Text Classification
Intent detection, ticket routing, and content tagging using fine-tuned encoders or zero-shot classifiers.
NLP Solutions
NLP solutions use natural language processing to turn unstructured text into structured, actionable data through text classification, entity extraction, sentiment analysis and semantic search built on embeddings. SpeedMVPs builds them independent of, or alongside, generative LLMs, choosing purpose-built models when they're faster, cheaper and more accurate than a general-purpose chat model for a fixed, well-defined task. Typical delivery takes 2-3 weeks.
Natural language processing covers the toolkit that turns unstructured text into structured, actionable data: classification, entity extraction, sentiment analysis, and semantic search built on embeddings. We build this independent of (or alongside) generative LLMs, choosing purpose-built models when they are faster, cheaper, and more accurate than a general-purpose chat model for a fixed, well-defined task.
2-3 Weeks
Typical Delivery Timeline
The NLP Toolkit, Applied to Your Data
1
Text Classification & Routing
- Intent classification for support tickets, emails, and form submissions
- Content moderation and multi-label tagging pipelines
- Fine-tuned encoder models (DistilBERT, RoBERTa) vs. zero-shot LLM classification, chosen by cost and latency needs
- Confidence thresholds that route uncertain cases to human review
2
Entity Extraction & Structuring
- Named entity recognition (NER) for people, organizations, dates, amounts, and custom entity types
- Relation extraction linking entities into structured records
- Fine-tuning on domain vocabulary that off-the-shelf NER models miss
- Output validated against a defined schema, not free-text guesses
3
Sentiment & Opinion Analysis
- Aspect-based sentiment analysis (what specifically a reviewer liked or disliked, not just an overall score)
- Mining reviews, support tickets, and survey text for trend aggregation over time
- Calibration for domain-specific language, sarcasm, and industry jargon that generic sentiment APIs mishandle
- Confidence scoring so ambiguous text is flagged rather than force-classified
4
Semantic Search & Embeddings
- Embedding model selection (OpenAI, Cohere, or open-source sentence-transformers) based on your data and budget
- Vector indexing on pgvector, Pinecone, or Qdrant
- Hybrid search combining keyword (BM25) and embedding similarity, since pure semantic search misses exact-match cases like SKUs and error codes
- Re-ranking and embedding versioning as your content and models change
5
Multilingual NLP
- Language detection and routing for mixed-language input
- Multilingual embedding models (e.g. multilingual-e5, LaBSE) for cross-lingual search without a separate model per language
- Machine translation pipelines where the task genuinely requires generating target-language text
- Evaluation on non-English test sets, not just the majority language in your data
6
Model Selection & Evaluation
- Choosing between classical ML, fine-tuned transformers, and LLM zero-shot based on measured accuracy, latency, and cost
- Labeled evaluation sets built before model selection, not after
- Precision/recall/F1 reporting against a held-out test set
- Drift monitoring so production accuracy decay gets caught before it affects users
Our NLP Services
The non-generative half of the NLP toolkit, applied where it beats a general-purpose LLM
Entity Extraction
Named entity recognition and relation extraction that turns free text into structured fields.
Sentiment & Opinion Mining
Aspect-based sentiment analysis across reviews, support tickets, and survey responses.
Semantic Search
Embedding-based retrieval with hybrid lexical and vector ranking for accurate, fast search.
Multilingual NLP
Language detection, multilingual embeddings, and translation pipelines for cross-lingual products.
Model Evaluation & Benchmarking
Labeled test sets and metric tracking to pick the right model, not just the newest one.
Drift Monitoring & Retraining
Production monitoring that flags accuracy decay and triggers retraining before it hurts users.
Why Teams Pick SpeedMVPs for NLP
Non-generative when it's the right call
We do not reach for an LLM by default. When a fine-tuned classifier is faster, cheaper, and more accurate, that's what we ship.
Evaluation before deployment
Every model ships with a precision/recall report against a labeled holdout set, not a demo that looked good once.
Built for your domain vocabulary
Off-the-shelf models miss your product's jargon and edge cases; we fine-tune and validate on your actual data.
Multilingual from the start
Language detection and multilingual embeddings are built in when your users aren't all English speakers, not bolted on later.
Why Teams Pick SpeedMVPs for NLP
Non-generative when it's the right call
We do not reach for an LLM by default. When a fine-tuned classifier is faster, cheaper, and more accurate, that's what we ship.
Evaluation before deployment
Every model ships with a precision/recall report against a labeled holdout set, not a demo that looked good once.
Built for your domain vocabulary
Off-the-shelf models miss your product's jargon and edge cases; we fine-tune and validate on your actual data.
Multilingual from the start
Language detection and multilingual embeddings are built in when your users aren't all English speakers, not bolted on later.
NLP Solutions, FAQ
For well-defined tasks like classification, NER, or sentiment analysis, a fine-tuned smaller model (e.g. DistilBERT or a similar encoder) is usually cheaper and faster at inference than calling a large hosted LLM, and often more accurate because it's trained specifically for the task. We reserve general-purpose LLMs for open-ended generation and reasoning, and use purpose-built NLP models for classification and extraction tasks with fixed label sets.
Keyword search matches literal terms; semantic search uses embeddings to match meaning, so a query for 'cancel my plan' can surface a document titled 'how to end your subscription' with no words in common. In practice we usually combine both (hybrid search) because pure semantic search can miss exact-match cases like SKU numbers or error codes that keyword search handles better.
It depends on the task and how similar it is to what a pretrained model has already seen. Simple binary classification can work with a few hundred labeled examples per class using transfer learning; nuanced multi-class or domain-specific extraction tasks typically need low thousands. We start with what you have, run a baseline, and tell you concretely whether more labeling will move accuracy before you invest in it.
We use multilingual embedding models (e.g. multilingual-e5 or LaBSE) so search and classification work across languages without training a separate model per language, and add machine translation only where the task genuinely requires generating text in the target language rather than just matching or classifying it.
Trusted by Global Companies Building AI Products
We've helped startups and enterprises worldwide transform their AI ideas into production-ready MVPs in 2–3 weeks. From fintech platforms to AI assistants, our global MVP development services have launched 18+ AI products serving users across the US, Europe, and Asia.
Portfolio: AI Products Built for Global Startups
From content platforms and AI assistants to analytics dashboards and fintech solutions: see how we've transformed ideas into production-ready MVPs in 2-3 weeks across diverse industries. Each product here is one our team worked on.
UseArticle
AI-powered content creation and management platform that helps teams produce high-quality articles at scale.
AgentHi
Intelligent virtual assistant that streamlines customer support and automates routine business tasks.
StatsHub
Comprehensive analytics dashboard providing real-time insights and data visualization for businesses.
Harimaxx
Personal fitness companion with AI-driven workout plans and nutrition tracking for optimal health.
Vaga
Smart travel planning app that curates personalized itineraries and local experiences.
FoodScan
Nutrition analysis app that scans food items and provides detailed nutritional information instantly.
MyJobReach
Job matching platform connecting talented professionals with their dream opportunities.
TravelGram
Social platform for travelers to share experiences, discover destinations, and connect globally.
SuperStatz
Advanced sports statistics platform delivering in-depth analysis and performance metrics.
Cashbook
Simple expense tracking and budgeting app that helps users manage their finances effortlessly.
TypeFast
Typing speed improvement platform with gamified lessons and real-time performance tracking.
Easy Loan
Streamlined loan management system that simplifies borrowing and lending processes.
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Post-MVP Iteration and Growth
Your MVP launched. Now build what users actually need. We run structured iteration cycles, 2-week sprints that ship improvements, new features, and AI quality upgrades based on real usage data.
Ready to Build Your MVP?
Schedule a complimentary strategy session. Transform your concept into a market-ready MVP within 2-3 weeks. Partner with us to accelerate your product launch and scale your startup globally.
The call is 30 minutes with Diyanshu Patel, our CEO and co-founder. Afterwards you get a written proposal with timeline, pricing and development plan.
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