A large share of what an organisation knows is stored as text and speech: support tickets, customer comments on social media, contracts, sales-visit reports, email threads and voice notes. Natural language processing unlocks that reserve — teaching computers to read, understand and summarise human language in both Persian and English. At PBDP we build NLP projects against the realities of written Persian: attached script, inconsistent spelling and the habit of dropping Latin words mid-sentence. The result is a system that turns an overwhelming volume of text into tidy categories, accurate summaries and answerable questions in seconds — work that manual reading would take months to complete, every single time.

Why text is the most neglected data asset

Enterprise systems produce numbers, and management has learned to manage numbers; unstructured text, meanwhile, sits there collecting dust. Industry studies put roughly four-fifths of organisational data in the semi-structured or free-form category — precisely what most of your competitors have never mined. Text analysis hears the hundred customers who never fill in a survey form, scans contracts in seconds and pulls out the clauses that matter, and sorts and prioritises thousands of support tickets before a human eye touches them. Every one of these has run as a real project of ours, and they all begin with the same deliberate decision: treating text as first-class data rather than an afterthought of the systems that generated it.

What can be done with Persian and English text and speech?

The most popular application is automatic categorisation and sentiment analysis: deciding whether each customer message is positive, negative, or simply about what — invaluable for brand monitoring and crisis triage. The second is automatic summarisation of reports and meeting transcripts. The third is entity extraction — pulling names, dates, amounts and national IDs out of unstructured text and into a database. The fourth is a semantic search engine over internal knowledge. And the fifth is conversational assistants that answer in natural language, typically delivered as a chatbot and virtual assistant on your website and messaging platforms.

What you receive

• Persian and English text models: trained on data from your own domain rather than borrowed generic demos, with room for bespoke model development where the problem demands it.
• Document processing pipeline: automated reading, splitting, cleansing and extraction of key fields into structured records.
• Language monitoring dashboard: volume, category and sentiment trends over time, connectable to your existing analytics reporting.
• API service: embedding the language capability inside internal systems, your site or mobile applications.
• Labelled dataset as your asset: kept permanently, with a labelling guide so your team can extend it without us.
• Accuracy and behaviour report: including error boundaries and the situations where the model must escalate to a human.

How the work progresses

1. Problem framing and sampling: choosing a real sample corpus and agreeing on a measurable objective.
2. Text preparation and normalisation: unifying spelling, tokenising Persian words and treating punctuation noise.
3. Labelling and training: building the training set together with your domain experts, then training the models.
4. Validation and tuning: accuracy testing on unseen data and correcting behaviour at the margins.
5. Deployment and integration: releasing the service and wiring it into live processes within your wider AI automation programme.

Persian: a language with no ready-made version

Most off-the-shelf language tooling is tuned for English and performs poorly on real Persian — colloquial, glued together, sprinkled with Latin words, unpunctuated. Our projects start by taking the target language seriously: building Persian datasets, adapting multilingual models to common internet register, and testing continuously against genuine samples. That is the line between a dependable system and a demo that only flatters itself. And if your text content also matters to search engines, outputs can be shaped with a content SEO lens so extracted knowledge eventually converts into traffic and sales.

Frequently asked questions

Do large language models replace a bespoke NLP project?

For part of the workload, yes — and we use large language models as project engines where they fit. But raw model answers without a connection to your internal knowledge are not enough. The winning combination is a frontier model plus semantic search over your own document base, the pattern we implement inside intelligent enterprise portals so answers stay sourced, internal and auditable.

Do you also work with Persian speech?

We do. Persian speech-to-text with downstream content analysis is one of our regular workstreams. For call centres, internal meetings or field sales visits we build the full route: recording and streaming, transcription, structuring, classification and search — while respecting the legal constraints of storing customer voice data.

Our text data is confidential; what safeguards exist?

Training and inference can run entirely on your own infrastructure or in a private cloud tenancy, and a non-disclosure agreement is the first signature on every project. In sensitive scenarios — contract scanning or medical records, for example — data never leaves your network, and access is restricted by organisational roles down to document level.

To discover what is hiding inside your organisation's documents and messages, send us a few sample files and we will return a no-cost initial assessment of what NLP can extract for you.