01 / FEATURE
Document classification
Route and categorise across hundreds of classes with measured precision and recall, not vendor-published benchmarks.
Production-grade Text & NLP, turn unstructured text into structured insight, in any language.
Contracts, support tickets, customer feedback, compliance filings. We build NLP pipelines that read, classify and extract at scale, with the accuracy your domain demands and the compliance your regulator requires.
The problem
Generic NLP doesn't understand your domain.
Off-the-shelf NLP is trained on news articles and Wikipedia. Your text is the opposite, contracts, claims, clinical notes, ticket threads, regulator-filed documents. Specialised vocabulary, mixed languages, structured-but-not-quite formats.
Your accuracy bar is high. A 90% extraction rate on news headlines is fine; on a contract clause it's a contract you can't enforce. Your compliance bar is strict, PDPA, sectoral rules, audit trails on every classification.
What you need is an NLP pipeline tuned to your text, evaluated against your edge cases, and instrumented so you see drift before it costs you.
Our approach
Custom NLP pipelines, FORGE-aligned, in your languages.
We design the extraction schema, fine-tune models on your text, ship with PII redaction and audit trails, and instrument drift from day one. Multilingual across ASEAN languages, with shared entity models so you don't maintain twelve copies of one pipeline.
Capabilities we ship into production.
02 / FEATURE
Named entity recognition
Extract people, organisations, dates, amounts and your custom entities, contract clauses, claim codes, drug names, at scale.
03 / FEATURE
Sentiment & intent analysis
Tone, sentiment and underlying intent across feedback, tickets and social. Multilingual, with code-switching handled.
04 / FEATURE
Summarisation
Long documents condensed into structured summaries that preserve the critical detail, not generic abstracts.
05 / FEATURE
Multilingual NLP
ASEAN languages with shared entity models, one pipeline, consistent accuracy, lower maintenance burden.
06 / FEATURE
Compliance & PII redaction
Detect and redact PII automatically. PDPA, GDPR and sectoral rules baked in, not bolted on.
The Framework
How we build it, FORGE-aligned, four phases.
From schema design to live pipeline, with accuracy and drift instrumented continuously.
PHASE 01
ASSESS
Audit your text corpus and downstream use. Define the extraction schema, classification taxonomy and accuracy bar. Map languages, formats and compliance constraints.
Deliverable
NLP brief & extraction schema
PHASE 02
ARCHITECT
Design the pipeline, ingestion, preprocessing, model selection, fine-tuning strategy, redaction layer, and evaluation harness. Decide hosted vs. open-source per task.
Deliverable
NLP architecture & eval plan
PHASE 03
BUILD
Label data, fine-tune models, build the pipeline and the API surface, integrate with downstream systems, and ship monitoring dashboards.
Deliverable
Production NLP pipeline + dashboards
PHASE 04
OPERATE
Drift monitoring, active-learning loops on misses, periodic retraining as language and topics evolve, and continuous schema extension.
Deliverable
Live pipeline + accuracy SLAs
Where Text & NLP earns its keep.
Six text-heavy patterns we've shipped, each measured against a manual baseline, each instrumented for drift.
Contract intelligence
Clause extraction, obligation tracking, risk flagging across thousands of contracts, in the languages your counterparties use.
Clause-level
Support ticket triage
Auto-classify and route tickets, draft first responses, surface trend signals to product. Multilingual, sentiment-aware.
24/7
Customer feedback analysis
Reviews, surveys, social mentions, structured sentiment and topic extraction with drill-down to source quotes.
Quote-linked
Compliance & filing review
Regulatory filings, KYC packets, AML alerts, entity extraction with audit trails the regulator can read.
Audit-ready
Clinical & medical text
Discharge summaries, pathology notes, medication lists, structured extraction with clinician review on uncertain cases.
Clinician-in-loop
Knowledge mining
Mine internal docs, manuals and tickets for FAQs, gaps and emerging topics, feeding GenAI and search systems upstream.
Feeds RAG
Custom NLP vs. an off-the-shelf API.
Why generic NLP endpoints undershoot the moment your vocabulary looks like itself.
Status Quo
Off-the-shelf NLP API
- Generic categories, no domain vocabulary
- Vendor benchmarks on news data, not yours
- Per-language pipelines, duplicated maintenance
- PII handling left to you
- Drift invisible until accuracy collapses
- Cloud-only, your text leaves the perimeter
The EIS Way
EIS custom NLP
- Fine-tuned on your domain text, your custom entities
- Eval harness measures precision and recall on your samples
- Multilingual with shared entity models, one pipeline
- PII redaction and audit trails first-class
- Drift monitoring and active-learning from day one
- On-prem option for regulated text
Compliance and governance, by design.
PDPA, GDPR and sectoral rules mapped into the pipeline
PII detection and redaction before any text leaves the perimeter
Audit trails on every classification and extraction
On-prem deployment option for regulated text
Active-learning loops on every miss, accuracy improves with use
Schema versioning so downstream systems don't break on changes
FAQ
Frequently asked
What product, ops and compliance leaders ask before they put NLP into production.
Q01How much labelled data do we need?
Depends on the task. Classification across a small taxonomy can work with a few hundred samples per class. Custom entity extraction usually wants more. We size the labelling effort during ASSESS and use weak supervision where it pays off.
Q02Which languages do you support?
English plus the major ASEAN languages, Thai, Vietnamese, Indonesian, Filipino, Malay, Burmese, and Mandarin. Shared entity models across languages where the schema overlaps.
Q03How do you handle PII?
Detection and redaction before downstream processing. PDPA, GDPR and sectoral rules are mapped into the pipeline at ARCHITECT, not retrofitted later.
Q04Can it run on-prem for regulated text?
Yes. Open-source models, on-prem inference, no data leaves your perimeter. We size the deployment for your throughput and latency budget.
Q05How do we know accuracy holds over time?
Drift monitoring on every deployment, active-learning loops on every miss, and a periodic retraining cadence sized to how fast your vocabulary moves. You see the metrics monthly.
Book a Text & NLP assessment
30-minute call. We'll review your text corpus, downstream use cases and compliance constraints, and recommend a pipeline shape and accuracy bar.
Book assessment
30 minutes · reply within 1 business day