Small model when small wins
A fine-tuned classifier that costs a fraction of a penny per document often beats an LLM on both accuracy and cost. We benchmark both rather than defaulting to the fashionable one.
Turning the text your business already generates into data
Contracts, tickets, emails, forms, reviews and transcripts — classified, extracted, routed and searchable. Sometimes that calls for a large language model and sometimes a small classifier does it faster, cheaper and more predictably. We pick on evidence.
Or see how we engage first — four shapes, no sales call required.
- Document extraction
- Classification & routing
- Named entity recognition
- Semantic search
- Sentiment & intent
- Topic modelling
- Summarisation
- OCR pipelines
- Multilingual
- Speech to text
- Redaction & PII
- Taxonomy design
- Active learning
- Annotation tooling
- spaCy
- Transformers
We remove the manual reading nobody should still be doing
No more rekeying invoices and forms into another system
Forget triage done by whoever opens the inbox first
Stop searching by keyword when people ask in sentences
Skip the backlog of documents nobody has time to read
Done with reports summarising what a spreadsheet already knows
Say bye to rules that break on every new supplier format
2–5 min
typical manual handling time per document, before automationindustry context
95%+
extraction accuracy achievable on structured forms with review in the looptypical, document dependent
1 in 5
documents that still need a human — design for it rather than around itour rule of thumb
Why build natural language processing with us?
Built for the exceptions
The interesting question is never the 80% that parses cleanly. We design the review queue, the confidence threshold and the correction loop from the start.
Your taxonomy, properly defined
Most classification projects fail on ambiguous labels, not on modelling. We write the guidelines with your people and test whether two of them agree before training anything.
PII handled deliberately
Detection, redaction and retention are designed in — which matters because text is where personal data hides in the places nobody audits.
We buildNatural language processing
How we engage
Four ways this usually starts
Most enquiries land in one of these. If yours does not, say so on the call — we would rather shape the engagement than sell you the nearest shape we have.
You have a document pile and a deadline
A one-off or recurring extraction over a body of documents, delivered as clean structured data you can load anywhere.
- OCR and layout handling for scans
- Schema designed with you
- Extraction with confidence scores
- Review pass on low-confidence records
Best for: Migrations, audits, due diligence, backlog clearance.
Most teams start here
You need it to run every day
A pipeline wired into the inbox, the drive or the API where documents actually arrive, writing into the system that needs them.
- Ingestion from email, SFTP, storage or API
- Classification and routing rules
- Validation against your business rules
- Write-back into ERP, CRM or finance system
Best for: Invoices, orders, claims, applications, onboarding packs.
Your people cannot find anything
Semantic search across the content you already hold, respecting the permissions you already have.
- Indexing across drives, wikis and ticketing
- Hybrid keyword plus semantic retrieval
- Permission-aware results per user
- Relevance tuned on real queries
Best for: Knowledge bases, policy libraries, technical documentation.
You need language understanding in a product
Classification, extraction or search shipped as a feature of your own software, with the latency and cost profile a product demands.
- Inference API with a latency budget
- Multilingual support where needed
- Per-tenant models or configuration
- Continuous improvement from live corrections
Best for: Software companies whose users work in text.
Case studies
Ideas that became systems in production
Every card is a build we shipped and still support. Open one for the decisions behind it.
Ops Copilot
- AI agents
- LLM
- Python
- +3
- +2
Tender Response Drafter
- AI
- LLM
- RAG
- +3
- +2
Policy & Contract Assistant
- AI
- RAG
- LLM
- +3
- +2
Pallet Counting
- AI
- Computer vision
- Python
- +4
- +3
Construction Site PPE & Safety Monitoring
- AI
- Computer vision
- Python
- +4
- +3
Crowd Density Monitoring
- AI
- Computer vision
- Python
- +4
- +3
Digital Security Planner
- React Native
- Node.js
- AWS Amplify
- +2
- +1
Virtual Estimator
- .NET
- Blazor WebAssembly
- Bootstrap
- +2
- +1
Joblogy
- Web app
- iOS
- Android
- +1
Pamacrafts
- WordPress
- WooCommerce
- PHP
- +4
- +3
RollBnB
- WordPress
- Listeo
- PHP
- +4
- +3
Venus Aesthetics
- React
- Tailwind CSS
- shadcn/ui
- +1
ShiftTracker
- Web platform
- iOS app
- Android app
- +4
- +3
Freight Dispatch
- Custom software
- Integrations
- Cloud
- +1
Invoice Reconciliation AI
- AI agents
- Document AI
- Finance
- +1
Chilled Traceability
- ERP
- Data platform
- Compliance
- +1
Steel Quoting Engine
- AI
- Pricing
- ERP
- +1
Factory Floor App
- Mobile
- Offline-first
- IoT
- +1
Fleet Driver App
- Mobile
- Telematics
- Platform
- +1
We serve the best in the industry
Scope
All of it, written down
What falls inside this service. Nothing here is an upsell discovered later.
What we do with text
- Structured extraction from documents
- Classification and routing
- Named entity recognition
- Semantic and hybrid search
- Summarisation of long documents
- Sentiment, intent and theme analysis
- Deduplication and record matching
- Translation and multilingual handling
Getting text in
- OCR for scans and photographs
- Layout and table parsing
- Email and attachment ingestion
- Speech-to-text for calls and meetings
- Handwriting where it is legible
- Format normalisation across sources
Keeping it honest
- Annotation guidelines and inter-rater checks
- Confidence thresholds and review queues
- Correction loop back into training
- PII detection and redaction
- Retention and deletion policy
- Per-class accuracy reporting
Stack
- spaCy, Hugging Face Transformers
- Claude and OpenAI for generative tasks
- Tesseract, AWS Textract, Azure Document Intelligence
- pgvector, OpenSearch, Elasticsearch
- Whisper for speech
- Python, FastAPI, Airflow
How it works?
The sequence matters more than the label. Skipping the first one is how projects in this area go wrong.
We write the taxonomy and the guidelines with your people, then check that two of them label the same document the same way. Disagreement here predicts failure later.
Testimonials
The people who have to live with what we build
Operations directors, CTOs and plant managers — the ones still using the system a year after the invoice cleared.
5.0
Freight operator · 14 depotsFreight operator · 14 depotsWe wanted answers our team could check against the original document. Having the relevant clause beside the response makes the assistant useful when reviewing policies and contracts.
Our rental journey had to work for both vehicle owners and travellers. Backendz brought listings, availability and bookings together into a platform that feels straightforward to use.
A single headcount does not tell us where congestion is developing. The zone-based view gives our team a clearer picture of how people are moving and where attention may be needed.
Tender preparation involves gathering evidence and getting the right people to review each section. Backendz brought those steps into one workspace, with drafts our team can check and approve.
Our estimating process needed to reflect how we actually price work. Backendz took time to understand the inputs and rules, then built a quoting journey our team could review and refine.
Thomas MitchellManaging Director at Ridgewell Roofing & CladdingThe team took time to understand how our staff work between appointments. The resulting system brings scheduling and client information together without making the day more complicated.
We could review working features throughout the build and give specific feedback. That made it easier to keep the product focused on the people using it.
Our field team needed access to job details away from the office. The mobile workflow keeps the relevant information close at hand and gives the office a clearer view of progress.
We wanted a client portal that reflected how we deliver our services. Backendz brought documents, requests and progress updates into a straightforward experience for clients and staff.
Our teams were entering the same information into several tools. Backendz mapped the process with us and built a more connected workflow with clear review points.
We needed scheduling, availability and shift changes in one place. Backendz shaped the system around the way our operations team works and stayed involved after launch to help us refine it.
Ask for references and we will put you on a call with the team that runs the system — not a case study PDF.
References available on request
We wanted a practical way to review PPE and safety-zone events using our existing cameras. The team focused on the situations our site managers need to investigate and made the footage easier to review.
The priority was a counting workflow our warehouse team could use during a normal shift. Backendz connected image-based counts with the records we already use and made discrepancies easier to review.
Our information was spread across several operational systems. Backendz designed a clearer way to ask questions, inspect the supporting evidence and keep approval with our team before an action is taken.
The reconciliation workflow gives our finance team one place to review mismatches and supporting records. We can trace an exception back to its source and decide what needs attention.
We had outgrown the spreadsheets that held our pricing rules. Backendz helped turn that knowledge into a quoting tool we can maintain and explain to new colleagues.
Connecting our sales and inventory systems was the priority. The integration gives our team a clearer view of updates and a practical way to investigate records that need review.
The cloud work started with understanding our existing systems and dependencies. The team documented the setup and gave us a clearer basis for monitoring and ongoing maintenance.
The engineers worked alongside our team and explained their decisions clearly. Code reviews and practical documentation helped us stay involved throughout delivery.
Why Backendz
5 yrs
In production
Ops CopilotTender Response DrafterPolicy & Contract AssistantPallet CountingConstruction Site PPE & Safety MonitoringCrowd Density MonitoringDigital Security PlannerVirtual EstimatorJoblogyPamacraftsRollBnBVenus AestheticsShiftTrackerFreight DispatchInvoice Reconciliation AIChilled TraceabilitySteel Quoting EngineFactory Floor AppFleet Driver App
We quote fixed and we hold it
2
Platforms run in-house
Document extractionClassification & routingNamed entity recognitionSemantic searchSentiment & intentTopic modellingSummarisationOCR pipelines
Cloud Strategy & ArchitectureCloud MigrationCustom ERP DevelopmentInfrastructure ModernisationCloud EngineeringERP ImplementationCloudOpsERP IntegrationDevOpsSupport & Managed Cloud ServicesERP Customisation & ModulesCloud Cost OptimisationERP Data MigrationCloud Security & ComplianceAI & Edge InfrastructureERP Support & OptimisationSaaS Stack AuditCustom CRM DevelopmentCustom HR & Workforce ManagementCustom Job & Field Service ManagementCustom Client Portals & BookingSaaS Data Migration & ExitAI Proof of Concept & MVP
MultilingualSpeech to textRedaction & PIITaxonomy designActive learningAnnotation toolingspaCyTransformers
AI Agent DevelopmentCustom AI Assistants & ChatbotsGenerative AIAI Workflow AutomationNatural language processingCustom Software DevelopmentComputer Vision DevelopmentWeb App DevelopmentMobile App DevelopmentAI Video Analytics for CCTVProduct DevelopmentMVP DevelopmentUX/UI DesignApplication ModernisationSoftware Testing & QAMachine Learning & Predictive AnalyticsERP & operationsAI Product DevelopmentData & cloudProduct & designAutomation & integrationsSupport & operations
45
Services, one team
Senior engineers, not account managers
24/7 production support and monitoring
Bring us the part of the business that still runs on workarounds
FAQ
The things people ask before they commit.
Still have questions?
Ask it on the call. We would rather answer an awkward question early than discover it in month three.
Book a callOur documents are scans of varying quality. Does that work?
Usually, with an OCR stage tuned to your material and a confidence threshold that routes the worst pages to a human. We test on your genuinely bad examples during feasibility rather than on clean ones, because the bad ones decide the economics.
LLM or a traditional model?
Whichever wins on your data. LLMs are strong on varied, unstructured material and on tasks you cannot get enough labels for; a fine-tuned classifier is usually faster, cheaper and more consistent on a fixed taxonomy at volume. We benchmark both and show you the numbers.
How do you handle personal data in text?
Detection and redaction as a pipeline stage, retention rules agreed with you, and processing kept inside your estate where the policy requires it. Text is where personal data hides in fields nobody audits, so we treat it as a design constraint, not an afterthought.
Can it handle more than one language?
Yes. Modern multilingual models handle mixed-language corpora well; where a language is critical to the business we validate it separately rather than trusting an average score across all of them.
What accuracy can we expect?
On structured forms, high nineties with review in the loop. On messy free text it depends entirely on how well-defined the labels are — which is why we spend the first week on the taxonomy rather than on modelling.
Does it need to replace our current process on day one?
No, and it should not. We usually run it alongside the manual process first, compare outputs, and only shift volume across once the numbers justify it.