AI and Machine Learning Development Services from Models to Data Pipelines
Language models, agents, vision systems and the data platforms that feed them are separate disciplines, and a project usually needs two or three of them together. The thirteen services below are grouped so you can find the layer you are missing and see how it connects to the rest.
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Core AI
Products built around language models, from a single assistant to agents that act on your systems.
- Fixed-scope build, figure agreed first
AI Development
Custom AI work across apps, automation, chatbots and machine learning, sized for your use case and able to scale.
LLM Development
LLM apps including custom AI assistants, retrieval-augmented generation and model integration, built securely for enterprise use.
AI Agent Development
AI agents that automate workflows, customer support and business operations, custom engineered with full source ownership.
Generative AI
Generative AI apps with secure model integration, automation features and an architecture built to scale.
Chatbot Development
Chatbots using NLP, LLM integration and RAG pipelines, deployed across several channels, with source code included.
ML & Vision
Models trained or tuned on your data for prediction, images, video and text.
- Fixed-scope build, figure agreed first
Machine Learning
Custom machine learning models and predictive analytics inside intelligent applications, with ownership of the code.
Computer Vision
Image recognition, object detection and video analytics, built as custom vision systems for your use case.
NLP Development
NLP applications for text analytics, sentiment analysis, language processing and custom chatbots for business use.
TensorFlow Development
TensorFlow work using Keras, TFX, TF Serving and TensorFlow Lite for custom ML, NLP and vision applications.
Data
The pipelines, analysis and dashboards that any model depends on, plus measured automation of manual work.
- Fixed-scope build, figure agreed first
- Advisory review with a written answer
Data Science
Models and interactive dashboards that turn the data your business already holds into predictive insight you can act on.
Data Engineering
Data pipelines and analytics platforms, plus cloud data applications with real-time processing and secure engineering.
Data Analytics
Dashboards, reporting, data visualization and BI tools that turn your data into usable business insight.
Automation
Process automation that starts by timing real work, ranks candidates by hours returned and builds inside tools you already use.
Need AI & Intelligence work beyond these 13 pages? Name your stack on a call.
Scope a custom build →Which Kind of AI Work Fits Your Problem?
Most requests fall into one of four shapes, and the shape decides whether you need a model, a pipeline or neither.
Answering from Your Documents
If users need accurate answers from manuals, policies or a product catalog, a retrieval-augmented LLM app or chatbot is usually the right start. The model reads your content at query time instead of being retrained, so correcting an answer means correcting a document.
Taking Actions, Not Just Replying
An agent calls tools: it books, files, updates a CRM or opens a ticket. That needs permission limits, human approval on risky steps and a log of every action. Choose agent development when the value is in the doing, and begin with a narrow, reversible task.
Predicting or Recognizing
Forecasting demand, scoring risk, spotting defects in images or classifying text are classic machine learning, computer vision and NLP problems. They need labeled historical data more than a large language model, and a smaller trained model is often simpler to run in production.
Data That Is Not Ready Yet
If the data sits in spreadsheets and disconnected tools, begin with data engineering or analytics. Clean pipelines and a trusted dashboard frequently answer the question outright, and they are the foundation any later model will need anyway.
How AI Projects Are Delivered
Twelve services are scoped as fixed builds, and process automation begins as an advisory engagement that measures the work before anything is built.
Included on every engagement
- Custom AI builds run 2 to 8 weeks; anything larger, such as a multi-agent platform, is scoped and quoted in writing first.
- Model API usage and third-party licenses sit on accounts in your name and are billed to you, not bundled into the build.
- Prompts, pipelines and application code are handed over in full, unencrypted, and deployed to infrastructure you own.
- Every build carries three overlapping support terms: guidance on the platform over 60 days, priority bug fixing over 6 months and product evolution over 12 months.
- No revenue share on anything the AI product earns for you.
Questions About AI & Intelligence
How long does an AI chatbot or agent take to build?
A custom AI build runs 2 to 8 weeks. A retrieval chatbot over a defined set of documents is a smaller build than an agent that writes into several business systems with approvals and logging, and the written scope puts dates on whichever you choose. Anything beyond that range gets a written scope and quote before anyone begins, including the data preparation it depends on.
Where is the price for an LLM or computer vision project?
On the matching service page. Every AI service lists a figure on its own page, and that number is the one to rely on, so this overview quotes none. Model usage fees from providers are a separate, ongoing cost paid from your own account. For a project that combines services, such as data engineering plus an agent, a free written scope with one figure usually follows within a few working days.
Should we fine-tune a model or use retrieval?
Start with retrieval in most cases. Feeding the model your documents at query time keeps answers current, lets you cite sources and needs no training run. Fine-tuning helps when you need a consistent style or format, a narrow task done cheaply at high volume, or behavior that prompts alone cannot hold. Many production systems end up combining the two.
Who pays for model API usage?
You do, directly. Third-party API costs and licenses are not part of the build, so model calls, vector database plans and similar services are billed to accounts in your name. That keeps pricing transparent and lets you switch providers without asking us. Nothing about those accounts has to be transferred when the build is handed over.
Does our data stay under our control?
The system is deployed to infrastructure you own and the model provider accounts are yours, so your data goes only where your own accounts and contracts allow. You receive the full source, including prompts and pipelines, at handover. If you want an independent view first, an architecture or security review is available as advisory work before anything is built.
Bring Us the Process or Question You Want AI to Handle
Describe the task and the data behind it, and you will have a first reply in under 2 hours for anything sent from Monday to Saturday inside our 10:00 to 19:00 IST window.
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