NLP Discovery Sprint (2 weeks)
Fixed-fee paid pilot. We audit your data, validate the use case, ship a working POC, and deliver a production roadmap. Best for teams still scoping the problem.
Turn unstructured text, voice, and document data into measurable business value with enterprise NLP solutions. We build production-ready NLP solutions for chatbots, document intelligence, sentiment analysis, and LLM-powered applications - engineered for scale, security, and accuracy.
Trusted by industry leaders
Decode customer sentiment from reviews, support tickets, social media, and survey data using advanced sentiment analysis tools. Our models go beyond positive/negative to detect intent, urgency, and emotion – giving product and CX teams real-time signal to act on.
Extract clauses, entities, dates, and obligations from contracts, clinical notes, invoices, and reports with document processing automation. Production deployments cut manual review time by 60-80% and unlock structured insight from previously unsearchable archives.
Build context-aware chatbots and voice assistants that understand intent, retain conversation history, and integrate with your CRM and knowledge base. We engineer both rule-based and LLM-powered conversational systems via our conversational AI development practice.
Train custom NER (Named Entity Recognition) models on your domain data using named entity recognition models – whether that is medical terminology, financial instruments, or legal clauses. Used for compliance flagging, automated tagging, and downstream analytics pipelines.
Generate summaries, reports, marketing copy, and structured outputs from unstructured input using generative AI language models. Our team builds on top of GPT-4, Claude, LLaMA, and Mistral with custom LLM development for clients who need full data sovereignty.
Engineer multilingual NLP solutions that operate across multiple languages, with locale-aware tokenization and culturally accurate sentiment models. Critical for global e-commerce, support, and compliance use cases.
That is the most common reason teams call us. Bring us your data sources and three problem statements - we will map a solution architecture in 30 minutes.
Book My NLP Discovery CallClinical note extraction, ICD-10 auto-coding, patient triage chatbots, prior-authorization automation
Contract analysis, KYC document automation, transaction narrative classification, fraud signal detection from communications
Clause extraction, e-discovery, contract redlining, regulatory document monitoring
Review sentiment, search relevance tuning, product attribute extraction, conversational shopping assistants
Resume parsing, semantic candidate matching, employee feedback and pulse analysis
Intent classification, multilingual ticket routing, AI-augmented agent assist, knowledge base search
We assess your text and voice data sources, label quality, regulatory constraints, and integration surface area. The output is a written technical scope with effort estimates and a risk register, not a generic SOW.
Custom transformer fine-tuning, retrieval-augmented generation with RAG development, API-based LLMs, or classical NLP - we pick the architecture that fits your accuracy, cost, and latency budget. Not the one we already know.
Most NLP projects do not fail at training; they fail at evaluation. We build domain-specific eval sets, run human-in-the-loop validation, and track precision/recall/F1 against business KPIs - not just academic benchmarks.
CI/CD for models, drift monitoring, prompt versioning, and on-call response. We deliver systems that survive in production, not models that work only in a Jupyter notebook.
We do not hand off Jupyter notebooks. Every NLP system we build ships with monitoring, versioning, drift detection, and a rollback path. Our MLOps maturity is what separates a 2-week deploy from a 6-month deploy.
Our team includes computational linguists and ML engineers who fine-tune models on your enterprise corpus. The result: accuracy that holds up on your jargon, your edge cases, your customers - not just public benchmarks.
HIPAA, GDPR, ISO/IEC 27001, and SOC 2-aligned engineering. Data residency options across US, EU, and APAC. NDA-backed engagement with no client data used for model training. The compliance work is built in, not bolted on.
You can engage us as a project team, an embedded NLP squad inside your engineering org, or via AI agent development services for autonomous workflows. We adapt the engagement to your team structure, not the other way around.
Fixed-fee paid pilot. We audit your data, validate the use case, ship a working POC, and deliver a production roadmap. Best for teams still scoping the problem.
Fixed scope, fixed price, milestone-based delivery. Best for teams with a defined NLP product or feature on the roadmap.
Hire dedicated NLP developers, ML engineers, and data scientists who work as an extension of your team. Best for teams shipping continuously or scaling existing NLP products.
What "HIPAA-ready" and "GDPR-ready" actually means when we build for you.
Developing a custom NLP solution can cost anywhere from $25,000 for a basic implementation to $350,000+ for a complex system. The exact price depends on your features, data requirements, and integrations. Share your project details with us to get a precise, tailored estimate.
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Read MoreWe build chatbots, voice assistants, document intelligence systems, sentiment analysis platforms, NER pipelines, multilingual translation, and generative NLP applications powered by LLMs. Each is custom-built and fine-tuned on your domain data.
Yes, we seamlessly integrate NLP solutions with CRMs, ERPs, customer support tools, and other enterprise software. Our team ensures compatibility with APIs and databases to enhance workflow automation and data processing efficiency.
Yes. Our Dedicated NLP Team model lets you hire NLP engineers, ML researchers, and data scientists on a monthly retainer. They work as an extension of your team, attend your standups, and report into your engineering leadership.
Yes. We engineer generative NLP systems on top of GPT-4, Claude, LLaMA, and Mistral. We also build self-hosted LLM deployments for clients who require full data sovereignty.
We are ISO/IEC 27001 certified and our architecture is HIPAA-ready and GDPR-aligned. Standard practice includes encrypted data pipelines, no training on client data, role-based access, audit logs, and NDA-backed engagement from day one.
Absolutely! We build NLP models that support multiple languages, allowing businesses to engage with global audiences. Our solutions include translation, localization, and contextual language understanding for accurate communication.
NLP (Natural Language Processing) is the umbrella discipline. NLU (Natural Language Understanding) focuses on machine comprehension - intent, entities, meaning. NLG (Natural Language Generation) focuses on producing human-readable output. Most modern enterprise systems combine all three.
Yes. We have shipped NLP integrations with Salesforce, HubSpot, SAP, ServiceNow, Zendesk, Intercom, and dozens of custom enterprise systems via REST APIs, GraphQL, webhooks, and event streams.
A POC takes 2-4 weeks. A production v1 takes 8-16 weeks. An enterprise NLP platform takes 4-9 months. Timelines depend on data readiness, integration complexity, and compliance scope.