Labs builds the internal platform layer so your teams get the speed of direct API access with the audit trail your risk function requires.
We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
Built by engineers who ship production systems, not by a practice that subcontracts the build. You own the code, the models where they are open-weight, and the documentation to run it without us.
What is included
- Model gateway across providers with failover
- Central prompt and template registry with versioning
- Per-team quotas, budgets and cost allocation
- Policy enforcement, PII handling, allowed models, data residency
- Full audit log of every prompt and response
- Self-service onboarding for product teams
Who this is for
We usually work with CIOs, enterprise architects, platform teams and AI governance leads, the people who own the outcome rather than the tooling decision.
Enterprise AI Platform by industry
Each sector changes the constraints, regulation, systems of record, and what a wrong answer costs.
- Enterprise AI Platform for Healthcare & HospitalsDPDP Act 2023
- Enterprise AI Platform for Pharmaceuticals & Life SciencesCDSCO
- Enterprise AI Platform for Financial ServicesRBI guidelines
- Enterprise AI Platform for InsuranceIRDAI regulations
- Enterprise AI Platform for Legal ServicesBar Council rules
- Enterprise AI Platform for ManufacturingISO 9001
- Enterprise AI Platform for Logistics & Supply Chaine-way bill compliance
- Enterprise AI Platform for Retailconsumer protection rules
- Enterprise AI Platform for Education & EdTechDPDP Act 2023
- Enterprise AI Platform for Government & Public SectorDPDP Act 2023
- Enterprise AI Platform for Energy & UtilitiesCEA regulations
- Enterprise AI Platform for SaaS & TechnologySOC 2
- Enterprise AI Platform for Media & Entertainmentcopyright law
- Enterprise AI Platform for Real Estate & Construction TechRERA compliance
- Enterprise AI Platform for TelecommunicationsTRAI regulations
- Enterprise AI Platform for Agriculture & AgritechFSSAI standards
- Enterprise AI Platform for HospitalityFSSAI for food service
- Enterprise AI Platform for AutomotiveAIS standards
- Enterprise AI Platform for Construction & Infrastructurebuilding codes
- Enterprise AI Platform for Professional Servicesprofessional body standards
- Enterprise AI Platform for Nonprofit & DevelopmentFCRA compliance
- Enterprise AI Platform for Travel & Tourismtourism ministry guidelines
- Enterprise AI Platform for BankingRBI master directions
- Enterprise AI Platform for E-commerceconsumer protection e-commerce rules
- Enterprise AI Platform for Recruitment & HR Techlabour laws
- Enterprise AI Platform for Mining & MetalsDGMS safety regulations
- Enterprise AI Platform for Textiles & Apparelexport documentation requirements
- Enterprise AI Platform for Chemicals & Process IndustryPESO licensing
- Enterprise AI Platform for AviationDGCA regulations
- Enterprise AI Platform for Defence & Aerospacesecurity clearance requirements
Enterprise AI Platform, stack options
We pick per workload. Each page states the honest trade-off.
- Enterprise AI Platform with Claudemodel
- Enterprise AI Platform with OpenAI GPTmodel
- Enterprise AI Platform with Google Geminimodel
- Enterprise AI Platform with AWS Bedrockplatform
- Enterprise AI Platform with Azure OpenAIplatform
- Enterprise AI Platform with TypeScriptframework
- Enterprise AI Platform with Pythonframework
- Enterprise AI Platform with PostgreSQLdata
- Enterprise AI Platform with Kubernetesinfra
- Enterprise AI Platform with Vercelinfra
Questions we get asked
Why not let teams call the APIs directly?
Because you lose cost visibility, audit trail and policy enforcement, and you end up with keys in a dozen repositories. A gateway gives teams the same speed with none of that exposure.
Does it lock us to one model provider?
The opposite, the gateway is what makes providers swappable, with failover when one has an outage.
How long does a platform take?
A usable first version with gateway, logging and quotas typically lands in six to eight weeks; governance depth grows from there.
Considering enterprise ai platform?
Tell us the workflow and the constraint. We will tell you honestly whether it is worth building.
Or email bd@dtrasglobal.com · call +91 74118 77878
