Samajh — a reader for the letters my mother couldn't read

Aditya D — Fri, 02 Oct 2026 23:31:33 +0000

Samajh — a reader for the letters my mother couldn't read

The letter

Every few weeks, an official letter arrives at home — a bank notice, an insurance renewal, a hospital bill, or another document written in formal English.

My mother can understand the underlying idea, but the language of the document makes the important part difficult to follow.

The problem is not always complexity. Sometimes one sentence determines whether something needs to be paid, when it is due, or what action is expected.

I kept seeing someone else read and explain these documents.

Understanding depended on another person.

I wanted to build something that could help with that first step.

That became Samajh.

What I built

Samajh is a document reader and explainer designed for people who may understand their own language better than the formal language used in official documents.

You provide a document, and Samajh explains it in simple language and in the reader's preferred language.

Instead of producing one large AI-generated summary, it focuses on five things:

What is this?
What is the gist?
What does it want from me, and by when?
What numbers and dates matter?
What is easy to miss, and what is uncertain?

Every factual explanation is connected back to the source document.

Input formats

Samajh can work with:

Plain text
.txt
.md
.csv
PDF documents
Photos/images when the configured AI model supports image input

It supports multiple languages, including English and Telugu, so the explanation can be presented in the reader's own language.

There is also deliberately no account, dashboard, or document history.

The goal is simple:

Understand the document first.

Why quotes matter

Large language models can produce fluent answers while still being wrong.

That becomes a serious problem when the document contains:

payment amounts
deadlines
dates
policy conditions
instructions
warnings

So Samajh does not treat the model's explanation as the final truth.

For each factual claim, the system tries to preserve evidence from the original document.

The flow is roughly:

Claim → Source quote → Verification

The quote is independently checked against the extracted document text.

If the system cannot verify the claim, it should not quietly present it as a fact.

Instead, it can mark information as uncertain or leave it out.

This was one of the most important design decisions in the project.

The goal is not to make AI sound confident. The goal is to make the result checkable.

How it works

flowchart TD
    A[Upload document] --> B[Extract text]
    B --> C[Structured AI analysis]
    C --> D[Validate model output]
    D --> E[Normalize and recover output]
    E --> F[Ground claims to source]
    F --> G[Reader-friendly explanation]

DEV Community: Aditya D

Samajh — a reader for the letters my mother couldn't read

Samajh — a reader for the letters my mother couldn't read

The letter

What I built

Input formats

Why quotes matter

How it works