Our deep learning development services turn your idea for image, text, speech or forecasting models into a working capability inside your product or operations. One cross-functional team, led by a named delivery lead, works from a written discovery scope with a review at every sprint. Send a short brief to start.
We start by checking whether deep learning, such as neural networks for images, text, speech or forecasting, suits your problem and your data. The written discovery scope sets out the use case, data sources, labelling needs and success measures. If a simpler machine learning approach would serve you better, we will recommend it after scoping.
Our technical architect is accountable for system design, technology selection and code quality standards. You review the model approach, the data pipeline and how the model will connect to your product before the build starts. That keeps data quality, integration and deployment questions out of the later sprints, where they cost the most to fix.
One cross-functional team covers back-end, QA and DevOps, so the model you approve is built, tested and deployed as part of your product. A named delivery lead owns day-to-day progress, and you see a review at the end of every sprint. At release you receive documentation and deployment access.
We turn your business problem into a written discovery scope. It covers the prediction or classification task, the data you hold, the integrations involved and whether deep learning or a simpler machine learning method fits better.
You receiveYou agree the written discovery scope
We define the model approach, data pipeline and system architecture, led by the technical architect. You review how the model will connect to your product and what it needs for training and deployment.
You receiveYou review the architecture before the build starts
We prepare the data, train and tune the models, and build the supporting services in structured sprints. You see working increments rather than waiting for one final delivery.
You receiveYou review at the end of every sprint
We test the model and the surrounding software against the acceptance criteria you agreed at the start. You check the results, the known limits and the open issues before anything goes live.
You receiveYou accept the model against the agreed criteria
We deploy the validated model into your environment and prepare the documentation and deployment access your team needs to run it. You confirm the release plan, including who monitors the model and who retrains it.
You receiveYou confirm handover before sign-off
Send a short brief on the problem, your data and the systems involved, and we reply with questions, a proposed written discovery scope and the team we would assign.
Training data is usually the biggest cost driver. We assess what you already hold, how much needs labelling and whether quality gaps will limit the model. If simpler machine learning would solve the problem with less data, we recommend it after scoping.
Model complexity and the systems around it shape effort. Image, text, speech and forecasting models each need different training work. We also map the APIs, databases and applications the model must connect to, so the written scope shows these dependencies before an estimate.
A model only pays off once it runs reliably in production. We agree where it will be hosted, what inference speed it needs and who monitors it. At release you receive documentation and deployment access, so your team can run and extend the work.
Fixed-price delivery for a defined deep learning use case, such as image recognition or text classification, once the written discovery scope is agreed.
A dedicated India-based team that builds, deploys and improves your neural-network models across successive releases, suited to a roadmap rather than one feature.
Engineers who join your team to extend or productionise models you already have, working inside your data, tools and review process.
Your technical architect selects the deep learning framework, such as PyTorch or TensorFlow, and the serving stack, and records the choice in the written discovery scope. The choice rests on your data type, where the model must run and who will maintain it. You see the reasoning before the build starts, so there is no rework from a stack that doesn't fit.
React
Node.js
Python
PostgreSQLNetofficials confirms this during written discovery scoping, before any build is agreed. Deep learning (neural networks) suits image, text, speech and complex forecasting problems where there is enough relevant data to learn from. If a simpler machine learning method or a rules-based approach would serve you better, we will recommend it after scoping. Send a short brief describing the problem and the data you hold, and we will start there.
Cost and timing are driven by data quality and volume, how much labelling is needed, model complexity, the systems the model must integrate with, and where it will be deployed. We set out the scope, assumptions, review milestones and commercial terms in writing before development begins. You can choose fixed-price project delivery, a dedicated India-based team or staff augmentation, and we will explain which fits your project once the scope is clear.
You own what we build for you: IP assignment is confirmed in the contract before the build begins, and documentation and deployment access are handed over at release. A named delivery lead owns day-to-day progress and your communication, and a technical architect is accountable for system design and code quality. We agree the communication window, meeting schedule and update cadence with you upfront, and you review the work at the end of every sprint.
Send a short brief describing the image, text, speech or forecasting problem you want to solve. We review your data and systems, tell you honestly whether deep learning or a simpler machine learning approach fits, and return a written discovery scope. Your named delivery lead then agrees the communication window and update cadence with you before work begins.
