Artificial Intelligence & Machine Learning
Generative AI, Large Language Models and predictive analytics to build intelligent agents, copilots and decision-support systems.
What we cover
Generative AI
Generative AI is useful when the output is text, summaries, drafts, answers or structured data that people used to produce by hand. We pick the model for the job, write and test the prompts, and add guardrails such as output schemas, fact checks against your data and human approval for anything risky. Cost and latency matter as much as quality, so we measure both on real samples before anything reaches users. The result is a feature your team trusts, not a demo.
LLM Integration
Connecting a large language model to a business system is mostly engineering around the model: authentication, retries, rate limits, token budgets and logging. We design prompts as versioned code, force structured JSON output so downstream systems never parse free text, and keep your data out of model training. We also build a fallback path so a provider outage or a bad answer never stops an operation. We have run this in production for scoring and Q&A over live business data.
AI Agents
An agent is an LLM that can decide what to do next and call tools such as searching a database, sending an email or updating a record. The craft is in limiting it: a clear goal, a short list of allowed tools, step limits, and an audit trail of every decision. We start narrow with one workflow done well, add human approval at the steps that carry risk, and widen the agent's authority only when its logs show it deserves that.
RAG
Retrieval-Augmented Generation lets a model answer from your documents instead of guessing. We split content into meaningful chunks, store it in a vector index with metadata, retrieve the best passages for each question and make the model cite them. Most RAG failures come from poor chunking, stale data and missing access control, so we handle ingestion, refresh schedules and per-user permissions as seriously as the model. Answers that cannot be backed by a source should say so.
Predictive Analytics
Predictive models turn history into a forecast: demand next month, which customers may leave, which invoices will be paid late. We begin with the decision the prediction will change, check that the data can support it, and set a simple baseline first so we know the model adds value. We validate on data the model has not seen, explain the main drivers in plain language, and monitor accuracy after launch because business patterns drift.
NLP
Natural language processing covers reading, classifying and extracting meaning from text: emails, tickets, reviews, CVs, contracts. We combine modern language models with classic techniques, using the cheaper approach where it is accurate enough. Typical work includes intent detection, entity extraction, sentiment, summarisation and multilingual handling, including Hindi and English mixed text. Every pipeline is tested against labelled samples from your own data and gets a measured accuracy figure.
Computer Vision
Computer vision lets software read images and video: reading text from documents and labels, checking product or shelf images, counting items, spotting defects. We choose between ready-made vision APIs and a custom-trained model based on accuracy needs and volume. The hard parts are usually lighting, camera angles and messy real-world images, so we collect representative samples early, measure error rates honestly, and design a review step for low-confidence results.
From idea to production
Discover
We map your process, data and goals and agree what success looks like.
Design
We design the solution, integrations and the way people will use it.
Build
We build and test in short cycles so you see working software early.
Run
We deploy, monitor and keep improving it after go-live.
Interested in Artificial Intelligence & Machine Learning?
Call +91 93153 90840 or write to info@intellivane.com.
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