NLP Development Services LLM-first where it fits. Traditional NLP where it must.
Sentiment analysis, classification, entity extraction, summarization, translation, semantic search, and structured information extraction. LLM-based and traditional NLP. Shipped in 8 to 16 weeks. USD pricing.
We tell you whether your NLP task fits an off-the-shelf LLM call, needs fine-tuning, or warrants a traditional model.
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Who we've built for.
How we work on NLP development
- What we build
- Sentiment · Classification · Entity extraction · Summarization · Translation · Semantic search · Topic modeling
- Stack
- Hugging Face · spaCy · OpenAI · Anthropic Claude · Llama 3 · Mistral · pgvector · Elasticsearch · LangChain
- Approach
- LLM-first where accuracy and cost permit · traditional NLP for high-volume, low-margin, or self-hosted
- Integrations
- Snowflake · BigQuery · Salesforce · HubSpot · Zendesk · Slack · Notion · Google Workspace · Microsoft 365
- Pricing in USD
- NLP pilot from $7,000 · Production NLP system from $11,000 · Custom NLP platform from $35,000
- Output
- Trained or configured model · API · eval set · drift monitoring · runbook · on-call coverage
NLP in 2026 is dominated by LLMs for most use cases. Sentiment, classification, entity extraction, summarization, translation: all of these are achievable zero-shot or few-shot with GPT-4o or Claude at acceptable accuracy. Traditional NLP (spaCy, fine-tuned transformers) wins where latency is critical, cost-per-call needs to be sub-cent, or data residency mandates self-hosted. We pick the approach to the task, not to the vendor we want to recommend.
Related builds
Production NLP and document-understanding systems:
What we build
Sentiment and emotion analysis
Customer support tickets, product reviews, social media. LLM call with structured output for low-volume. Fine-tuned RoBERTa or DistilBERT for high-volume.
Text classification
Ticket routing, content moderation, topic tagging, intent classification. Few-shot LLM or fine-tuned classifier depending on volume and accuracy needs.
Entity extraction (NER)
Named entity recognition, structured field extraction from unstructured text. spaCy fine-tuned for traditional. LLM with structured output for complex domain-specific extraction.
Summarization
Long document summarization, meeting notes, news digests. Claude (200k context) or GPT-4o (128k) for long-context. Map-reduce strategies for very long inputs.
Translation
Domain-specific translation. DeepL or Google Translate for general. LLM with glossary and brand-voice control for marketing and product content.
Semantic search and retrieval
Embedding-based search over your text corpus. OpenAI embeddings, Cohere, or open-source. Vector store (Pinecone, pgvector, Weaviate). Hybrid with BM25 for best accuracy.
Related AI capabilities: AI & machine learning, AI chatbot development, Generative AI, Machine learning, AI consultation, AI-powered software, Data pipeline engineering.
Use cases with cost ranges
Customer support ticket triage
Classification (intent, priority, product area), sentiment, entity extraction (order ID, account ID, product SKU). Integration with Zendesk, Intercom, or Salesforce Service Cloud. LLM-first with cost monitoring. Typical build 8 to 12 weeks. Range $8,000 to $14,000 depending on ticket volume and integration complexity.
Document understanding and structured extraction
Extract structured fields from contracts, invoices, claims, medical records. LLM with structured output (JSON schema). Validation layer. Human review for low-confidence. Typical build 10 to 14 weeks. Range $14,000 to $28,000 depending on document types and accuracy target.
Semantic search over knowledge base
Embedding-based search over internal docs, KB, runbooks. Hybrid with BM25. Re-ranking. Integration with Slack, Teams, or internal portal. Typical build 8 to 12 weeks. Range $8,000 to $14,000 depending on document volume and integration count.
Review and feedback analysis
Sentiment, theme extraction, action-item extraction across product reviews, NPS comments, support feedback. Dashboard for product and CX teams. Typical build 8 to 12 weeks. Range $8,000 to $14,000 depending on data volume and dashboard scope.
How we run the build
Five-phase rhythm for NLP builds. Eval set authored before model selection.
- Discovery and data audit (1 to 2 weeks). Use case definition. Sample data audit. Eval set authored. Accuracy and latency targets set.
- Model selection and prompt design (1 to 2 weeks). LLM versus traditional model decision. Prompt design or fine-tuning data preparation.
- Build and iteration (3 to 6 weeks). Two-week sprints. Eval gate every PR. Cost-per-call monitored.
- UAT and integration testing (1 week). Real-data testing. Integration end-to-end. Performance under load.
- Launch and dual on-call (1 week plus 2 weeks). Production deploy. Accuracy and cost monitoring. Runbook delivered.
Tech stack
- LLM layer: OpenAI GPT-4o for most use cases. Claude Sonnet for long-context. Claude Haiku or GPT-4o-mini for high-volume cost-sensitive. Open-source via vLLM for self-hosted.
- Traditional NLP: spaCy for NER and dependency parsing. Hugging Face transformers (BERT, RoBERTa, DistilBERT) for fine-tuned classification. NLTK for legacy preprocessing.
- Embeddings: OpenAI text-embedding-3-large. Cohere embed-v3 for multilingual. Open-source (BGE, GTE) for self-hosted.
- Vector store: pgvector for PostgreSQL-resident. Pinecone for managed scale. Weaviate or Qdrant for self-hosted scale. Elasticsearch for hybrid (BM25 plus vector).
- Orchestration: LangChain or LlamaIndex for multi-step. LangSmith or PromptLayer for observability and prompt versioning.
- Evaluation: Eval set with pass-fail criteria. LLM-as-judge for subjective tasks. Human review on production sample for ongoing quality monitoring.
- Cloud: AWS or Azure with regional data residency. SageMaker or Vertex AI for fine-tuning workloads.
Pricing
NLP pilot
From $7,000
- Use case validation with LLM prototype.
- 3 to 5 weeks. Validates feasibility before productionisation.
Production NLP system
From $14,000
- Single use case (sentiment, classification, entity extraction, summarization) deployed with monitoring.
- 8 to 12 weeks.
Semantic search system
From $11,000
- Embedding pipeline, vector store, search API, basic UI.
- 8 to 12 weeks.
Document understanding pipeline
From $21,000
- Structured extraction from one to three document types with validation.
- 10 to 14 weeks.
Custom NLP platform
From $35,000
- Multi-task NLP platform with shared infrastructure.
- 12 to 18 weeks.
FAQ
LLM-first for most use cases in 2026. Traditional NLP (fine-tuned BERT, spaCy) wins when you need sub-50 ms inference, sub-cent per-call cost, or fully self-hosted with no API dependency. We assess cost-quality-latency at scoping and pick accordingly.