Your model scores well on clean test data, then misreads slang, code-switching, and the messy language real customers actually use. The hard part was never the model—it's grounding, evaluation, and the production pipeline around it. Banao builds NLP systems that survive real traffic, on the same stack we've run inside our own 300-person operation since 2017.
Most teams sit on millions of tickets, contracts, and reviews they can't read fast enough—and the NLP pilot that looked promising never shipped because nobody solved hallucination, evaluation, or integration. Banao closes that gap: the support-triage and document-extraction stack we run for clients was first proven on Banao's own 300-person operation, across India, UAE, UK, and US. The same NLP layer feeds our generative AI and AI customer-support work when a problem spans more than text.
We build the full NLP stack—ingestion, entity extraction, classification, summarization, and multilingual inference—with evaluation and telemetry wired in so you can see accuracy before it reaches a customer, and catch drift after.
Book a 45-min scoping call
Know What Customers Actually Feel
We build sentiment and intent pipelines over tickets, reviews, and social text—grounded in your own labels, not a generic model's guess—so trends surface while you can still act on them.
Turn 40-Page Documents Into Decisions
Summarization tuned to your document types—contracts, reports, claims—with source citations so reviewers can verify every line instead of trusting a black box.
Read Every Customer, in Every Language
Cross-lingual inference for global support and feedback, handling code-switching and regional language the way real customers write—tested on production traffic, not clean corpora.
Route Every Ticket Without a Human
Auto-classification and tagging that sends each ticket, email, or document to the right queue, with a confidence threshold that escalates the uncertain cases to people.
Pull Structured Data From Messy Text
Entity and relationship extraction that turns unstructured documents into structured records your systems can query—the backbone of the document automation we built for logistics and legal clients.
NLP Built for Your Domain Language
We fine-tune and orchestrate models on your domain vocabulary—clinical, financial, industrial—because off-the-shelf NLP is exactly where most pilots break in production.
Industries We Empower with NLP
Retail & E-commerce
Classify and route product reviews and support tickets automatically, and surface sentiment trends before they show up in churn.
Education & EdTech
Parse free-text student answers to detect misconceptions and summarize dense material to each learner's level—proven on our Studylab AI build.
Healthcare & Life Sciences
Extract entities from clinical notes and research papers into structured records, with citations so clinicians can verify every extraction.
Finance & Insurance
Read compliance documents and claims at a volume no team can match, with evaluation baked in so you see accuracy drift before auditors do.
Manufacturing & Logistics
Extract entities from shipping documents and supplier emails and classify exceptions automatically—the NLP layer behind our Supply Chain Intelligence platform.
Telecom & Utilities
Auto-categorize support messages and route them to the right team, turning a shared inbox into same-day action on flagged issues.
Recent Work
Supply chain
A logistics and manufacturing enterprise was drowning in unstructured shipment documents, supplier emails, and exception reports no team could read fast enough. Banao built a Supply Chain Intelligence Platform with NLP pipelines that extract entities from shipping documents and classify exceptions automatically, routing each to the right team instead of a shared inbox. Planners now act on flagged disruptions the same day instead of discovering them after the delay.
Generative AI | Knowledge Agents
Studylab AI
StudyLab needed to turn open-ended student answers and dense learning material into personalized guidance, not just multiple-choice scores. Banao built an adaptive learning platform where NLP models parse free-text responses to detect misconceptions, and summarization condenses material to each student’s level. Feedback now ties to what students actually wrote, and pacing adjusts to real comprehension instead of a fixed syllabus.
Fuzu, a leading East African career platform, struggled to match millions of job seekers to roles from messy, free-text profiles and listings. Banao built an AI recommendation engine that uses NLP to parse skills and intent from unstructured CVs and job descriptions, grounding every match in real profile signals. Job seekers now see roles that fit their actual experience, even in a market with little structured data.
Identify text analytics goals, define KPIs, and determine the best NLP approach for your business needs. Why this matters: most NLP pilots fail because no one defined what 'accurate enough' means before building—we set the evaluation target first.
Gather structured and unstructured data, clean, annotate, and prepare datasets for NLP model training. Why this matters: real text is full of slang, code-switching, and noise; vendors who skip this step ship models that break on the first production batch.
Choose open-source NLP models or build custom ones, fine-tuning for sentiment, summarization, and multilingual support. Why this matters: off-the-shelf models miss your domain vocabulary—we fine-tune on your language instead of forcing a generic model to guess.
Test model accuracy, performance, and bias using domain-specific datasets and validation metrics. Why this matters: this is the step that separates a demo from production—we test for bias and drift on your data, not a clean benchmark.
Deploy NLP models to cloud, on-prem, or hybrid environments with APIs and system integrations. Why this matters: a model that isn't wired into your CRM, ticketing, or warehouse is a science project—we ship it into the systems your team already uses.
Track model performance, retrain, and optimize pipelines to ensure long-term effectiveness and reliability. Why this matters: NLP accuracy decays as language shifts; we instrument telemetry so you catch drift before customers do, not after.
Client Voices: NLP in Production
Ritika Malhotra
COO, SmartSupply
Thomas Lee
CTO, FinEdge
Unstructured text, finally usable
Banao's NLP pipeline reads our supplier emails and shipment documents and routes exceptions automatically. Our planners stopped triaging a shared inbox and started acting on flagged disruptions the same day.
Here is what you will get for submitting your contact details.
45 minutes of free consultationA strict non-disclosure agreementFree market & competitive analysisSuggestions on revenue models & planningDetailed feature list documentNo obligation proposalAction plan to kick start your projectWe ran an NLP pilot before and it never reached production. Why would this be different?
Usually the model wasn't the problem—grounding, evaluation, and integration were. We diagnose exactly where your last attempt broke, then design for that failure mode. We've broken and fixed our own NLP systems running Banao's operation since 2017; that scar tissue is what you're hiring.
How do you stop the model from hallucinating or mislabeling text?
We ground outputs in your own data, attach source citations to summaries and extractions, and set confidence thresholds that escalate uncertain cases to people. Evaluation and telemetry run continuously, so you see accuracy—and drift—instead of trusting a black box.
Can your NLP handle our industry's jargon and multiple languages?
Yes. We fine-tune on your domain vocabulary—clinical, financial, industrial—and run cross-lingual inference that handles code-switching and regional language, tested on production traffic rather than clean corpora.
Who owns the models, data, and IP?
You do—100%. Custom code, trained models, and training data are yours. We don't retain IP, sub-license, or build derivative products from your data. Mutual NDA before the first detailed discussion, DPA for regulated industries.
Should we just build this in-house?
Fair option. In-house NLP teams typically take 12–18 months because the talent is hard to hire and the project competes with day jobs. We compress that to 8–12 weeks because it's our day job—and many clients come to us six months into a stalled in-house build.
Will it integrate with our existing systems—CRM, ticketing, data warehouse?
Yes. We're stack-agnostic and ship NLP into the systems you already run via APIs and event pipelines—Salesforce, Zendesk, your warehouse, or custom backends. A model that isn't integrated is a science project; we don't deliver those.
What does an NLP engagement cost and how long does it take?
Most production NLP engagements run $50K–$250K depending on scope, data readiness, and integrations, with a first working system in 8–12 weeks. We start with a fixed-scope discovery sprint that gives you a prioritized roadmap and ROI projection. Book a 45-min scoping call and we'll band it for your case.
