NLP development
Language understanding for every document and conversation.
Agleam Labs builds natural language processing systems that read documents, sort messages, extract key facts, gauge sentiment, and summarise long text. We work across languages, including Bangla, and design each pipeline for the accuracy and volume your operation needs.
- Information extraction
- Text classification and routing
- Sentiment and summarisation
- Multilingual, including Bangla
What we do
Structure from unstructured text.
Emails, contracts, reviews, chat logs, and reports carry information your systems cannot use until someone reads them. Natural language processing does that reading at scale: pulling out names, dates, amounts, and clauses, assigning categories, detecting tone, and condensing long material into a few lines. The output is structured data that feeds your workflows and reports.
We combine large language models with smaller specialised models, choosing per task for accuracy, speed, and cost. Being based in Dhaka, we bring practical experience with Bangla text, including mixed Bangla and English and romanised Bangla, alongside English and other major languages.
What we deliver
NLP for documents, messages, and conversations.
Each capability can run on its own or as part of a larger document or support pipeline.
01 Information extraction +
- Entity extraction
- Contracts
- Invoices
- Key-value fields
- Validation
Pulling named entities, dates, amounts, parties, and clauses from contracts, invoices, applications, and reports into validated fields.
02 Text classification and routing +
- Intent detection
- Ticket triage
- Topic tagging
- Priority
Sorting emails, tickets, and forms by topic, urgency, or intent, and sending each to the right queue or team automatically.
03 Sentiment and feedback analysis +
- Sentiment
- Aspect analysis
- Themes
- Voice of customer
Measuring tone and recurring themes across reviews, surveys, social posts, and support conversations, with trends shown in dashboards.
04 Summarisation +
- Document summaries
- Call summaries
- Briefings
- Key points
Concise summaries of long documents, call transcripts, case histories, and meeting notes, in formats tailored to each audience.
05 Bangla and multilingual NLP +
- Bangla
- Code-mixed text
- Translation
- Language detection
Processing Bangla, English, and mixed-language text, including romanised Bangla, with language detection, translation, and language-specific tuning.
06 Conversational language understanding +
- Intents
- Slot filling
- Dialogue context
- Speech to text
The language layer behind chat and voice assistants: intent recognition, entity capture, and context tracking across a conversation.
07 Document comparison and search +
- Similarity
- Deduplication
- Clause comparison
- Semantic matching
Finding differences between document versions, spotting duplicate or near-duplicate text, and matching documents by meaning.
Why NLP
Text becomes data you can act on.
-
Hours of reading saved
Documents and messages are processed in seconds instead of read line by line.
-
Faster routing
Requests reach the right team immediately, without a manual sorting step.
-
Insight from feedback
Trends in customer sentiment and complaints show up before they become problems.
-
Local language support
Customers can write in Bangla or English and still be understood correctly.
-
Fewer entry errors
Extracted data is validated automatically before it reaches your systems.
-
Drowning in documents or messages? Book a free consultation and we will suggest where NLP fits.
Book a free consultation →
Why Agleam Labs
Language AI with local depth.
Hover or tap a card to see what it means in practice.
How we work
From raw text to structured results.
-
01
Sample and define
We study real samples of your text and agree the fields, labels, or outputs you need.
-
02
Label and baseline
We build a labelled evaluation set and measure how well candidate approaches perform.
-
03
Build the pipeline
We develop the models, validation rules, and integrations, with human review for low-confidence results.
-
04
Run and refine
We deploy, monitor accuracy on live data, and improve the pipeline using corrections from your team.
Agleam Labs in numbers
- Enterprise apps delivered
- 50+
- Enterprise apps delivered
- On-time delivery
- 99.8%
- On-time delivery
- Years of senior experience
- 10–30
- Years of senior experience
- Years of DevOps experience
- 20+
- Years of DevOps experience
- Technologies in production
- 20+
- Technologies in production
- Office, worldwide clients
- 1
- Office, worldwide clients
Technology
Our NLP toolkit.
A mix of large language models and specialised NLP libraries, chosen for each task.
-
Python
- Hugging Face
- spaCy
- PyTorch
- BanglaBERT
- OpenAI
- Claude
- Whisper
- Azure AI Language
- Amazon Comprehend
- Elasticsearch
- PostgreSQL
-
AWS
-
Azure
-
Docker
-
Serverless
Industries
NLP where text volumes are high.
Sectors handling many documents, customer messages, or regulated records benefit most from language AI.
FinTech
Payment, lending, and transaction platforms for always-on financial operations.
Telecom
Digital platforms, integrations, and service systems for continuous delivery.
Healthcare
Secure digital systems for data-heavy healthcare workflows and service delivery.
EdTech
Learning and assessment platforms for institutions, enterprises, and growing communities.
E-Commerce
Commerce technology connecting customers, inventory, payments, and retail operations.
Pharma
Compliant platforms for pharma operations, distribution, and regulated workflows.
FAQs
NLP development questions.
Straight answers about nlp development with Agleam Labs.
Talk to an expert
Tell us what you are working on. A senior engineer will reply with honest advice and a suggested next step.
Book a call →Do you support Bangla? + −
Yes. We process Bangla script, romanised Bangla, and mixed Bangla and English text, and we test accuracy on real Bangla samples rather than assuming a general model will cope.
Do you use large language models or traditional NLP? + −
Both. LLMs are flexible and quick to adapt; smaller trained models are faster, cheaper, and easier to host privately at high volume. We choose per task after testing.
How is NLP different from RAG or generative AI? + −
NLP here means analysing text to extract, classify, or summarise it into structured results. RAG answers questions from a document collection, and generative AI creates new content. They often work together.
Can you process scanned documents? + −
Yes. We combine OCR with NLP so scanned and photographed documents can be read, then extracted and classified like digital text.
What happens when the system is unsure? + −
Results below a confidence threshold are sent to a person for review. Their corrections are logged and used to improve the pipeline over time.
How long does an NLP project take? + −
A single extraction or classification pipeline typically takes six to ten weeks. Multilingual or multi-document programmes are delivered in phases.
Start a project
Let's make sense of your text.
- A free consultation with a senior engineer
- Clear scope, timeline, and budget
- Confidential, with an NDA on request
- Support after delivery
Prefer email?
info@agleamlabs.comLet software do the reading.
Tell us what documents or messages your team processes. We will suggest an NLP approach and a first estimate.