What We Build With It
We engineer intelligent NLP solutions that automate manual processes and extract critical intelligence from your text data.
Intelligent Document Processing (IDP)
Systems to extract specific entities (names, dates, values) from millions of unstructured documents, automating data entry and compliance.
Semantic Search & Knowledge Retrieval
Beyond keyword matching: building internal or customer-facing search engines that understand intent and provide precise answers, often with RAG.
Sentiment Analysis & Customer Insights
Analyzing customer feedback from reviews, calls, or social media to identify trends, pain points, and product opportunities.
Automated PII Redaction & Compliance
Systems to automatically find and redact personally identifiable information from documents and communications to ensure data privacy and regulatory compliance.
Text Classification & Routing
Automatically categorizing support tickets, emails, or messages and routing them to the appropriate department or workflow.
Custom Knowledge Graph Construction
Extracting relationships and entities from large text corpora to build structured knowledge bases that power advanced search and reasoning.
Why Our Approach Works
We focus on building practical NLP systems that deliver measurable business outcomes, avoiding the pitfalls of theoretical models.
Pragmatic Model Selection
The biggest model isn't always the best. We choose the right model for the job—balancing performance, cost, and speed—often fine-tuning smaller models for superior domain-specific results.
Data-Centric NLP
Off-the-shelf models fail with domain-specific language. We prioritize creating high-quality, labeled datasets for fine-tuning, the most critical factor for NLP project success.
End-to-End Lifecycle Management
An NLP model is a living system. We build robust MLOps pipelines for continuous evaluation and retraining, ensuring your models adapt as your data and business needs evolve.
Our Go-To Stack for NLP Engineering
We build modern NLP pipelines using transformer-based models and a robust set of tools for processing and understanding text at scale.
Transformer Models
BERT-derivatives (RoBERTa, DistilBERT), T5, and modern LLMs (Llama, Mistral, Claude) for deep language understanding.
Core Libraries
Hugging Face Transformers, spaCy, NLTK for efficient text processing and model interaction.
Vector Search & Databases
Pinecone, Weaviate, FAISS for building semantic search and Retrieval-Augmented Generation (RAG) systems.
Data Annotation
Label Studio, Doccano for creating and managing custom, high-quality training data.
Deployment & Serving
FastAPI, TorchServe for deploying models as scalable, low-latency APIs.
Evaluation & Benchmarking
Custom evaluation suites and frameworks like LangSmith or Arize Phoenix for continuous performance tracking.
Frequently Asked Questions
Do we need a massive dataset to get started with NLP?
+Not always. Thanks to transfer learning and modern foundation models, high accuracy can often be achieved with a surprisingly small amount of labeled, domain-specific data. We help design efficient data labeling strategies.
How do you handle industry-specific jargon or terminology?
+This is where fine-tuning excels. We take a powerful pre-trained model and continue its training on a dataset of your own documents, teaching it the specific nuances of your domain for superior accuracy.
Can we run these models on our own infrastructure for data privacy?
+Yes. Many high-performing models are open-source and can be fine-tuned and hosted entirely within your private cloud environment, ensuring your sensitive data never leaves your control.
Do you support multi-lingual NLP systems?
+Yes. We build systems that can understand and process dozens of languages using cross-lingual embeddings and multi-lingual transformer models, ensuring consistent performance across global markets.
How accurate is automated entity extraction?
+For standard entities (names, dates), it’s very high (>95%). For complex, domain-specific entities (like technical parts or legal clauses), we use targeted fine-tuning and active learning to reach production-grade accuracy.
Can you analyze sentiment and emotion in customer communications?
+We go beyond ‘positive/negative’ to identify specific emotions, intent, and urgency, allowing your teams to prioritize high-risk customer issues and uncover deep qualitative insights from feedback at scale.