Turn language into decisions.
End-to-end Natural Language Processing — text analysis, sentiment, chatbots, speech recognition, and real-time translation. Built on your corpus, tuned for your domain, and shipped with the instruments to keep it accurate as language shifts.
80% of enterprise data is language. Most of it goes unread.
Reviews, tickets, contracts, calls, chat transcripts, clinical notes — the mass of evidence your business already generates. NLP pipelines turn that unstructured stream into signals your analytics, product, and support teams can act on. We design them on your corpus, ship them into production, and keep them accurate as language drifts. Explore the adjacent custom AI engagements or our full AI/ML capability surface.
Every pipeline is fine-tuned on your text or voice data. Generic models set the floor; domain fit sets the ceiling.
Evaluation suites, confidence intervals, and human-in-the-loop review ship with the pipeline. Nothing is black-box.
The same pipeline serves a 200ms chat turn and an overnight backfill — one system, two cadences, zero duplication.
Six disciplines, one pipeline.
Most real engagements pull from more than one — a chatbot feeding into sentiment analytics, a translation layer sitting on top of document intelligence. We design the intersection, not the silo.
- 01
Text analysis and mining
Extract structure from the 80% of your data that lives in documents, tickets, and chat logs. Entity extraction, topic modelling, and summarisation tuned to your domain vocabulary — not generic LLM prompts.
- 02
Sentiment analysis
Read the emotion behind reviews, survey responses, and social chatter at scale. Fine-tuned classifiers that understand sarcasm, negation, and industry-specific idioms most general models miss.
- 03
AI-powered chatbots
Conversational agents that handle support, bookings, and knowledge-base lookup 24/7. Retrieval-augmented, grounded in your content, and handed off to humans on escalation — not just a prompt wrapper.
- 04
Language translation
Real-time and batch translation across dozens of languages. Deployable for websites, product descriptions, live support, and internal comms — with glossary overrides for terminology that must stay exact.
- 05
Speech recognition
Transcribe, command, and route calls from voice. Tuned acoustic models for noisy environments, accented speech, and domain jargon — plus alignment with existing IVR and telephony stacks.
- 06
Document intelligence
Classify contracts, extract clauses, and turn scanned forms into structured records. Legal, medical, and financial pipelines where precision and audit trails matter more than novelty.
Corpus to production, one team throughout.
The people who frame the problem are the people who ship the model — and the people you call when language drifts in month eighteen. No handoffs, no staffing pyramid.
A short workshop with your ops and product leads. We map the text or voice surfaces where value is trapped, score them, and pick the first one worth automating.
We inventory the data you already have — support tickets, review dumps, call recordings, PDFs. Labelling, privacy, and retention constraints get surfaced before any model runs.
Transformer, fine-tuned classifier, RAG, hybrid — picked to match your latency, cost, and accuracy envelope. We benchmark candidates on your data, not a public leaderboard.
A working pipeline on your real corpus, evaluated against measurable baselines in two to four weeks. You leave with numbers a CFO can read — not a demo video.
Observability, drift detection, human-in-the-loop review, and rollback. NLP models decay as language shifts; we build the instruments that catch it before users do.
Quarterly reviews, retraining against labelled feedback, and vocabulary refresh. Your pipeline widens its lead each cycle rather than calcifying.
NLP is domain-sensitive. The vocabulary wins.
A clinical note and a support ticket share syntax but nothing else. We bring vertical context through dedicated practice leads — not generic consultants reading a playbook. Dive into our industry practices for playbooks.
- 01
Healthcare
Clinical-note summarisation, intake chatbots, and voice-transcribed visit records. HIPAA-compliant pipelines and de-identification built in from day one.
- 02
Finance
Document analysis for loan applications, compliance triage for filings, and sentiment tracking across earnings calls. Deterministic where regulators need it, learned where they don't.
- 03
Legal
Contract clause extraction, deposition transcription, and precedent search. Pipelines engineered for traceability — every extracted claim links back to the source line.
- 04
Retail and e-commerce
Multilingual product descriptions, review sentiment, and conversational shopping assistants. One backbone scales across catalogues, regions, and seasonal launches.
- 05
Media and publishing
Topic detection, auto-tagging, and summarisation for content archives and live feeds. Editorial workflows augmented rather than replaced.
- 06
BPO and customer support
Call-intent classification, agent assist, and post-call analytics. Reduce average handle time while surfacing the themes no survey will catch.
Frameworks chosen for production longevity.
Pragmatic stack — classical NLP where it's still the right answer, modern transformers where reasoning matters. See the wider AI tech stack breakdown for rationale on specific choices.
Not a chatbot vendor. A language-systems team.
27 years of shipping software across 90+ countries. NLP is the newest surface, layered on the same engineering discipline that's carried every earlier wave.
- 01
Domain-tuned, not template
Generic models collapse on jargon, misspellings, and region-specific idioms. Every NLP pipeline we ship is fine-tuned on your corpus — accuracy lifts show up on week two, not eventually.
- 02
Multilingual by default
Deployments cover English plus the languages your customers actually speak. Translation, transliteration, and cross-lingual retrieval live in the first commit, not a later phase.
- 03
Hybrid architectures
LLMs where reasoning matters, classical NLP where latency and cost dominate. The right tool for the right layer — not a single expensive hammer on every task.
- 04
Real-time and batch
The same pipeline serves a 200ms chat response and an overnight billion-row analysis. You don't maintain two stacks for two cadences.
- 05
Privacy-first processing
PII redaction, on-premise or VPC deployment, and zero data-retention contracts with upstream providers. Regulated industries ship NLP with us because compliance is scoped in, not bolted on.
- 06
Owned IP
Fine-tuned weights, glossaries, evaluation suites, and orchestration code all transfer at engagement close. No usage meters, no residual licenses.
Outcomes that stick in production.
Pilots are easy; operational NLP is rare. These are the results we bring to engagements — and the habits that keep them compounding.
Unlock unstructured data
Most enterprise value is buried in free-form text and voice. NLP pipelines turn that mass into structured signals your existing BI and workflow tools can consume.
24/7 customer coverage
Chatbots and voice agents handle the long tail of repetitive queries without burning out a support team — and escalate the hard ones with full context attached.
Faster, evidence-backed decisions
Sentiment shifts, emerging complaints, and regulatory signals surface in hours instead of weeks. Your teams react to what's happening, not last quarter's report.
Global reach without rework
Translation and multilingual modelling widen your funnel without rebuilding the product for each market — the same backbone serves every region.
Lower cost per interaction
Automate the 70% of interactions that don't need a human, without eroding the experience. Unit economics of support, moderation, and review drop meaningfully.
Defensible data advantage
Every labelled interaction improves the next model. Teams that invest early compound an accuracy gap competitors can't shortcut with frontier model releases.
What teams ask before they commit.
01When should we fine-tune our own model versus use a general-purpose LLM via API?
02How accurate are NLP pipelines on our data?
03Can you work with our existing data — support tickets, PDFs, call recordings?
04Do you deploy in our cloud or yours?
05How do you handle multilingual or regional-language needs?
06What happens when language drifts or new topics emerge?
07How do you price NLP engagements?
One corpus, one working POC.
Share a sample of your text or voice data. We'll come back with a scoped POC, measurable accuracy targets, and an honest view on whether NLP is the right lever for the outcome you need.