What does LLM integration mean?

It means connecting a large language model, the technology behind AI chat assistants, to your own product, documents or workflow so it does one specific job, such as answering questions from your files.

Do we need an AI agent, or is something simpler enough?

Often something simpler is enough. When the steps are known, a fixed workflow with a model doing one step is cheaper, faster and easier to check. We recommend an agent only when the steps cannot be written in advance, and we tell you which we advise before you commit.

Is it safe to use AI with our business data?

Before we build, we agree with you what data the AI may see and which service processes it, and we check that service's data-retention settings. Every AI feature includes a human approval step for actions that matter.

How do you know the AI output is good?

We test it on real examples from your work before launch, measure how often it is right, and keep checking after launch. Where it is unsure, it hands the case to a person.

All services

AI automation, chatbots and LLM integration

Ajima Innovation is an AI development and automation company in Kathmandu, Nepal, helping businesses in Nepal and abroad get more value from their products and systems with AI. We add AI features to your product, or integrate AI into the systems you already run, so routine work gets faster and your team stays in control. We start with the simplest design that does the job, and a person approves every action that matters.

What we build

We do not replace what works. We add AI to your product, or integrate it into your existing systems, for tasks where it saves real time. Usually that is a fixed workflow where a model does one clear step. We use an agent only when the steps cannot be written down in advance.

Document processing. Read, sort and pull key details from invoices, forms, contracts and emails. Unclear cases go to a person instead of being guessed.
AI chatbots that answer from your documents. Staff or customers ask a question in plain language and get an answer drawn from your documents, with the source shown so it can be checked.
Internal assistants. Assistants that help with routine work, such as drafting replies, summarising long email threads or preparing a first version of a report.
AI agents and workflow automation. When a task needs several steps across your tools, an agent can carry them out. It runs inside a harness that limits which tools it may use, remembers relevant earlier context, and connects through the Model Context Protocol (MCP) where it fits. A person approves anything consequential.
Quality checks. We test AI output against real examples from your work before launch and keep checking after, so you can see how often it is right and where it fails.

How a project runs

For AI work, we begin with a small test on your real documents or tasks, so you see what is possible before you commit to a full build. Every project starts with a first conversation, where we check that it is work we can do well. In discovery we agree the scope, assumptions and risks in writing, with written acceptance criteria for each feature. You then receive a written proposal with an estimate range, a timeline and the risks. Delivery runs in one- to two-week iterations, and each one ends with a demo or a written update. Every change is reviewed by a person, and every release is tested on a staging site first. Anything outside the signed scope becomes a change request with an estimate, and work starts only after you approve it. The whole path is described on the page about our software development process.

Technology

LLM APIs, document search (RAG), graph memory, an agent harness, MCP, output evaluation and human approval steps. We choose the simplest design that works, and we explain why.

Support after launch

We stay after launch. AI features change as models, your data and your business change, so monitoring, quality re-checks and updates are available on a monthly retainer. Every quote gives a range, includes testing, documentation, deployment and handover, and states what is out of scope.

Questions

What does LLM integration mean?: It means connecting a large language model, the technology behind AI chat assistants, to your own product, documents or workflow so it does one specific job, such as answering questions from your files.
Do we need an AI agent, or is something simpler enough?: Often something simpler is enough. When the steps are known, a fixed workflow with a model doing one step is cheaper, faster and easier to check. We recommend an agent only when the steps cannot be written in advance, and we tell you which we advise before you commit.
Is it safe to use AI with our business data?: Before we build, we agree with you what data the AI may see and which service processes it, and we check that service's data-retention settings. Every AI feature includes a human approval step for actions that matter.
How do you know the AI output is good?: We test it on real examples from your work before launch, measure how often it is right, and keep checking after launch. Where it is unsure, it hands the case to a person.

Tell us about your project