Service · Remote-first senior specialists
Turn a specific business problem into a useful AI solution. MV.tech builds AI automation, retrieval-augmented generation systems, Model Context Protocol (MCP) servers, custom AI connectors and agentic workflows that link approved company knowledge, databases, APIs and internal tools to models such as GPT, Claude, Llama and Qwen.
The starting point
What we deliver
Plan document ingestion, chunking, retrieval and source references around a defined knowledge collection. Account for updates, permissions and questions the available material cannot answer, and put retrieval behind your own access rules.
Connect an assistant to a support workflow, internal tool or customer application. Multi-step workflows can retrieve context, generate SQL, validate a result and produce a formatted output, with permitted actions defined and a person in control of approvals and exceptions.
Build Model Context Protocol servers in Python that expose approved tools and data to compatible AI applications. Define tool inputs, resource access, authorisation through OAuth and managed secrets, and logging around the business systems being connected.
Connect enterprise AI platforms and agents to databases, APIs and internal tools with scoped, token-aware access. Package recurring tasks — retrieval, SQL generation, validation, formatted export — as reusable connectors, plugins and AI skills rather than one-off prompts.
Integrate document processing, classification, extraction or decision support into an existing workflow. LLM-assisted extraction can pull structured records out of documents, APIs and web sources; agree the expected output, validation rules and review path before automating a business action.
Bring responses from GPT, Claude, Llama or Qwen into existing business logic and user interfaces. Validate inputs and outputs, handle failures, compare models against your own task, and keep application permissions under server-side control.
Agree representative questions and expected behaviour. Review source grounding, unsupported answers, latency and usage cost before expanding the feature's audience. Where an assistant touches financial or audited systems, access is scoped with OAuth and managed secrets and every tool call is logged, which is the kind of evidence a SOC 2 programme asks for.
A useful first project
Choose a defined set of documents and a small group of users. Collect questions they actually ask, including questions outside the documents' scope. A first release can then be assessed against source references, correct handling of missing information and the time it takes to find an answer. Wider access, an MCP server, a connector into a live database or automated actions can be scoped once those fundamentals are clear.
See our delivery processWorking together
Identify users, approved sources, permissions and the decisions the assistant will support.
Connect retrieval, model behaviour and the interface. Test with real questions and review failure cases.
Release within an agreed scope, track answer quality and usage, and document the update process.
Teams our engineers have worked with
Before we begin
Not answered here? Ask us directly or read the full FAQ.
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.
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.
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.
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.
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.
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.
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ExploreRelated guides
AI approach
When each approach fits, with permissions, evaluation and cost in plain language.
MCP explained
What an MCP server exposes, when building one makes sense, and the security decisions involved.
Buyer's checklist
Who does the work, how the first milestone is scoped and how correctness is proven.
Your next step
Bring your goal, current tools and preferred working hours. We use the first 30-minute conversation to clarify fit and an initial scope, and you leave with a written next step.
Remote from Ahmedabad, India · Overlap with any timezone · No obligation
