Get production-ready NLP pipelines — from text classification and sentiment analysis to LLM fine-tuning and RAG systems — built by a London-based AI and machine learning engineer with 5+ years of specialist experience.
I Will Build Custom NLP & AI Solutions in Python
A focused NLP task — text classification, sentiment analysis, or NER — on datasets up to 1,000 words.
- Text classification OR sentiment analysis OR NER
- Up to 1,000 words of input data
- Research into your specific use case
- Data preprocessing included
- Python code delivered
- 1 revision round
A complete, deployed NLP model with API integration, documentation, and unlimited revisions.
- Everything in Starter, with expanded scope
- Custom model creation and training
- Cloud deployment of the trained model
- API integration for immediate use
- Full model documentation
- Unlimited revisions
End-to-end NLP solution with fine-tuning, validation, performance monitoring, and full source code.
- Everything in Boost, with maximum scope
- LLM fine-tuning for your specific domain
- Model validation and rigorous testing
- Performance monitoring setup
- Full source code provided
- Unlimited revisions
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Key details about this service to help you decide. Generated by Zinn Hub, not the seller.
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NLP Scope
AI Frameworks
Upgrade Path
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What You'll Receive
Full Description
Turn raw text data into actionable intelligence with a bespoke NLP solution engineered to your exact requirements. Whether you need to classify customer feedback at scale, extract named entities from documents, build a conversational AI assistant, or deploy a Retrieval-Augmented Generation (RAG) pipeline, this service delivers clean, well-documented Python code ready to plug into your workflow.
Zinn Digital is a London-based AI and machine learning practice with over five years of NLP expertise and three years of hands-on experience with large language models and RAG applications. Every solution is built using state-of-the-art frameworks and tailored to the problem at hand — not a generic template.
**What this service covers:**
- Text Classification & Sentiment Analysis
- Named Entity Recognition (NER)
- Topic Modelling & Summarisation
- Machine Translation
- Chatbot & Conversational AI Development
- Text Generation using LLMs (GPT-4, Llama, Mistral)
- Question Answering Systems
- Information Extraction & Knowledge Graphs
- Retrieval-Augmented Generation (RAG) with LangChain & LlamaIndex
- LLM Fine-tuning for Specific Domains
**Frameworks and tools used include:** Transformers (BERT, RoBERTa, T5), Hugging Face libraries, spaCy, NLTK, TensorFlow, PyTorch, LangChain, LlamaIndex, Vector Databases (Pinecone, Weaviate, FAISS), and OpenAI, Anthropic, and Cohere APIs.
**How it works:**
Once your order is placed, you will be asked to share your dataset or text samples, a description of your goal, and any relevant context about your use case. Research and data preprocessing are carried out first to ensure a clean foundation, then the appropriate model or pipeline is designed and built to specification. For mid and full-scope orders, the solution is packaged with documentation, cloud deployment, and API integration so it is immediately usable in production.
**Who this is for:**
This service suits product teams, data teams, researchers, and businesses that need a reliable NLP capability without building an in-house ML team. It is equally suited to startups prototyping an AI feature and enterprises adding intelligence to existing workflows.
**Why Zinn Digital:**
Five-plus years of focused NLP and AI experience means the right architecture is chosen first time. Solutions span classical techniques and cutting-edge deep learning, ensuring the approach is always proportionate to the problem — performant, maintainable, and ready to scale.
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Compare Packages
| Feature | Starter | Boost | Premium |
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
| Revisions | 1 | unlimited | unlimited |
| Text classification OR sentiment analysis OR NER | ✓ | ✕ | ✕ |
| Up to 1,000 words of input data | ✓ | ✕ | ✕ |
| Research into your specific use case | ✓ | ✕ | ✕ |
| Data preprocessing included | ✓ | ✕ | ✕ |
| Python code delivered | ✓ | ✕ | ✕ |
| 1 revision round | ✓ | ✕ | ✕ |
| Everything in Starter, with expanded scope | ✕ | ✓ | ✕ |
| Custom model creation and training | ✕ | ✓ | ✕ |
| Cloud deployment of the trained model | ✕ | ✓ | ✕ |
| API integration for immediate use | ✕ | ✓ | ✕ |
| Full model documentation | ✕ | ✓ | ✕ |
| Unlimited revisions | ✕ | ✓ | ✓ |
| Everything in Boost, with maximum scope | ✕ | ✕ | ✓ |
| LLM fine-tuning for your specific domain | ✕ | ✕ | ✓ |
| Model validation and rigorous testing | ✕ | ✕ | ✓ |
| Performance monitoring setup | ✕ | ✕ | ✓ |
| Full source code provided | ✕ | ✕ | ✓ |
Portfolio
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Build Custom NLP & AI Solutions in Python
Build Custom NLP & AI Solutions in Python
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Frequently Asked Questions
Ideally, share your text dataset or a representative sample, a clear description of what you want to achieve (e.g. classify reviews as positive or negative, extract organisation names from contracts), and any technical constraints such as a preferred cloud provider or existing API infrastructure. The more context you provide, the faster work can begin.
This refers to the volume of input text processed in the task — for example, a batch of customer reviews or a set of short documents totalling no more than 1,000 words. If your dataset is larger, the Boost or Premium package is the right fit.
The trained model is deployed to a cloud environment so it can be called via an API endpoint — meaning your application or team can send text in and receive predictions or outputs back without needing to run code locally. The exact platform can be discussed based on your existing infrastructure.
Fine-tuning adapts a pre-trained large language model — such as a Llama or Mistral variant — on your own domain-specific data, improving its accuracy and relevance for your particular use case. It is included in the Premium package and is most valuable when you need consistent, specialised performance rather than general-purpose output.
RAG connects an LLM to a knowledge base of your documents, allowing it to retrieve relevant context before generating a response. This is ideal for intelligent Q&A systems, internal knowledge assistants, and any application where the model needs to answer questions grounded in your specific content rather than general training data.
A revision covers adjustments to the delivered solution based on your feedback — for example, tweaking model parameters, adjusting preprocessing logic, or refining output formatting. The Starter package includes one revision round; Boost and Premium include unlimited revisions within the agreed scope.
Yes. If you have an existing Python environment, database, or API layer, that context can be factored into the build. Please share relevant technical details when submitting your requirements so the solution integrates cleanly with what you already have.
Source code is included in the Premium package. For Starter and Boost, the deliverable is functional code you can use; if you specifically need editable source files for those tiers, the source code add-on is available.
Customer Reviews
See what our customers say about this Zinn
Aashir found a great solution to the problem we described to him – this is phase 1 in a larger project and we are very impressed with the results! It's been a pleasure working with him (implementing an AI LLM pipeline).
Delivered exactly what I needed and was very patient with all the edits and requests. I wish him all the best.
Very good Freelancer to work with ! i Will definetily reccomend him to anyone lookint to create his first chatbot ! Love you Aashir !
One of the most trustworthy freelancers I've worked with. Unlike others who rush or cut corners, he truly cares about delivering quality. He puts in a lot of effort, listens closely to the client's needs, and always does his best to ensure the project meets expectations. Reliable, dedicated, and committed to doing things right. Highly recommended. Thank you, brother.
Phase 1 of my project is complete – Aashir has understood the assignment well and completed things in a timely manner! Looking forward to Phase 2!
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