Enterprise AI solutions that work on your data, wherever you operate
We design, build and run production AI for companies in the US, UK, Europe, the Middle East, Asia-Pacific and India: private large language models, retrieval-augmented generation (RAG) with citations, and AI agents that act inside your systems with permissions and an audit trail.
- Cloud, private cloud or on-premise
- Vendor-neutral: hosted or open-weight models
- Overlap hours planned for your time zone
Enterprise AI is a data and integration problem first
Most companies have already tried a chatbot. The hard part of enterprise AI is not the model: it is connecting it to the right data, keeping that data private, making answers checkable and fitting the result into how people already work. Our enterprise AI solutions cover that whole path, from a scored use case to a monitored system in production.
We build generative AI applications on hosted models when that is the right call, and deploy private LLMs on your own infrastructure when data cannot leave it, which for regulated work in Europe, the Gulf and India is often the starting constraint. Retrieval-augmented generation (RAG) grounds answers in your documents with citations, and AI agents carry out multi-step work through your APIs, with a person approving anything irreversible.
OMVIANTECH is an engineering company based in Lucknow, India, delivering remotely to clients worldwide. You get engineers who build and run what they design, overlap hours planned around your time zone, and the code, prompts and infrastructure handed over to you at the end.
Where we deliver
- North America: US and Canada
- UK and Europe, including Germany
- Middle East: UAE, Saudi Arabia, Bahrain
- Asia-Pacific: Singapore and Australia
- India: on site in Lucknow, remote elsewhere
Enterprise AI services we deliver
From a first generative AI use case to agents running inside your core systems: the services below are scoped, built and supported by the same team.
Generative AI development
Copilots, assistants and content workflows built on large language models, designed around a real job your team does every day rather than a demo.
Private & on-premise LLM deployment
Open-weight models served on your hardware or inside your own cloud account and region, so prompts and documents never leave your boundary.
RAG development
Retrieval-augmented generation over your documents and databases: hybrid search, reranking, citations and permission checks before the model reads anything.
AI agent development
Agents that plan, call your APIs and tools, check results and ask a human before anything irreversible, with every step logged.
Custom LLM fine-tuning
Fine-tuning and distillation when a smaller model needs your domain language, format or tone. We tell you when RAG or better prompts will do instead.
AI chatbots & virtual assistants
Customer and employee assistants on web, WhatsApp and internal tools that answer from approved sources and hand over to people cleanly.
Intelligent document processing
Invoices, contracts, claims and forms read, classified and extracted into your systems, with confidence scores and a review queue for the uncertain ones.
Machine learning & predictive analytics
Forecasting, scoring, anomaly detection and recommendations on your operational data, where classic machine learning beats a language model.
AI integration services
AI added to the software you already run (ERP, CRM, helpdesk, data warehouse) through APIs and events, without a rip-and-replace.
LLMOps, evaluation & monitoring
Test sets with expected answers, regression checks before each release, cost and latency tracking, and drift alerts once real users arrive.
AI governance & security
Access control, prompt-injection defences, PII redaction, audit logs and data-retention rules designed in from the first sprint.
Data engineering for AI
Pipelines that clean, chunk, embed and keep your knowledge base current, because retrieval quality is decided long before the model runs.
Neural networks, explained in four steps
Every modern AI system, from image recognition to large language models, is built from the same idea: layers of simple units whose connections are learned from data.
What a single neuron computes
- 01
Inputs become numbers
Text, images or sensor readings are converted into numbers the network can work with.
- 02
Weighted connections
Each connection multiplies the signal by a learned weight; each neuron adds its inputs and applies an activation function.
- 03
Layers learn features
Early layers pick up simple patterns, deeper layers combine them into concepts the task needs.
- 04
Training adjusts weights
During training the error at the output is pushed back through the network (backpropagation) and every weight is nudged to reduce it.
How a large language model writes an answer
An LLM such as GPT, Claude, Gemini or Llama does one thing extremely well: predict the next token. Here is that process on a real business prompt.
Prompt
“Summarise this invoice for the finance team”
Tokens and their vocabulary IDs
␣ marks a leading space. 8 tokens for 7 words.
How retrieval-augmented generation grounds AI in your data
An LLM only knows what it saw in training. RAG retrieves the right passages from your own documents at question time, checks the user may see them, and makes the model answer from them with citations.
How AI agents get multi-step work done
An agent is an LLM that can plan and use tools. The engineering is in what it is allowed to touch, how its results are checked, and where a person signs off.
- goalChase customers with invoices overdue by more than 60 days.
- 1. GoalA person or a schedule gives the agent an outcome to reach.
- 2. PlanThe model breaks the goal into steps and chooses which tools to use.
- 3. ActIt calls approved tools, such as your ERP, CRM, email or database APIs, with scoped permissions.
- 4. ObserveIt reads the results and checks them against the goal and your business rules.
- 5. ApproveAnything irreversible waits for a human approval, and every step is written to an audit log.
Prompting vs RAG vs fine-tuning vs AI agents
The right approach depends on where the knowledge lives and what the system has to do with it. Most production systems combine two or three.
| Approach | Best for | Your data | Typical effort | Trade-off |
|---|---|---|---|---|
| Prompt engineering | General tasks the base model already understands | None stored; context is pasted into each prompt | Days | Cheapest to try; limited by the context window and by what the model already knows |
| RAG (retrieval-augmented generation) | Answers grounded in your documents and live data | Indexed in a vector / hybrid search store and kept current | Weeks | Citable and current; quality depends on retrieval, chunking and permissions |
| Fine-tuning | A fixed format, tone or domain vocabulary at lower cost per call | Curated examples used to update the model’s weights | Weeks to months | Teaches style and behaviour well, facts poorly; needs retraining as data changes |
| AI agents | Multi-step work that needs actions in other systems | Tools and APIs the agent may call, with permissions | Weeks to months | Highest leverage; needs guardrails, approvals and careful evaluation |
Deploy AI where your data needs to live
Data residency, latency and cost decide the deployment, not fashion. We design every solution so the model can move between these options later.
Hosted model APIs
Frontier models from providers such as OpenAI, Anthropic or Google behind a gateway we control. Fastest to start; best when data is not sensitive.
Private cloud, your region
Managed model services such as Azure OpenAI, AWS Bedrock or Google Vertex AI inside your own account and region, for data residency in the EU, UK, Gulf or India.
On-premise / air-gapped
Open-weight models (for example Llama, Mistral or Qwen families) served on your own GPUs. Nothing leaves your network; suits regulated and government work.
Hybrid routing
Sensitive requests stay on a private model, the rest go to a hosted one, behind a single interface so the application never needs to know which.
AI use cases by industry
Where enterprise AI is paying for itself today, and what we build for each sector.
Banking, finance & insurance
Policy and procedure assistants, KYC and claims document extraction, collections agents, fraud and anomaly signals.
Healthcare & life sciences
Clinical guideline search, discharge and referral summaries, coding assistance and private deployment for patient data.
Manufacturing & supply chain
Maintenance copilots over manuals and work orders, demand forecasting, quality inspection notes and supplier document processing.
Retail & e-commerce
Product content generation, conversational search, support assistants and demand and pricing models.
Legal & professional services
Contract review and clause search, knowledge retrieval across matters, drafting assistance with citations.
Real estate & construction
Lead qualification assistants, project document search, cost-sheet and contract extraction.
Education & training
Tutoring assistants grounded in course material, assessment generation and student-support bots.
Government & public sector
On-premise assistants for policy and records, multilingual citizen services and document digitisation.
From AI use case to production
Every engagement starts with measured evidence on your data, so you decide on production with results in hand.
- 01
Discovery & use-case scoring
We look at your workflows and data, score candidate use cases on value, feasibility and risk, and agree success measures before any build.
- 02
Proof of concept on your data
A working prototype on a real slice of your data, with an evaluation set, so the decision to continue rests on measured results.
- 03
Production build
Integration with your systems, permissions, guardrails, monitoring and the user interface your team will actually use.
- 04
Launch, evaluate, improve
Staged rollout, feedback loops, regression tests before every change, and a support arrangement that fits your time zone.
Working with AI teams across time zones
We plan the working day around yours: shared hours for stand-ups and reviews, asynchronous handover for the rest.
- United States (Eastern)UTC−5 · calls by arrangement
- United KingdomUTC+0 · ≈3.5 h overlap
- Germany & EU (CET)UTC+1 · ≈4.5 h overlap
- Saudi Arabia & BahrainUTC+3 · ≈6.5 h overlap
- UAEUTC+4 · ≈7.5 h overlap
- India (our team)UTC+5:30 · our working day
- SingaporeUTC+8 · ≈6.5 h overlap
- Australia (Sydney)UTC+10 · ≈4.5 h overlap
Engagement models
A fixed-scope discovery and proof of concept, a dedicated AI team for a product, or engineers added to your own team.
Contracts, NDA and IP
NDA before discovery, IP assignment in the contract, and the code, prompts, evaluation sets and infrastructure handed over to you.
Data protection by design
Deployment in the region you need, access control before retrieval, PII redaction and audit logs, built to support GDPR and local data-protection law.
One team, many regions
The same engineers across discovery, build and support, so knowledge of your system does not get lost in a handover.
AI technologies we work with
Vendor-neutral by design. The stack follows the requirement, including the parts of it you already run.
- Python
- PyTorch
- Hugging Face
- vLLM
- Ollama
- LangChain
- LlamaIndex
- OpenAI API
- Anthropic API
- Google Gemini
- Azure OpenAI
- AWS Bedrock
- Vertex AI
- pgvector
- Qdrant
- Elasticsearch
- FastAPI
- Next.js
- Docker
- Kubernetes
AI terms, explained plainly
The vocabulary you will hear in any AI project, without the jargon.
- Large language model (LLM)
- A neural network trained on very large amounts of text to predict the next token. That one skill, at scale, lets it summarise, answer, translate, classify and write code.
- Token
- The unit an LLM reads and writes: a word, part of a word or a symbol. Pricing, speed and context limits are all measured in tokens.
- Embedding
- A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together and can be searched by similarity.
- Vector database
- A store optimised for finding the embeddings nearest to a query embedding. It is the retrieval engine behind most RAG systems.
- Retrieval-augmented generation (RAG)
- A pattern where relevant passages are retrieved from your own data and given to the model with the question, so the answer is grounded and can cite sources.
- Fine-tuning
- Further training of a pre-trained model on curated examples to change its behaviour, format or vocabulary. It is a poor way to teach facts that change.
- AI agent
- An LLM-driven system that plans steps toward a goal and calls tools or APIs to carry them out, checking results as it goes.
- Hallucination
- A confident answer that is not supported by fact. Grounding with RAG, citations and evaluation sets are the main defences.
- Context window
- How many tokens a model can consider at once: the prompt, retrieved documents and its own answer together.
- LLMOps
- The practice of evaluating, deploying, monitoring and improving LLM applications in production, the AI counterpart of DevOps.
Enterprise AI — questions buyers ask
Straight answers, including where AI is the wrong tool.
What does an enterprise AI solution typically include?
A model (hosted, private-cloud or on-premise), a retrieval layer over your data, permissions and guardrails, integrations with the systems people already use, an interface, and an evaluation and monitoring setup. The model is usually the smallest part of the work; data, retrieval quality and integration decide whether it is useful.
Do you work with companies outside India?
Yes. We work remotely with teams in the US, UK, Europe, the Gulf and Asia-Pacific, with planned overlap hours for each time zone. Contracts, NDAs and IP assignment are agreed up front, and the code and infrastructure are handed over to you.
Can the AI run on our own servers so data never leaves?
Yes. Open-weight models can run on your own GPUs or inside your cloud account in a region you choose, which is how we handle sensitive and regulated data. Where a hosted model is the better tool and the data allows it, we will say so.
Should we use RAG or fine-tuning?
For answers based on your documents and data that change, use RAG: it stays current and can cite sources. Fine-tuning is for teaching a model a format, tone or vocabulary, or for making a smaller model cheaper to run. Many production systems use RAG first and fine-tune later, if at all.
How do you stop the AI from making things up?
By grounding answers in retrieved sources with citations, refusing to answer when nothing relevant is found, and testing every release against an evaluation set of real questions with expected answers. Hallucinations cannot be eliminated entirely, so the design makes them visible and checkable.
How do you handle data privacy, GDPR and data residency?
We design for your requirements from the start: deployment in the region you need, access control applied before retrieval, PII redaction where required, retention rules and audit logs. Compliance responsibility stays with your organisation; we build the system so that it can meet it.
How much does an AI project cost and how long does it take?
It depends on the use case, data readiness, integrations, deployment model and expected usage. We usually start with a short, fixed-scope discovery and proof of concept on your data, and give a costed plan for production only after the results are measured.
Which LLM do you use?
We are vendor-neutral. We choose between hosted models (such as OpenAI, Anthropic or Google) and open-weight models (such as the Llama, Mistral or Qwen families) on accuracy for your task, data sensitivity, latency and cost, and we keep the application able to switch models later.
Can AI be added to our existing ERP, CRM or software?
Usually, yes. Most of our AI work connects to systems that already exist through their APIs, databases or exports. Where a system has no API, we agree a safe integration route with your team.
Who owns the code, models and data?
You do. Your data stays yours, and the application code, prompts, evaluation sets and any fine-tuned model weights produced for you are handed over under the contract.
Related services, products and reading
Have an AI use case in mind?
Tell us the workflow and the data behind it. We will tell you whether AI is the right tool, which approach fits, and what a proof of concept on your data would take.