AI & Intelligent Systems
Custom AI assistants, document readers and RAG systems
Brihat Infotech builds language-model systems that answer from your own documents and read the paperwork your teams key in by hand. The system runs in your environment, and the code sits in your repository.
What we build
- Knowledge assistants (RAG)
staff ask a question and get an answer built from your policies, manuals or contracts, with the source passage shown.
- Enterprise copilots
an assistant inside the screen your team already works in.
- Document intelligence
invoices, KYC files, contracts and insurance claims read into structured fields.
- Fine-tuned models
when a general model does not know your terms.
- Ingestion and freshness pipelines
new and changed documents reach the assistant without manual uploads.
- A model abstraction layer
you can switch the model behind the assistant without rewriting it.
What the build hands over
- A working system in your own environment
- A scored test set built from your real cases, each with a known answer
- A data-boundary note: what reaches a model and what stays inside
- A cost model per transaction, so you know the monthly bill before launch
How a build runs
A 2-week trial on your own documents comes first. It shows what the model reads correctly and where it fails, before you commit to the full build. The production build then follows, and every fortnight a release reaches a live environment.
Two AI engineers, one platform engineer and an architect make up the team. We pick the model that fits the data: Claude or GPT APIs under zero-retention terms, or open-source models hosted in your own network when documents cannot leave it. Vector databases, embeddings, LangGraph for multi-step flows and an evaluation framework do the rest.
For the full project path, see how we engage.
Reading paperwork in detail
For invoices, KYC files, contracts and claims, the system pulls named fields out of each page, checks them against rules you give it, and shows the reviewer the page beside the extracted values. A field it cannot read confidently is highlighted instead of guessed. Clean files pass through, so reviewers spend their time on the odd ones.
Ingestion pipelines keep the knowledge side current. When a policy document changes, the assistant stops quoting the old version.
Choosing the model
You do not have to commit to one model. The abstraction layer sits between your application and the model, so the vendor can change when prices or quality change. Where documents are sensitive, an open-source model on your own servers keeps every page inside your network. The cost model per transaction lets you compare the running cost of each option before you choose.
Related work
Edmertion, college matching. A model-backed counselling app that takes a student's profile and suggests colleges, delivered as web, mobile and an admin portal. See the Edmertion case study
Questions buyers ask
RAG stands for retrieval-augmented generation. The system first finds the passages in your documents that relate to a question, then asks the language model to answer from those passages only and to show where each answer came from.
Bring a sample of the documents you want read or searched.
Tell us what you want to build.
The first call is 45 minutes with an architect, usually the founder. It is free, and we will sign an NDA first if you ask.