From the first language sample to monitored production, you get NLP work scoped to the text your business actually produces.
DigiLawyer needed a language assistant that could answer rights questions with live legal context, including for users in tier 2 and tier 3 cities.
Over 200 daily user queries in the first month, with legal research time reduced from hours to seconds.
Off-the-shelf language APIs sound fluent in demos. You need models that survive your acronyms, policy phrases, and angry ticket slang.
You are not buying another chatbot skin. You are buying a team that proves one language KPI, integrates it, and keeps accuracy honest when wording shifts.
Every vertical speaks differently. We shape NLP around the tickets, claims, listings, and reviews your industry actually writes.
You get a six-step path from messy text to monitored language systems, with gates so spend follows proven accuracy.
Start with a scoped language pilot, expand to a full NLP build, or embed a dedicated team. Pricing stays Custom until we see your text and systems.
These results show NLP-relevant and adjacent AI work BigOhTech has shipped. DigiLawyer and hospitality sentiment map closest to language systems.
Generic LLM APIs and in-house-only hires often stall after a clever demo. Compare what you need when domain language, evals, and integration matter.
Different text problems need different NLP App Solution shapes. Pick the use case that matches how language shows up in your product.
Stack choice follows your privacy rules, latency budget, and where answers must land. We stay on tools BigOhTech already delivers with on AI programs.
Partners help you train, serve, and scale language workloads without forcing every decision onto one vendor.
We turn tickets, reviews, and documents into intents, fields, and themes your teams can act on without reading every line. Why do so many language pilots stall before they change a queue?
These capabilities combine so your product can understand, extract, and respond with thresholds your operators accept.
Yes. Predictions ship through secure APIs and workflows so agents see tags, fields, or drafts inside tools they already use.
No. You need representative samples. We assess quality, design labels, and plan cleanup so training is not wasted on unusable text.
We sample live traffic, watch confidence and error queues, and retrain on versioned datasets when products or slang change.
Yes. We fine-tune or train classifiers on your tickets, docs, and policies, then measure on eval sets that mirror production wording.
We classify intent and urgency, suggest replies or routes, and hand off to humans when confidence or policy rules require it.
Yes. Dedicated NLP engineers can pair on data, training, evals, and deployment without taking ownership of your IP.
They cover scoping, custom models, intent and entity systems, document extraction, customer-service NLP, sentiment analytics, integration, and monitoring with retraining after launch.
NLP Consulting locks the language KPI, data readiness, and integration path first so you do not spend on a bot that cannot route or escalate correctly.
Accuracy depends on label quality and class balance. We publish eval metrics on your held-out set and set thresholds operators can live with.
Yes. You can begin with a 40-hour free engagement on one language KPI before committing to a full NLP build.
Share your project goals, timeline, and technical requirements. We'll review your requirements and send a tailored solution with an indicative estimate within 48 business hours.
