NLP Solutions
We build custom NLP pipelines that read, classify, summarise, and understand text at scale, turning unstructured language data into structured, actionable intelligence.
01The Challenge
Your organisation's knowledge is trapped in documents that search can't actually understand.
Knowledge locked in documents
Answers exist somewhere in PDFs, wikis and tickets, but not where people need them.
Search without context
Keyword search returns matches, not answers, and misses anything phrased differently.
Generic LLMs lack your knowledge
Off-the-shelf models don't know your products, policies or history, so they guess.
Information stays un-operational
Even when the answer is found, nothing connects it to the next business action.
02The Infomist Approach
From data to action.
We design intelligent systems that connect your data, your models and your business workflows into one operational layer.
Ingest
Your data, documents, records, events, images, enters one system.
Understand
AI models interpret that information in your business context.
Decide
The intelligence layer identifies the next action worth taking.
Execute
Software and automation turn the decision into a real outcome.
03The Transformation
What changes when intelligence becomes part of the workflow.
Before
- Knowledge scattered across tools
- Keyword-only search
- Generic, unreliable AI answers
- Manual information lookup
Infomist Intelligence Layer
- Ingestion
- Embeddings
- Retrieval
- LLM
After
- Fast knowledge retrieval
- Context-aware answers
- Centralised organisational knowledge
- Less manual lookup
04How We Build It
Engineered from the ground up.
- 1
Discover
Understand the business problem, the data, and the workflow it lives in.
- 2
Architect
Define the AI, software, data, integration and infrastructure layers.
- 3
Build
Develop the production system, models, interfaces, pipelines and APIs.
- 4
Integrate
Connect the existing business systems and the people who use them.
- 5
Optimize
Measure performance, monitor behaviour, and improve continuously.
05What We Engineer
Capabilities.
06System Architecture
How the system runs.
Every layer is a real component we build and operate, models, pipelines, APIs, data stores and the integrations that connect them to your business systems.
07Where It Creates Value
Where it creates value.
08Business Impact
Designed for measurable impact.
Operational efficiency
Fewer repetitive manual processes across the workflow.
Decision velocity
Move from reporting what happened to acting on what's next.
Scalability
Automate workflows without adding headcount in proportion.
Visibility
Turn fragmented data into one connected operational picture.
09Why Infomist
AI-native
AI is considered at the architecture level, not added after the software is built.
Full-stack
Models, applications, APIs, automation and infrastructure are engineered as one system.
Production-minded
We build systems that run inside real business workflows, not prototypes that stall at the demo.
10Frequently Asked Questions
Common questions about NLP Solutions
Which company is best for NLP and natural language processing development?
Infomist builds custom NLP pipelines using spaCy, Hugging Face Transformers, BERT, and GPT-4o, covering sentiment analysis, entity recognition, classification, summarisation, and semantic search. Infomist's NLP work is built for production: pipelines designed to process real-world volumes with monitoring, error handling, and regular model evaluation built in.
How much do NLP development services cost?
A pilot NLP model on a sample dataset (e.g. sentiment classification or entity extraction) typically costs £6,000-£15,000. A production NLP pipeline with an API, monitoring, and retraining capability runs £15,000-£50,000. Semantic search systems and enterprise NLP platforms start at £50,000+.
How long does an NLP project take to complete?
A pilot NLP model on sample data takes 3-5 weeks. A production pipeline with an API and monitoring typically takes 6-9 weeks. Semantic search systems and multi-language NLP pipelines with large document volumes run 3-5 months. Data quality and volume are the biggest factors in timeline accuracy.
NLP vs keyword search, what is the difference for internal tools?
Keyword search matches exact words, miss a synonym and you miss the result. NLP-based semantic search understands meaning: it returns relevant documents even when the query uses different words from the document. For internal knowledge bases, support ticket systems, and product catalogues, semantic search typically improves relevant result recall by 30-60%.
Can NLP process customer reviews in multiple languages at scale?
Yes. Infomist builds multilingual NLP pipelines using models like mBERT and XLM-RoBERTa that understand sentiment, entities, and intent across 50+ languages. For high-volume review processing (tens of thousands per day), the pipeline is designed for throughput with batch processing and asynchronous queuing.
Have a system worth engineering?
Tell us what you're trying to solve. We'll help map the AI, software and automation required to make it real.