Turn messy documents and messages into useful business data.
Language systems built for your industry terms, abbreviations, formats, and real-world text, not perfect examples from a public test.
We build systems that read, sort, search, and extract information from the language your business already produces. From clinical documents and contracts to support tickets and multilingual search, every solution learns the words your teams use and fits directly into the next business step.
Field-level extraction accuracy on industry documents
Documents and messages processed every month
Average classification time at production volume
Clients we've worked with
Real business language is rarely clean or consistent.
Your documents contain spelling mistakes, abbreviations, mixed formats, scanned pages, and terms that may not exist outside your industry. A model can perform well on a public test and still struggle with your contracts, tickets, or clinical notes. We measure success using text that looks like yours.
The biggest model is not always the best choice. A smaller model trained on strong examples from your business can be faster, cheaper, and more accurate for a focused task.
Explore our NLP capabilitiesPotential cost difference between a specialised small model and a large general model
Improvement in processing time per document
From routing one message to understanding an entire document collection.
We build the smallest system that can solve the problem reliably. That may be a focused classifier, an extraction process, meaning-based search, or a multilingual solution.
Lower processing cost than comparable general-purpose models
AurvikAI client data
Real language, processed at real business scale
Reliable systems working with the documents and messages your organisation receives every day.
Move information automatically with confidence
Extract important fields from scanned and digital documents. High-confidence results move directly into the next system, while uncertain fields go to a person with the supporting text already highlighted.
field-level accuracy on industry documents
Built around your text, vocabulary, and quality standards
Every stage is prepared for real-world language and measured using your own information.
Handle language as it actually arrives.
Correct common encoding issues, scan errors, abbreviations, and inconsistent formatting before processing.
Preserve your product names, codes, and industry language accurately.
Identify language within individual sections when documents combine several languages.
Keep tables, headings, and reading order so meaning is not lost.
Language systems often fail on vocabulary, not grammar.
We report accuracy separately for each document type, language, and source instead of hiding weak areas inside one average. Uncertain cases go to a person, every extracted field links to its supporting text, and monitoring alerts the team when new terminology appears.
Overall
- One average, which on its own hides where the system fails
By document type
- Digital, scanned and handwritten pages each scored on their own
By language
- Every language measured separately, never blended into one figure
By source
- Each channel and supplier reported apart, so a weak one shows
A model can look 95% accurate overall while performing poorly on scanned documents, a second language, or a new product line. We make those gaps visible before customers find them.
Of extracted information traceable to the source passage
Reported by document type, language, and source
Selected work
AI systems running in production, described rather than named.
A UK community pharmacy groupAnswering pharmacy calls without adding headcount.An AI phone receptionist covering twenty stores around the clock, each with its own knowledge base and its data walled off from the rest.20 storesanswered around the clock by one AI receptionistRead the case
A Los Angeles private car serviceTurning every call into a booking.Phone and web bookings answered by AI, written straight into the dispatch system, then rebuilt as a product other operators can buy.24/7booking cover on phone and chat, with nobody on the lineRead the case
An industrial hygiene consultancyTurning lab data into review-ready reportsLab PDFs read automatically, exposure limits checked against the right jurisdiction, and report prose generated with the regulation cited.Under 1 hrto produce a report that used to take six to tenRead the caseWhat we build with
Models
- Anthropic Claude
- GPT (OpenAI)
- OpenAI Whisper
Retrieval
- OpenAI Embeddings
- pgvector
- Pinecone / Chroma
Services
- FastAPI (Python)
- PostgreSQL
- Redis
Questions we get asked
What can you do with our text that search cannot?
Group documents by what they are about, pull structured fields out of unstructured writing, route incoming messages by intent, and answer questions across a corpus. Search finds documents containing a word. These techniques work on meaning, which is what you want when the right document never uses your phrasing.
Does it work on our industry's vocabulary?
Usually better than expected, and we test rather than assume. General models handle most professional language well; genuinely closed vocabularies, unusual abbreviations or multilingual mixtures need checking on your own material first. That evaluation takes days and prevents a much more expensive discovery later.
Can it handle messy documents?
Scanned pages, inconsistent formats and poor structure are the normal case rather than the exception. Extraction quality tracks input quality, so we measure it early and set expectations from your worst documents instead of your best. Where a source is genuinely unreadable, saying so beats a confident wrong extraction.
How do you handle multiple languages?
Current models work across major languages without separate systems, though quality varies and needs measuring per language. What usually needs deciding is whether output should match the input language or be normalised to one, because that choice affects everything downstream.
What happens to text we cannot send externally?
That constraint decides the architecture, so it belongs in the first conversation. Options run from vendor terms that exclude your data from training, through regional processing, to models running entirely inside your own infrastructure. Each has a real cost in capability, and we would rather price that honestly upfront.
How do you measure whether it is good enough?
Against the task rather than the language. For extraction, the proportion of fields correct on documents you have checked by hand. For routing, how often the message reaches the right team. Fluency is easy to judge by eye and tells you almost nothing about whether the work got done.
Partners in delivery
Ready to make your documents and messages useful?
Start with the text piling up inside your business. We will tell you what can be extracted, sorted, or searched reliably, what your current document quality allows, and whether you need a large model at all.