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46 AI platforms across five layers
AI Platforms and AI Integration Services
We build production AI on OpenAI, Claude, Gemini, Llama, Snowflake, Databricks, Pinecone and the rest of the modern AI stack. Our AI integration services pick the model, framework, data platform, vector database and monitoring that fit your job, then connect them to your product. Fixed price after one scoping call, your data stays in your cloud, and the first workflow goes live in 4 to 8 weeks.
NDA first. Reply within 12 hours. You talk to an AI architect, not a sales rep.
Your production AI stack 5 layers
Layer 1Model
+8
+8
Layer 2Framework
+6
+6
Layer 3Data platform
+6
+6
Layer 4Vector search
+6
+6
Layer 5Monitoring
+7
+7 A routing layer sits between them, so any piece can be swapped later without a rewrite.
- 300+Projects launched since 2017
- 70+Senior engineers
- 22+Countries served
- 93%Returning client base
- 46AI platforms across five layers
Start with the problem
Tell us the job and we point you to the right AI platform.
Most buyers do not shop by vendor name. Pick the job closest to yours and go straight to the platform pages that fit it.
Search and answers on my own documents
LangChain or LlamaIndex with Pinecone or Milvus, on any model. Answers come with citations.
AI agents on Azure or AWS
OpenAI or Claude through your own cloud account, with LangChain running the workflow and approval steps.
A private model on my own servers
Llama or Mistral, served and tuned with Hugging Face tools, so no data leaves your network.
A data platform ready for AI
Snowflake, Databricks or Redshift, with Airbyte moving the data in from the tools you already use.
Monitoring for a live AI app
See every request, cost and failure in one place, and catch bad answers before users do.
A cheaper or faster model
Move between Gemini, Mistral and Llama behind one routing layer, without a rewrite.
The stack
Five layers, 46 AI platforms, one engineering team.
An AI platform is the software a production AI system runs on: the model that answers, the framework that runs it, the data platform underneath, the vector database it searches and the tools that monitor it. Pick one from each layer, or let us pick for you.
- AI models 12
- RAG and agent frameworks 8
- Data platforms 9
- Vector databases 9
- MLOps and observability 8
Live pageNew pageCards marked New page are platforms we work with whose own page is coming soon.
Layer 1
AI models
Hosted models for speed to market. Open models when data must stay inside your network.
Also covered: Open-weight models such as gpt-oss, on the OpenAI page. LaMDA and PaLM fold into the Gemini page, and BERT into Hugging Face.
Consulting
OpenAI
Fastest route to GPT apps and agents
OpenAI consulting services
Integration
Anthropic Claude
Long tasks, coding and agents that use software
Claude integration services
Integration
Google Gemini
Images, audio and very long documents on Google Cloud
Gemini integration services
Development
Meta Llama
Open model for private hosting and tuning
Llama development services
Integration
Mistral AI
Small, fast models with EU data residency
Mistral integration services
Development
Hugging Face
Open models, the BERT family and testing tools
Hugging Face services
New page
Azure OpenAI
OpenAI models inside your own Azure tenant
Page coming soon
New page
Amazon Bedrock
Claude, Llama and more through your AWS account
Page coming soon
New page
Google Vertex AI
Gemini, agents and model tuning on Google Cloud
Page coming soon
New page
DeepSeek
Low cost reasoning models, hosted or self hosted
Page coming soon
New page
Qwen
Alibaba's open models for multilingual and private use
Page coming soon
New page
Grok (xAI)
xAI's Grok models through the API
Page coming soonLayer 2
RAG and agent frameworks
The code that connects a model to your documents, tools and approval steps.
Development
LangChain
Document search and agents with saved state
LangChain development services
Development
LlamaIndex
Best when the job is mostly documents
LlamaIndex development services
New page
LangGraph
Agents with state, branches and human approval
Page coming soon
New page
CrewAI
Teams of role based agents that work together
Page coming soon
New page
Microsoft Agent Framework
AutoGen and Semantic Kernel agents on Azure
Page coming soon
New page
MCP server development
Connect agents to your tools and data through MCP
Page coming soon
New page
n8n
Workflow automation with AI steps, self hosted or cloud
Page coming soon
New page
Dify
Low code LLM apps and agents
Page coming soonAlso covered
OpenAI Agents SDK, Google ADK, Pydantic AI, Mastra, and agent memory layers such as Mem0, Letta and Zep.
Layer 3
Data platforms
Warehouses and pipelines that make company data clean enough for a model to use.
Also covered: Snowflake Cortex AI, Databricks Mosaic AI and Unity Catalog, change data capture with Debezium, and document pipelines with Unstructured.
Consulting
Snowflake
Governed analytics with built in AI features
Snowflake consulting services
Consulting
Databricks
Data engineering and model training in one place
Databricks consulting services
Consulting
Amazon Redshift
The warehouse for AWS first teams
Redshift consulting services
Consulting
MongoDB Atlas
Documents and vector search in one database
MongoDB Atlas services
Integration
Airbyte
Moves data from 400+ sources into your platform
Airbyte integration services
New page
Google BigQuery
Google Cloud warehouse with built in ML
Page coming soon
New page
Microsoft Fabric
One analytics platform for Azure teams
Page coming soon
New page
dbt
Tested, documented data models ready for AI
Page coming soon
New page
Fivetran
Managed data pipelines with little upkeep
Page coming soonLayer 4
Vector databases
Where the model's search index lives. Managed for zero upkeep, open source for control.
Also covered: Turbopuffer, LanceDB and Vespa.
Vector DB
Pinecone
Fully managed, nothing to run yourself
Pinecone services
Vector DB
Weaviate
Keyword and meaning search together
Weaviate services
Vector DB
Milvus and Zilliz
Open source at very large scale, managed on Zilliz
Milvus services
Development
Supabase
Postgres with vector search for app teams
Supabase development services
New page
Qdrant
Fast open source vector search with filters
Page coming soon
New page
Chroma
Lightweight vector store for apps and prototypes
Page coming soon
New page
pgvector
Vector search inside the PostgreSQL you already run
Page coming soon
New page
Elasticsearch and OpenSearch
Keyword and vector search in one engine
Page coming soon
New page
Redis vector search
Very low latency vector search, plus caching
Page coming soonLayer 5
MLOps and observability
Tracking, testing and cost control once the AI system is live.
Consulting
Datadog
Every request, cost and failure of a live AI app in one view
Datadog consulting services
New page
LLMOps services
Run, test and improve LLM apps after launch
Page coming soon
New page
LangSmith
Tracing and evaluation for LangChain apps
Page coming soon
New page
Langfuse
Open source LLM tracing, prompts and evals
Page coming soon
New page
Arize Phoenix
Open source evaluation and tracing for LLM apps
Page coming soon
New page
MLflow
Experiment tracking and a model registry
Page coming soon
New page
Weights and Biases
Experiment tracking and Weave evaluations
Page coming soon
New page
Portkey
An AI gateway for routing, caching and cost control
Page coming soonAlso covered
Private inference with vLLM, Ollama and NVIDIA NIM, covered on private AI deployment.
How we choose
How we pick an AI platform for your project.
Four questions decide most platform choices. We answer them on the first scoping call and write the reasoning into the proposal.
Cost and speed
How many requests a month, and how fast each answer must come back. A hosted model is cheaper at low volume; an open model is cheaper at high volume.
Where the data may go
HIPAA, GDPR or client contracts decide whether data can leave your cloud. That removes half the options before we look at quality.
What you already run
If you are on Azure, we build on Azure OpenAI and Fabric. If you are on AWS, it is Bedrock and Redshift. We work with your setup, not against it.
How easy it is to leave
Every build has a routing layer, so the model or vector database can be swapped later without a rewrite. We show you the exit path before you sign.
Not sure which platform fits?
Book a 30 minute call. You get a written platform recommendation, with no obligation.
Delivered work
See all case studies
AI products we shipped, with the stack behind them.
Products we designed, built and shipped for clients, with the results each one reported.
Figures are client-reported and copied from each case study.
Services
The AI engineering services behind every platform.
Platform pages describe the tools. These nine pages describe the work, and each one links back to the platforms it runs on.
AI development
Custom models and AI applications, end to end.
Explore the serviceGenerative AI development
Copilots and apps that work with text, images and voice.
Explore the serviceAI agent development
Agents that use tools, remember context and ask for approval.
Explore the serviceEnterprise RAG development
Answers from your own documents and data, with citations.
Explore the servicePrivate AI deployment
Open models on your own cloud or servers.
Explore the serviceLLM development
Custom and fine tuned language models for your domain.
Explore the serviceAI chatbot development
Chat and voice assistants grounded in your data.
Explore the serviceAI consulting
Strategy, platform choice and a roadmap before you build.
Explore the serviceAgentic AI consulting
Where agents pay off in your business, and where they do not.
New page coming soonAlready live? App maintenance covers monitoring, evaluation and model updates as a monthly plan.
FAQ
AI platform questions buyers ask us.
Which AI model should we use for regulated data?
Azure OpenAI, Amazon Bedrock or a private Llama deployment, depending on where your data must stay. We map the data rule first, then pick the model.
Can we change the model later without a rewrite?
Yes. Every build has a routing layer, so the model behind it can change in days, not months.
Do you resell platform licences?
No. You contract with the vendor directly and own the account. We build on it and hand over everything.
How long does an AI platform project take?
Four to eight weeks for the first workflow, at a fixed price. A full migration takes longer and is scoped after discovery.
What does AI platform work cost?
AI builds start from $17,000 and agentic automations from $8,000. The exact price is fixed after one 30 minute scoping call. For a first estimate, try the AI cost calculator.
Which vector database should we choose?
Pinecone if you want zero operations, Milvus or Weaviate if you want open source and control, Supabase if you already run Postgres. We confirm the choice on the scoping call from your data volume and hosting rules.
Can our data stay in our own cloud?
Yes. We build on Azure OpenAI, Bedrock, or open models such as Llama and Mistral inside your own cloud or servers. See private AI deployment.
Do you work with AI platforms not listed here?
Yes. Tell us what you run. Besides the platforms on this page, we work with tools such as Pydantic AI, Mastra, vLLM, Ollama and NVIDIA NIM.
Who owns the code and the models?
You do. Ailoitte transfers 100 percent of the IP, and your platform accounts are in your name from day one.
How do you keep AI run costs under control?
We route simple requests to small models, cache repeated answers and set token budgets. Then we quote the monthly platform bill alongside the build price.
Do you offer support after launch?
Yes. Monitoring, evaluation and model updates are available as a monthly plan through our app maintenance service.
Where is the Ailoitte team based?
Engineering is in Bengaluru, with shifts covering US, UK and EU hours. The US entity is Ailoitte LLC in Dover, Delaware.
Tell us what you are building.
One 30 minute call, then a platform recommendation and an exact price with a fixed scope and a date. A good AI platform consulting partner should also tell you when a hosted model is enough. If something simpler would serve you better, we will say so on that call.
- NDA signed before we look at anything
- A senior AI architect on the call, not a salesperson
- A written scope, price and date within 12 hours
- No obligation, and nothing to pay to find out
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"Ailoitte understood our requirements immediately and built the team we wanted. On time and budget. Highly recommend working with them for a fruitful collaboration."
Priyank Mehta
Head of product, Apna
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- EduTech
My experience working with Ailoitte was highly professional and collaborative. The team was responsive, transparent, and proactive throughout the engagement. They not only executed the core requirements effectively but also contributed several valuable suggestions that strengthened the overall solution. In particular, their recommendations on architectural enhancements for voice‑recognition workflows significantly improved performance, scalability, and long‑term maintainability. They provided data entry assistance to reduce bottlenecks during implementation.
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CEO, Sanskritly
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- FinTech
On paper, Banksathi had everything it took to make a profitable application. However, on the execution front, there were multiple loopholes - glitches in apps, modules not working, slow payment disbursement process, etc. Now to make the application as useful as it was on paper in a real world scenario, we had to take every user journey apart and identify the areas of concerns on a technical end.
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CEO, Banksathi
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“Working with Ailoitte was a game-changer for us. They truly understood our vision of putting ‘Health in Your Hands’ and brought it to life through a beautifully designed, intuitive app. From user experience to performance, everything exceeded our expectations. Their team was proactive, skilled, and aligned with our mission every step of the way.”
Saurabh Arora
Director, Dr.Morepen
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“Working with Ailoitte was a game-changer. Their team brought our vision for Reveza to life with seamless AI integration and a user-friendly experience that our clients love. We've seen a clear 25% boost in in-store engagement and loyalty. They truly understood our goals and delivered beyond expectations.”
Manikanth Epari
Co-Founder, Reveza
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- India
- HealthTech
“Ailoitte truly understood our vision for iPatientCare. Their team delivered a user-friendly, secure, and scalable EHR platform that improved our workflows and helped us deliver better care. We’re extremely happy with the results.”
Dr. Rahul Gupta
CMO, iPatientCare
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- India
- EduTech
"Working with Ailoitte was a game-changer for us. They truly understood our vision of putting ‘Health in Your Hands’ and brought it to life through a beautifully designed, intuitive app. From user experience to performance, everything exceeded our expectations. Their team was proactive, skilled, and aligned with our mission every step of the way."
Saurabh Arora
Director, Dr. Morepen
