Natural Language Processing
Natural Language Processing Services For Text Problems At Volume
Not every text problem needs a large language model. Sorting a million documents a day, pulling fields off invoices, ranking search results, finding personal data before it leaves your estate. Those have a cost per document and a latency budget, and they are built rather than prompted.
Triple Protection Guarantee
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Triple Protection Guarantee:
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Years In Business
0
+
Engineers On Staff
0
+
Avg. Engineer Exp.
0
+
Where NLP Goes Wrong
Labelled Data
Built First
Senior Engineers
Vetted Only
Timezone Overlap
Live Hours
Cost Per Doc
Kept In View
Delaware LLC
US Entity
Eval Set Kept
Versioned Up
Drift Watched
Every Month
PII Found And Masked
Before Use
Latency Budget
Set Upfront
IP Assignment
Signed
Delivery Overlap
Fixed
Hours
Model Data Owner
You
Trusted By Startups
What Our Natural Language Processing Covers
Natural language processing is the engineering behind text a system has to act on rather than a person has to read. Sorting support tickets into queues. Reading a field off ten thousand invoices a night. Deciding which of forty thousand products a search query meant. Finding a card number buried in free text before the export runs. These jobs are judged on cost per document, latency and whether last month's fix still holds.
Stallyons is registered in Delaware as a US company, and our engineers work a time-zone window agreed before the project starts, with four-plus hours of daily overlap. You sign a US contract, run diligence on a US entity and pay one US invoice. Behind the work sits twelve-plus years of delivery across six continents and around thirty-five engineers, averaging four-plus years of production experience. Teams come to us when a promising text pilot has to become something that runs every night without supervision.
What A Text Pipeline Covers
Labelled data before modelling: annotation guidelines written down, a gold set your own people agree on, and a check that two annotators reach the same answer, because a model can only be as consistent as its labels.
The right tool chosen honestly. A small fine-tuned model is often cheaper and far faster per document than an API call, and sometimes an API call is simply the better answer. We say which, and why, before you commit.
Entities pulled out and then linked: a name, an account number or a part code matched against your own identifiers, so the output joins to your database instead of becoming another list of strings.
Search that returns the right thing: keyword and vector retrieval combined, a reranking pass on top, and a judged set of real queries so relevance changes can be proven rather than argued.
Sensitive text found before it moves: names, card numbers, health details and identifiers detected and masked, with a record of what was removed and where it went.
One accountable vendor: one contract, one invoice and one entity for legal and finance to run diligence on, instead of a spread of contractors across four jurisdictions.
What To Ask A Natural Language Processing Vendor First
- Ask what the labelled data looks like. If nobody has agreed what a correct answer is, there is nothing to train against and nothing to measure a change with later.
- Ask what one document costs to process. At a thousand documents the answer never matters. At ten million it decides whether the whole idea is worth building at all.
- Ask when they would tell you not to build a model. A vendor who has never recommended an off-the-shelf tool is selling capacity rather than judgement.
- Ask how the output joins your data. Extracting a company name is easy; matching it to the right record in your own system is the part that makes it usable downstream.
- Ask what happens when the text changes. Language drifts, products get renamed, and a model trained on last year's tickets gets worse without anyone noticing.
- Ask where sensitive text is caught. Personal data hides in free-text fields, and detection has to happen before the export, not after somebody reports it.
How A Natural Language Project Starts
Every project starts with a free 45-minute scoping session. No slide deck, no sales script. You bring the documents, the volume and the decision the text has to drive; you leave with a build plan and a timeline.
We are selective about new projects and cap how many we run at once, because the scoping is the product. If a rules engine or an off-the-shelf tool solves it, we will tell you before you buy a model.
Why Clients Choose Us
Full
Written IP Transfer
USA
Contract Entity
Yours
Models & Datasets
Named
Delivery Lead
Ready to make your text data actually useful?
What We Build With NLP Models
The Natural Language Processing We Deliver
Every business has different documents and the underlying jobs repeat: sort it, pull the facts out, find the right one, and keep the sensitive parts out of sight. These are the text builds we deliver most often.
Text Classification
Routing, tagging, triage at volume
End-to-End
Entity Extraction
Names, IDs, amounts, dates
Linked To IDs
Document Parsing
Invoices, contracts, forms, layout
Extracted
Search & Ranking Work
Retrieval, reranking, evaluation
Live In Production
Find The PII
Detect, mask, log what moved
Before Sync
Sentiment At Scale
Aspect level, per product line
Readable
Topic & Clustering
Themes across open-text feedback
Linked
Annotation & Labelling
Guidelines, gold sets, agreement checks
Sign-Off
Custom Model Training
Fine-tuned, small, cheap to run
Future-Proof
Support & Retraining
Monitoring, drift checks, updates
Kept Running
Not sure which text problem to solve first? Let us map it.
Common Challenges
Why Do NLP Systems Break In Production?
Six patterns behind almost every language model that never got past the pilot. All six start with a good demo.
No Labelled Data
01
Nobody wrote down what a correct answer looks like, so two people label the same document differently. The model learns the disagreement, and every later argument about quality is really one about the labels.
An LLM Call Per Row
02
The pilot classified four hundred documents beautifully. Production has eleven million a month, and the arithmetic on price and latency stops working first.
Entities Never Linked
03
The system extracts a company name perfectly and stops there. Nothing matches it to the right customer record, so the output is a list nobody can join to anything.
PII Left In Text
04
Structured fields are locked down carefully while personal data sits in a free-text notes column, gets copied into a warehouse, and turns up somewhere it was never meant to reach.
Accuracy Judged By Eye
05
Quality is whatever the last twenty examples looked like to whoever checked them. With no held-out set, nobody can say whether the change shipped last week helped or hurt.
Model Drifted Quietly
06
Products get renamed, customers change how they complain, and a model trained on last year's text gets slowly worse. Nothing alerts, because nothing was watching.
Recognise a few of these? Let us do it properly.
Our Language Services
6 Natural Language Processing Services
Six ways to buy language engineering from one accountable vendor. Run one, or run several in parallel under a single contract.
Custom NLP Model Development
01
A model trained on your own text and your own labels, sized so it runs inside your latency and cost budget, with a named lead who reports into you rather than an account manager.
Text Classification
02
Documents, tickets and messages sorted into the categories your business already uses, at a throughput and a price per item that hold up when the volume doubles.
Document Data Extraction
03
Fields lifted off invoices, contracts, forms and scanned pages, using layout as well as words, then validated against rules you can read and change.
Search Relevance Tuning
04
Keyword and vector retrieval combined, a reranking pass on top, and a judged set of your real queries so every relevance change can be proven before release.
NLP API & Backend Work
05
The services around the model, built by our own API development practice: batch jobs, queues, storage and webhooks into your stack.
Text Analytics & Reporting
06
Topics, themes and sentiment pulled out of open-text feedback at scale, then delivered as tables and dashboards the rest of the business can query.
Not sure which piece you need first? Let us scope it together.
Why Choose Us
What Makes Our Natural Language Processing Different
The details that decide whether a language model survives contact with your real documents.
A US Legal Entity
01
Stallyons is registered in Delaware. Your contract, your invoice and your legal recourse sit with a US company, not an unknown one.
Right Tool, Honestly
02
Sometimes a trained model wins on cost and speed, and sometimes a general model behind an API is the better answer. We tell you which.
Overlap You Set
03
You choose the hours we share with your working day, and stand-ups, reviews and escalations all happen inside that window.
Cost Per Document
04
Throughput, price per item and latency are estimated at scoping and tracked through the build, so the arithmetic is settled before volume arrives.
Reviewed Code
05
Every merge is reviewed against an agreed definition of done, on your board, where you can read it yourself.
One Contract
06
One contract covers the engagement, so procurement, legal and finance each deal with a single named counterparty.
Ready to see what a proper text pipeline looks like?
Our Process
From First Call To Live NLP Models In Six Steps
A build process that settles the task, the labelled data and the evaluation before any model.
Discovery
Understand the text, the volume and the decision
Scoping
Agree the task, cost per doc and latency
Design
Label guidelines, gold set, evaluation plan
Contracting
NDA, IP assignment, access and onboarding
Deliver
Trained, scored, reviewed on merge
Tune & Monitor
Watch drift, then retrain on newer text
Want to see how this maps to your roadmap?
Technology Stack
What Our NLP Engineers Actually Work With
The libraries, models and infrastructure we build language systems on, and what keeps them running.
Core Libraries
spaCy Pipelines
Hugging Face
PyTorch Models
scikit-learn
Python Core
Language Models & Vectors
Transformers
Sentence Vectors
Classifiers
NER Sets
Fine-Tuned Models
Search & Ranking
Vector Indexes
BM25 Keyword
Hybrid Retrieval Mix
Reranker Step
Relevance Test Sets
Data & Labelling
Gold Test Sets
Annotation
PII Masking
Postgres & Vectors
MongoDB Storage
Build & Deliver
Python Services
Node Runtime
Docker Packaging
GitHub Actions / CD
Datadog Monitoring
Who We Build This For
Natural Language Processing For Every Kind Of Text
Eight kinds of business with different documents and one shared need: text a system can act on.
Banking & Fintech
KYC documents, complaints triage
Healthcare & Records
Clinical notes, coding, de-identify
Legal & Contracts
Clause finding, obligation tracking
Retail & Reviews
Product matching, review themes
Support & Service Desks
Ticket routing, intent tagging
Media & Publishing Ops
Tagging, archive search, dedupe
Logistics & Trade
Shipping docs, customs text
HR & Recruitment Tech
CV parsing, skills matching
Working in another sector? See all industries we serve.
How We Compare
Your Natural Language Processing Options
An honest look at your four delivery options.
| Capability | Prompt For Everything | In-House Generalist | Freelance Data Scientist | Stallyons Technologies |
|---|---|---|---|---|
| Cost at a million documents | ✕ Scales with volume | Found on the invoice | Rarely modelled | Priced per document first |
| Latency per document | ✕ Network round trip | Measured late | Batch only | Budget set at scoping |
| Labelled data and evaluation | ✕ Nothing to score | Spot checks | Sometimes included | Gold set, agreement checks |
| Entity linking to your records | Strings only | Manual matching | Fuzzy match | Joined to your identifiers |
| Search relevance tuning | ✕ Not the same job | Keyword only | Vectors only | Hybrid plus a reranker |
| Sensitive text before export | ✕ Sent to the provider | Structured fields only | ✕ Out of scope | Detected and masked in place |
| Drift and retraining | Provider decides | Nobody watching | ✕ Ends at handover | Monitored and retrained |
See the difference for yourself
Complete Engagement
Everything Included In Your Natural Language Processing
From Scoping to Contracting to Delivery, One Vendor
Here is everything included when you build language systems with us:
One Language Build Price: No Hidden Fees And No Surprises.
Every language engagement includes all eight components above. One contract, one senior team, one predictable cost, and no vendor sprawl.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free Text Data Review
A written read on your documents, the labelling you already have, where sensitive text is sitting and what a model would cost to run at your volume.
Included Free
Build Plan And Estimate
A phased build plan with scope, milestones, the integrations it needs and a transparent, itemised estimate for the engagement.
Included Free
Free Vendor Checklist
The questions we would ask any language vendor about labels, cost per document, evaluation and drift, so you can put them to us too.
Included Free
Risk-Free Partnership
Our NLP Development Promise
We stand behind every engagement with commitments that protect your investment.
01
Scope Agreed First
The task, the evaluation, working hours and cost structure are written down and agreed before contracting, so nothing is discovered later.
02
Built to Last
Senior developers, code review, automated tests, security and accessibility audits, and clean, documented code you fully own.
03
IP And Access Protected
NDA and IP assignment are signed before access, permissions are scoped per person, and your accounts stay under your control.
Start your language build with confidence, backed by our Triple Protection Guarantee.
Track Record
Engagements That Ship, Scale, and Compound
500+
Projects Delivered
29+
Service Categories
81%
Repeat Client Rate
4.9 ★
Clutch Rating
"Stallyons took our Figma design and built it into a live web application, a cognitive game with level-based match play, messaging, a tutorial, and a directory that ranks users nationally. What impressed me most was their grasp of the code behind that logic, and the quality of the experience. Delivered on time with steady updates."
Jerry L.
Founder
PicCiti LLC
"We brought Stallyons in to absorb an overflow of work, and they delivered ten iOS and Android apps, from reporting to geo-location for logistics, plus several backend systems, owning design, development, and app-store submission. Everything stood out: code quality, speed, and reliability. Perfect code, on time, adopted company-wide."
William B.
Director
Amplo Solutions
FAQ
Frequently Asked NLP Development Questions
Natural language processing is the engineering that lets software read text and act on it, and it has nothing to do with the personal development method that shares the abbreviation. In a business it usually means classifying documents into categories, extracting fields and entities, ranking search results, grouping open-text feedback into themes, or finding personal data hidden in free-text fields.
When volume, latency or cost decide the design. A general model behind an API is excellent for open-ended work, small batches and fast iteration. A small task-specific model usually wins when the same narrow decision has to be made millions of times, when the answer must come back in milliseconds, or when the text cannot leave your estate. Often the right answer is both, and if you mainly need a general model wired into your product, see working with large language models.
Build cost follows how much labelled data exists, how many distinct tasks are in scope and how tightly the output must join to your systems. Running cost is separate and follows volume, so we estimate a price per document at scoping rather than after launch. We scope first, then price, and itemise each phase so you can cut before you commit.
Natural language processing is the part that deals with language: text, documents, transcripts and the messy ways people write. Machine learning is the wider practice, covering forecasting, recommendations, tabular prediction and vision as well. If your problem is numbers in a table rather than words on a page, our modelling practice is the better fit.
You do, from the first day. The trained model weights, the labelled datasets, the annotation guidelines, the evaluation set and the repositories are yours, and any provider accounts are registered in your name. NDA and IP assignment are signed before anyone gets access to a single document.
Not to begin, but you will need some before anyone can claim the system works. We usually start by writing the annotation guidelines with your subject experts, labelling a modest gold set together, and checking that two people reach the same answer. That set then doubles as the yardstick every later change is measured against.
By watching the inputs as well as the outputs. New product names, new complaint patterns and seasonal language all shift the text underneath a model that has not changed. We sample live traffic, score it against the held-out set on a schedule, alert when the gap widens, and retrain on newer text before anyone notices.
Yes, though it is a scoping question rather than a switch. Some languages have strong off-the-shelf models and plenty of training text; others need more labelling effort to reach the same place. Mixed-language documents and people switching languages mid-sentence are the harder case, and both are worth naming before the work is priced.
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready To Build Language Systems That Work?
Get a free consultation. We will look at your real documents, name what is actually solvable, and send a written proposal.
