AI consulting and solution planning
Assess the workflow, users, available information and expected output. Compare an AI approach with conventional automation, and define the evaluation criteria and integration work before development begins.
MV.tech services
AI consulting and development for business workflows.
Turn a useful AI idea into a working business application. MV.tech builds knowledge assistants, document-processing workflows and AI features connected to company data and tools. We help you choose the use case, build the integration and evaluate how it performs before a wider rollout.
- Remote from Ahmedabad, India
- Overlap with your timezone
- Senior specialists, direct access
- Scope agreed in writing
The starting point
Put AI to work where it has a clear business purpose.
- Help employees find answers in approved company documents and business information.
- Extract and organise information from documents that currently need manual review.
- Add an assistant or an AI-driven step to a product, internal application or reporting workflow.
What we deliver
AI services from use-case selection to application integration.
RAG and knowledge assistants
Build retrieval-augmented generation over an agreed document collection. Include ingestion, search, source references and access rules, with a process for updating the information the assistant uses.
Document processing and workflow automation
Use models to extract, classify or summarise information from documents and business inputs. Add validation and review steps where incorrect output could affect downstream work.
AI application and model integration
Connect GPT, Claude, Llama or Qwen to existing interfaces, APIs and business logic. Test representative tasks, monitor usage and failures, and define which actions need a person’s approval.
- Python
- Model Context Protocol (MCP)
- Custom AI connectors
- LLM APIs (GPT, Claude, Llama, Qwen)
- RAG
- Embeddings
- Agentic workflows
- Document retrieval
- OAuth
- Secret management
- REST APIs
- SQL
- Streamlit
Business applications
Where this service fits.
- Internal knowledge search. An assistant that answers questions from policies, product documentation or operational guides and points users to its sources.
- Document-led operations. Extract structured records from recurring documents and route uncertain results for review.
- AI-enabled reporting. Let users ask questions about approved datasets through an application with controlled query access.
Engagement scope
Start with a use case you can evaluate.
We define the users, information sources and tasks the feature should handle. A first release can focus on one document collection, one processing workflow or a limited set of business questions. Evaluation results then inform broader access, additional integrations and further automation.
See our delivery processWorking together
How we deliver this work.
- 01
Assess the use case
Review business value, source quality, integration requirements and the cost of incorrect output.
- 02
Develop and evaluate
Build the retrieval or workflow, test it against representative inputs and review failures with your team.
- 03
Integrate and improve
Release to the agreed audience, monitor performance and update the application as requirements change.
Teams our engineers have worked with
- Volvo
- BCW
- RootstockLabs
- Chainlabs
- Toptal
- Turing
Before we begin
Questions buyers ask about RAG, LLM integration and AI agents
Not answered here? Ask us directly or read the full FAQ.
What is retrieval-augmented generation, or RAG?
RAG retrieves relevant material from an approved source collection and provides it to a language model when generating an answer. It can help an assistant answer questions about your own information, but retrieval quality, access controls and evaluation still matter.
What is MCP development?
Model Context Protocol (MCP) provides a shared interface for AI applications to connect to tools and data. We build MCP servers in Python around your approved APIs or systems, define the tools and resources they expose, handle authorisation with OAuth and managed secrets, and test them with the intended compatible clients. Access permissions and approval requirements remain part of the application design.
Can an AI agent query our databases safely?
It can, when access is engineered rather than prompted. In practice that means a connector or MCP server that authenticates as a scoped service identity, exposes only approved tables or endpoints, generates SQL against a governed reporting layer, validates the query and the result before returning it, and logs every call. Whitelists and read-only access keep an agent away from anything it has no business touching.
Do we need to train our own AI model?
Not necessarily. The right scope may use an existing model with retrieval and application integration. We review your use case, data, evaluation criteria and operating constraints before proposing an approach.
Can the assistant respect different user permissions?
Role-aware access can be part of the scope. The design must enforce authorisation before protected data is retrieved or an action is performed; instructions written into a prompt are not a substitute for application access controls.
What determines the cost of an AI assistant?
The main factors are source preparation, integrations, permission rules, evaluation, user interface and support. Ongoing costs also depend on model usage, retrieval infrastructure and traffic. These should be considered alongside the build estimate.
Related services
Other ways we can help.
MCP Servers & AI Connectors
Connect AI assistants to the systems your business already uses.
ExploreWorkflow Automation Consulting
Reduce the manual work between your business systems.
ExploreData Engineering Consulting
Build the data foundation behind reporting, analytics and business applications.
ExploreRelated guides
Background reading on AI delivery.
AI approach
RAG or fine-tuning for an internal knowledge assistant?
When each approach fits, with permissions, evaluation and cost in plain language.
MCP explained
What is an MCP server, and when should a business build one?
What an MCP server exposes, when building one makes sense, and the security decisions involved.
AI data access
Letting AI assistants query your data warehouse safely
Scoped identities, checked SQL, row limits and provenance for assistants connected to business data.
Your next step
Discuss your AI project.
Tell us about the work, your current systems and the people who will use the result. We’ll discuss the requirements, delivery options and what an engagement would include.
Book a 30-minute call Email your brief contact@mvtech.solutions
Remote from Ahmedabad, India · Overlap with any timezone · No obligation