Our natural language processing services turn your support tickets, documents, emails or chat into features that classify, extract, search or summarise inside your own product or workflow. One India-based team builds to a written discovery scope, with a review each sprint. Send a short brief and we'll tell you what fits.
We start with the text you already hold, such as support tickets, contracts, emails or chat logs, and the decision you want it to support. Together we agree what the feature should classify, extract, search or summarise, and which languages it must handle. If a rules-based or off-the-shelf tool would serve you better, we will say so after scoping.
Our cross-functional team builds the NLP pipeline and connects it to your product or workflow through APIs. A technical architect is accountable for system design, technology selection and code quality, so the approach is chosen for your requirements rather than for novelty. You see the working result at each sprint review.
Before the build begins, we write down how the feature will be judged: the sample texts, the accuracy you expect and the edge cases that matter to you. Our QA work tests against those criteria. At release you receive documentation and deployment access, and IP assignment is already confirmed in the contract.
We work with you on the language task, such as classifying, extracting, searching or summarising text, and on the data sources, languages and accuracy you need. You review the written scope and the open questions before anything is built.
You receiveYou agree the written discovery scope
We map how the NLP feature sits inside your product or workflow, from data intake to the output your users see. You review the approach and the architecture, including how model quality will be measured and how privacy is handled.
You receiveYou review the design before the build starts
We build the NLP pipeline and connect it to your systems in Agile sprints. You test working builds and see results on your own text samples as the work progresses.
You receiveYou review at the end of every sprint
We test the feature against the acceptance criteria agreed in discovery, including accuracy on your own text and how it handles unusual inputs. You review the QA results and the open issues before release.
You receiveYou sign off the results against the agreed acceptance criteria
Netofficials deploys the NLP feature into your product or workflow and prepares the documentation and deployment access your team needs to run it. You review the handover pack and confirm your team can operate the feature.
You receiveYou confirm the handover of documentation and deployment access
Send a short brief on your text data and the outcome you want, and we reply with questions, a written discovery scope and the team we would assign.
Your text sources set the scope: support tickets, contracts, emails, chat or call transcripts, the languages involved and how clean the data is. We agree what a correct output looks like for your team, such as the right label, the right extracted field or a usable summary, before design starts. That keeps accuracy expectations written down instead of assumed.
Your NLP feature has to work inside the systems your team already uses, such as a helpdesk, CRM, document store or internal API. We map where text comes from and where results go, and what personal or confidential data is involved. If you need a mutual NDA before sharing samples, we put it in place first. Each of these points affects cost, so we settle them before estimating.
Some problems need a language model, and others are served better by rules-based matching, search or an off-the-shelf service. If a simpler approach suits you, we will say so after scoping. We also plan testing, deployment, documentation and handover with you, so you know how the feature will be reviewed and supported after release.
A fixed-price engagement for one clearly bounded language task, such as classifying support tickets or extracting fields from documents, where you want the scope settled before the build.
An India-based team that keeps improving your NLP features over time, suited to products where models, languages and data sources will keep changing.
Context-only field; not returned.
Before the build starts, your written discovery scope names the NLP approach for your language, volume and accuracy target. That might be a pretrained language model, a fine-tuned classifier or a retrieval pipeline. You also see how each choice affects cost, privacy and upkeep. Our technical architect is accountable for technology selection and code quality, so you aren't handed a fixed stack. If a simpler method would serve your text better, we recommend it after scoping.
React
Node.js
Python
PostgreSQLCost and timeline follow the scope, not a fixed rate card. The main drivers are your text sources (tickets, documents, emails or chat), the languages involved, the accuracy the task needs, how many systems the output must feed, and any privacy limits on the data. A written discovery scope sets these out, with an initial estimate and open questions, before the build starts. We then agree review milestones and commercial terms, and you can choose fixed-price project delivery, a dedicated team or staff augmentation.
You do. Netofficials confirms IP assignment in the contract before the build begins, so ownership of the NLP code is settled up front rather than at handover. At release we hand over documentation and deployment access, so your team can run and extend the work. If sensitive text is involved, we can sign a mutual NDA before you share details.
Yes. If a rules-based approach, a search upgrade or an off-the-shelf tool would solve your text problem more simply, Netofficials will recommend it after scoping. A named delivery lead manages progress and communication, and a technical architect is accountable for design and technology choices. You see working results at a client-facing review at the end of every sprint. Send a short brief and we will reply with questions and a proposed scope.
Tell us what text you hold, such as support tickets, documents or emails, and what you want it to do. We reply with questions and a proposed scope, and we say so after scoping if a rules-based or off-the-shelf approach would serve you better. You will know the scope, the team and the commercial terms before any build starts.
