Language models that understand your customers, automate your documents, and scale your knowledge.
Natural language processing and large language models are rewriting what is possible with text. We help businesses harness NLP and LLMs responsibly — from intelligent document processing and automated support to retrieval-augmented generation and fine-tuned enterprise assistants — with the safety and governance guardrails that enterprise use requires.
Discuss Your ProjectExtract, classify, and summarise information from thousands of documents automatically.
LLM-powered workflows that handle complex language tasks humans used to do manually.
Private deployments, data residency controls, and prompt injection safeguards.
Define the language task, quality requirements, and governance constraints.
Rapid prototype with off-the-shelf models to validate feasibility.
Fine-tuning, prompt engineering, or RAG implementation for production quality.
Secure deployment with evaluation monitoring and human-in-the-loop reviews.
Analytics & Insights
Statistical rigour and ML-powered analysis that drives real decisions.
Architectural BIM, scan-to-BIM, 3D visualisation, and automation — all under one roof.
Common questions about our NLP & LLMs service.
GPT-4 and Claude via API cover 80% of enterprise use cases with excellent out-of-the-box performance. Fine-tuning is worth the investment for highly domain-specific tasks, latency-sensitive applications, cost reduction at scale, or when data privacy requirements prevent using external APIs.
RAG (Retrieval-Augmented Generation) grounds responses in your verified knowledge base and requires the model to cite sources. We add citation requirements, confidence scoring, output validation layers, and human review workflows for high-stakes decisions.
RAG retrieves relevant documents from a knowledge base and includes them in the model prompt at inference time — no retraining required. Fine-tuning adjusts model weights on your domain data. RAG is better for knowledge that changes frequently; fine-tuning is better for style, format, and specialised reasoning patterns.
We offer three approaches: using private API deployments with data processing agreements, deploying open-source models (Llama, Mistral) on your own infrastructure, or using Azure OpenAI Service with data residency and no training-data retention. The right choice depends on your data sensitivity and regulatory context.
Yes — this is a common RAG application. We ingest your documents into a vector database (Pinecone, Weaviate), build a retrieval pipeline, and connect it to an LLM that generates answers grounded in your specific content with source citations.
A RAG prototype over a defined document corpus can be running in 2–3 weeks. Production-grade deployment with evaluation frameworks, safety guardrails, and monitoring takes 6–10 weeks. Fine-tuned models with custom training data take longer depending on dataset size.
We use LLM evaluation frameworks (LangSmith, Ragas, HELM) that assess factual accuracy, relevance, groundedness, and safety. For production systems we also run red-team adversarial testing to identify prompt injection vulnerabilities and failure modes before launch.
Content moderation layers to block harmful outputs, topic constraints that keep the model on-scope, rate limiting to prevent abuse, and human escalation paths for queries the model cannot handle confidently. We design safety as a system property, not a single guardrail.
Our team will scope your requirements and come back with a clear proposal within 48 hours.