AI development and LLM integration
AI features that work on Monday morning, not just in the demo
Most AI prototypes impress in a meeting and fall apart on real data. We build AI features with retrieval, guardrails and evaluation from the start, so you can measure whether they work before your customers find out.
An AI development company designs and builds software that uses machine learning or large language models (LLMs), such as chat assistants, document search, classification, extraction and workflow automation, and integrates it into existing products and systems.
TechNimbus builds on OpenAI, Anthropic and open-source models, uses retrieval augmented generation (RAG) to ground answers in your own data, and adds evaluation so quality is measured rather than guessed.
What we build
AI work that earns its place
Assistants over your documents
Chat and search that answer from your policies, manuals and tickets, with citations back to the source.
Document processing
Extract fields from invoices, forms, contracts and CVs into structured data, with confidence scores and human review.
AI agents with limits
Agents that call your APIs to complete defined tasks, with permissions, logs and a human in the loop where it matters.
Classification and routing
Sort tickets, leads and messages automatically, and send them to the right person or system.
AI inside your product
Summaries, drafting, recommendations and smart search added to an existing web or mobile app.
Evaluation and monitoring
Test sets, scoring and dashboards that show whether a change made the AI better or worse.
How we approach it
Start with the job, not the model
The most common AI failure is building something clever for a problem nobody had.
We begin every AI project by picking one job that costs real time or money today: answering the same support questions, keying data from documents, triaging inbound requests. Then we measure how it is done now, so there is a baseline to beat.
From there the build follows a pattern that holds up in production:
- Data first. Clean, chunk and index the documents or records the AI will rely on, with access controls carried through.
- Retrieval before generation. Ground answers in your own data with RAG, and show the sources.
- Guardrails. Limit what the system may say and do, handle refusals properly and log every action.
- Evaluation. Build a test set from real questions and score every change against it.
- Cost control. Pick the smallest model that passes the evaluation, cache where possible and watch token spend.
We work with OpenAI and Anthropic models, open-weight models where data must stay in your infrastructure, LangChain and similar frameworks, and vector databases such as pgvector and managed alternatives.
Market context
What AI engineers cost in India
AI and ML engineers are the most expensive developer profile in India, and demand is still accelerating.
| Role / stack | Junior (0–2 yrs) | Mid (2–5 yrs) | Senior (5–8 yrs) | Lead (8+ yrs) | Dedicated senior, monthly |
|---|---|---|---|---|---|
| AI / ML engineer (PyTorch, LLMs, RAG) | $22–35 | $40–65 | $60–95 | $85–120+ | $9,600–15,200 |
| Data engineering (Snowflake, Spark, dbt) | $20–32 | $34–56 | $50–80 | $70–100 | $8,000–12,800 |
| Backend (Node.js, Python, Java, .NET) | $16–28 | $28–48 | $45–70 | $65–90 | $7,200–11,200 |
CompanyBench reports AI, ML and GenAI job postings in India up 68% year on year in 2026, the fastest-growing category for the second year running. Sources: Jellyfish Technologies, CompanyBench Research.
What is RAG and why does it matter?
Retrieval augmented generation (RAG) means the system first searches your own documents or data for relevant passages, then asks the language model to answer using only those passages. It reduces made-up answers, keeps responses current without retraining a model, and lets you show users the sources behind each answer.
Which AI models do you use?
We choose per project. OpenAI and Anthropic models for general language tasks, open-weight models when data must stay inside your own infrastructure, and smaller specialised models when cost or speed matters more. The choice is driven by evaluation results rather than preference.
Is our data safe when using AI APIs?
We design for it. That includes using API tiers that do not train on your data, removing personal data before it is sent where possible, keeping access controls on retrieved documents, and hosting models yourself when the data cannot leave your environment. We also design for India's DPDP Act 2023 and GDPR where they apply.
How long does an AI pilot take?
It depends on the data and the integration, not the model. A focused pilot on one task with clean data is far quicker than one that needs data cleaning and several integrations first. We give you a realistic plan after discovery.
Can you add AI to our existing product?
Yes. Most of our AI work is adding features to software that already exists: search, summaries, assistants and automation, connected to your current database, APIs and authentication.
Can we hire an AI engineer instead of a project?
Yes. If you have the product direction and need the engineering capacity, you can hire an AI or ML engineer as a dedicated or contract developer. See our page on hiring AI and ML engineers.
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