Since 2010
Building Software
Text In. Structured Decisions Out.
NLP Development Services
We build natural language processing systems that read, classify, extract, and summarise text at production scale, from modern transformer pipelines to lean classifiers that beat LLM pricing at volume. Fixed pricing, senior engineers.
- 120+
- AI Engineers
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 3,900+
- Apps Published
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
When classic NLP beats a big model
Not every text problem needs a frontier LLM. Classification, entity extraction, and routing at high volume are often better served by fine-tuned transformer models that cost a fraction per document and run in milliseconds. Part of our job is knowing which tool the problem actually calls for, and proving it with evaluation before committing.
We deliver the whole pipeline: data labelling strategy, model training or configuration, serving, and monitoring for drift. Where understanding must feed conversation or generation, the same pipelines plug into our AI development services stack.
What our NLP development services cover
Understanding tasks we ship most often, end to end.
Text classification
Routing, tagging, and triage of documents, tickets, and messages with confidence scores and human review for the uncertain middle.
- ✓Multi-label classifiers
- ✓Confidence thresholds
- ✓Active learning loops
- ✓Drift monitoring
Entity extraction
Structured data pulled from contracts, invoices, and forms, validated against schemas and pushed into your systems.
- ✓Custom entity types
- ✓Schema validation
- ✓Human-in-the-loop review
- ✓Downstream integrations
Semantic search
Meaning-based search over documents and knowledge bases, with hybrid keyword blending and per-field relevance tuning.
- ✓Embedding pipelines
- ✓Hybrid retrieval
- ✓Relevance evaluation
- ✓Access-aware results
Summarisation pipelines
Condensing long documents into structured summaries with citations, tuned to your formats and review workflows.
- ✓Domain summary templates
- ✓Citation of sources
- ✓Length and focus controls
- ✓Quality sampling
Sentiment and intent analysis
Voice-of-customer analytics over reviews, surveys, and conversations, mapped to categories your teams act on.
- ✓Aspect-level sentiment
- ✓Custom intent taxonomies
- ✓Trend dashboards
- ✓Multilingual coverage
Multilingual NLP
Processing across languages with quality measured per language rather than assumed from a translation layer.
- ✓Cross-lingual models
- ✓Per-language evals
- ✓Script and locale handling
- ✓Fallback strategies
What every engagement includes
Every NLP project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped NLP roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All NLP source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with NLP
Systems our clients run over their text at scale.
Ticket triage engine
Automatic classification and routing of support tickets to the right queue with priority scoring and escalation rules.
Contract intelligence
Extraction of parties, dates, obligations, and renewal terms from contract libraries into searchable structured data.
Voice-of-customer platform
Sentiment and theme analysis across reviews, NPS comments, and survey responses, reported per product line.
Enterprise semantic search
A search layer that finds documents by meaning across repositories, respecting per-user access permissions.
Compliance monitoring
Automated scanning of communications for policy-flagged language, with review queues and audit trails.
How a NLP Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your NLP goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The NLP architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. NLP work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged NLP launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our NLP builds
Default choices, stated with reasons.
Models
Fine-tuned transformer encoders for classification and extraction where volume demands low cost; LLMs where reasoning or open-ended understanding dominates.
Data tooling
Annotation workflows with inter-annotator agreement checks, because labelled data quality caps model quality, always.
Serving
Batch and real-time inference with autoscaling, caching where inputs repeat, and per-model latency budgets.
Evaluation
Held-out test sets, per-class metrics, and production drift monitoring, reported in dashboards your team keeps.
How intelligence flows through the pipeline
What the model reads, and what it is allowed to change.
Text ingest
Normalises documents and messages into the processing pipeline.
Reads
Documents, tickets, messages, forms
Changes
Normalised text records
Understanding models
Classify, extract, and summarise according to task configuration.
Reads
Normalised text and task labels
Changes
Structured output records
Review and delivery
Routes uncertain cases to humans and delivers results to your systems.
Reads
Confidence scores, review queues
Changes
Downstream records and audit logs
What's included, standard
The baseline on every NLP engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each NLP release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Data readiness assessment
An assessment of your text corpora, labelling needs, and achievable accuracy before build starts.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for NLP work
The differences live in engineering habits, not pitch decks.
Right-sized models
We deploy lean fine-tuned models where they beat LLMs on cost and latency, and say so with evaluation evidence rather than fashion.
Data quality discipline
Annotation guidelines, agreement checks, and active learning, because model quality is capped by data quality and we treat that as the real work.
Accuracy you can audit
Per-class metrics on held-out sets and drift monitoring in production, so accuracy claims come with receipts.
120+ AI engineers
A deep AI bench means modelling, data engineering, and platform work happen in one accountable team.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with text intelligence systems among them.
NLP development FAQs
$ moxace launch --nowReady to Put Your Text to Work?
Talk to our NLP team about a fixed-scope build with measured accuracy, right-sized models, and full source-code ownership.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
All product names, trademarks, and brands referenced are the property of their respective owners, used for identification purposes only. Read the full legal notice on our disclaimer page.