AI as a Service (AIaaS)
AI capability delivered as a managed service: hosted models, ready-to-call APIs and monitoring, run for you instead of built from scratch.
OZRIT's AI as a service gives you working AI without building and running the infrastructure yourself. You call an API or use a managed application, in a dedicated environment for your data, with service levels agreed up front and our engineers keeping it running.
Tell us what you need
A consultant replies within one business day. No sales deck.
- 17+years building softwareFounded in Hyderabad in 2009
- 500+in-house engineersFull-time, not freelancers
- ISO/IEC 27001:2022certifiedFor information security
- 98%client retentionLong-term partnerships
AI as a service, explained
Many companies do not want to run AI. They want it to work, from a single call.
AI as a service, often shortened to AIaaS, delivers AI capabilities on a managed basis. Rather than building models, pipelines and infrastructure yourself, you use hosted services: APIs for tasks such as classification, extraction and question answering, or managed applications built on them. The provider runs, monitors and improves the service.
The attraction is speed and predictability: you can start quickly, pay in line with usage and avoid hiring a specialist team to operate infrastructure. The risks are data control, lock-in and fit, which is why the details matter. OZRIT offers dedicated environments for your data, clear terms on how data is used and stored, and the option to move to a private deployment as volumes or compliance needs grow.
We also advise honestly on when a managed service is the right choice and when building your own makes more sense. Often the path is to start with a managed service to prove value, then move parts in-house once you know exactly what you need.
- Managed model hosting
- Ready-to-call AI APIs
- Managed AI applications
- Dedicated environments
- Usage-based pricing options
- Monitoring, support and SLAs
AI as a service offerings
Managed AI you can call, configure and rely on, with the operations handled for you.
Managed model hosting
We host and run the models on your behalf, including scaling, updates and availability.
Ready-to-call AI APIs
Endpoints for common tasks such as classifying, extracting, summarising and answering questions.
Managed AI applications
Assistants, document workflows and analytics delivered as a service, configured to your content.
Dedicated environments
Your data and models live in an isolated environment, in a region you choose.
Usage-based pricing options
Pricing agreed to match how much you use, with limits and alerts to avoid surprises.
Monitoring, support and SLAs
Uptime, response time and quality monitored, with support and service levels agreed in writing.
Security and data controls
Encryption, access control, no-training settings and clear retention rules for your data.
Path to private deployment
Move to your own cloud account or on-premise when scale or compliance calls for it.
How an AI as a service engagement works
Six steps from the first call to a service that runs without you managing it.
Onboard and run
Often live within weeks
- 1
Define the capability
We agree what you need the AI to do and how you will measure whether it does it well.
- 2
Choose the service model
We decide between ready-made APIs, a configured application or a dedicated deployment.
- 3
Set up your environment
We provision an isolated environment, access control and data connections.
- 4
Configure and test
We tune the service on your content and test it on real examples before it is used.
- 5
Go live with service levels
We launch with agreed uptime, support and reporting, and monitor from day one.
- 6
Review and adapt
We review quality, usage and cost regularly and adjust, including moving to private deployment if needed.
AI as a service use cases
Where a managed AI service is the quickest route to value.
Text and email classification
Route support emails, tickets and forms to the right team automatically.
Document data extraction
Turn incoming invoices, forms and IDs into structured fields through an API.
Knowledge assistants
Answer staff or customer questions from your content without running the infrastructure.
Summaries and drafting
Condense long documents and calls, and draft replies, on demand.
Search and recommendations
Add semantic search and suggestions to a website or app through a hosted service.
Analytics and prediction
Forecasts and scores delivered as a service, for teams without a data science group.
AI as a service for your industry
The same build applies across sectors, but the data, rules and risks differ. These are the industries where we are asked for it most.
Fintech app development
Fraud signals, credit scoring, document checks and support assistants for lenders, payments and banks.
Retail and eCommerce app development
Product search, recommendations, demand forecasting and support assistants for stores and marketplaces.
Healthcare app development
Record summaries, triage support, scheduling and personalised plans for providers and health-tech teams.
Logistics app development
Route planning, ETA prediction, proof-of-delivery checks and exception alerts for fleets and supply chains.
Education app development
Tutoring assistants, content generation, assessment help and engagement analytics for learning platforms.
Real estate app development
Lead qualification, property search, valuation support and document handling for developers and brokers.
The technology behind our managed AI
We run the service on the models and cloud that fit your data location, performance and cost needs.
Models
Chosen per task, cost and privacy need
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Hugging Face models
Service layer
APIs and control
- REST APIs
- FastAPI
- API gateway
- OAuth 2.0
- Webhooks
- Redis
Cloud and deployment
In your cloud account or ours
- AWS Bedrock and SageMaker
- Azure OpenAI and ML
- Google Vertex AI
- Docker
- Kubernetes
What decides the cost of AI as a service
Managed AI is usually priced by capability, volume and the level of service you need.
Capabilities included
Each service or API, and any custom configuration, affects the price.
Usage volume
Pricing follows requests, documents or users, depending on the service.
Environment type
A dedicated or private environment costs more than a shared one.
Service levels
Higher uptime, faster support and stricter response times raise the price.
Customisation
Tuning on your content and integrations adds set-up effort.
Compliance needs
Regional hosting, audit logs and retention rules affect the design and cost.
Security and privacy in AI as a service
A security or compliance team will ask where the data goes, who can see it and how quality is checked. These are the safeguards we build into every project.
Where your data lives
Models can run in your own cloud account or on private endpoints, with provider settings that stop your data being used for training. You choose the region.
Access control
Assistants respect the permissions of the person asking. Someone who cannot open a document cannot get its content through a chat window either.
Testing before launch
We build an evaluation set from your real cases and measure accuracy, safety and cost before and after every change, not just once at launch.
Certification and ownership
OZRIT is ISO/IEC 27001:2022 certified for information security. You own the code, prompts, models you commission and your data.
All eight AI safeguardsCertifications and AI frameworks we work to
AI as a service: frequently asked questions
Straight answers on cost, timelines, data, security and ownership.
What is AI as a service?
AI as a service, or AIaaS, is AI capability delivered as a managed service. You use hosted models, APIs or managed applications while the provider runs, monitors and improves the infrastructure and the service.
When should we choose AIaaS instead of building our own AI?
A managed service suits teams that want to start quickly, lack in-house AI operations skills, or have needs that standard capabilities cover well. Building your own suits unique requirements, strict data control or very high volumes.
How much does AI as a service cost?
Pricing depends on the capabilities, usage volume, environment and service levels. We agree pricing to match your usage. A discovery sprint from $1,000 clarifies the right model and ends with a scoped quote.
How quickly can we start?
Often within weeks, because the infrastructure and many capabilities already exist. Set-up time depends on configuration, integrations and any security review we need to complete.
Is our data safe in a managed AI service?
We use dedicated environments, encryption, access control and no-training settings, and we agree retention and usage terms in writing. OZRIT is ISO/IEC 27001:2022 certified for information security.
Will our data be used to train models?
Not for third-party training. We configure providers' no-training options and agree in writing how your data is stored and used. If you need stricter control, we can move to a private deployment.
Can we move to a private deployment later?
Yes. Many clients start with a managed service and later move to their own cloud account or on-premise as volumes or compliance needs grow. We plan for that path from the beginning.
What service levels do you offer?
We agree uptime, response times, support hours and reporting up front and put them in the contract. The levels depend on the service and your needs, and they are priced accordingly.
Do we avoid vendor lock-in?
We build on open interfaces and keep a gateway between your systems and the models, so providers can be changed with limited impact. You keep ownership of your data and your configuration.
Can we customise the service to our content?
Yes. We configure the service on your documents, terminology and rules, and test it on your real examples. Heavier customisation is scoped separately.
Try managed AI without the build
Tell us what you want AI to do and how much you expect to use it. We will recommend a managed service or a build, and what getting started would take.
What happens next
- 1An AI consultant reads your note and may reply with a few questions.
- 2We hold a call to understand the problem, your data and the systems involved.
- 3If AI fits, we propose a discovery sprint or a scoped pilot with clear success measures.