Data pipelines and analytics
LLM-powered data cleaning, enrichment, and insight generation from unstructured or messy data sources.
A lot of valuable data is messy - free text, inconsistent formats, scattered across spreadsheets and emails. LLMs can clean, normalise, enrich, and extract insights from unstructured data in ways that traditional ETL struggles with.
I build pipelines that ingest your messy data, apply AI for cleaning and enrichment, and output structured data for analytics or downstream systems. Use cases include: normalising product or customer data, extracting entities from notes or feedback, generating summaries for reporting, or enriching records with external context. For businesses with legacy data or manual data entry, this often unlocks analytics that weren't feasible before.
Example AI integrations
AI services and tools I've integrated for businesses include:
Unstructured.io
AI-powered parsing of PDFs and docs for LLM ingestion. For data pipelines, it ingests messy documents and outputs structured data for analytics.
Pandas AI
Natural language to dataframe queries via LLM. For data pipelines, it lets analysts query and clean data using natural language.
LangChain
Document loaders and chains for data extraction and enrichment. For data pipelines, it chains loaders and LLMs for extraction and enrichment.
LangSmith
LLM observability, tracing, and evaluation for AI pipelines. For data pipelines, it traces and debugs LLM runs and evaluates outputs.
Haystack
NLP framework for LLM pipelines and document processing. For data pipelines, it builds document processing and extraction pipelines.
Ragas
AI evaluation and benchmarking for RAG pipelines. For data pipelines, it evaluates and benchmarks RAG and extraction quality.
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Types of businesses I work with
- Logistics and supply chain - Route optimisation, demand forecasting, inventory planning, and shipment tracking with AI.
- Manufacturing and engineering - Process documentation, quality checks, supplier comms, and internal knowledge bases. Often starting with one high-friction workflow.
- E-commerce and retail - Product search, customer support, inventory and order triage, and personalised recommendations.
Frequently asked questions
- AI handles free-text fields, inconsistent formats, duplicate records, misspellings, and data scattered across spreadsheets and emails. It can normalise addresses, standardise product names, extract entities from notes, and merge records that traditional tools would miss.
- Not necessarily. I can build pipelines that work with your existing tools - spreadsheets, databases, or cloud storage. For larger volumes, a simple data warehouse setup can be added, but many businesses start with what they have and scale as needed.
- AI reads your existing records and adds missing context - for example, categorising customer feedback by topic, extracting key dates from contracts, or adding industry codes to company records. It uses language understanding rather than rigid rules, so it handles variation and ambiguity.
- Yes, with the right setup. I build pipelines with data governance in mind - audit trails, access controls, and the option to run models on-premises or in your own cloud account so data never leaves your infrastructure.
What kind of messy data can AI clean up?
Do I need a data warehouse or special infrastructure?
How does AI data enrichment work?
Is AI data processing suitable for sensitive or regulated data?
Want to discuss AI for your business?
I help businesses integrate AI into their workflows. Get in touch to talk through your specific situation.