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AI & Intelligent Automation

Cut repetitive work and sharpen decisions with AI and automation engineered into your operations — not bolted on.

[ OVERVIEW ]

THE APPROACH

Based in Pune, India, we build AI into the systems your team already runs on — retrieval over your own documents, automation across the tools you use, and models kept on a leash by guardrails you can see. The goal is fewer manual hours and faster, better-evidenced decisions, not a demo.

Every automation ships with a human-in-the-loop path, monitoring, and a clear owner. You keep control of the model, the prompts and the data — and the code. Pair it with custom software or cloud infrastructure and we can own the full stack.

[ PIPELINE ]

5 STAGES
01Discovery & process auditMap where time is lost and where AI actually pays off.
02Automation architectureDesign the pipelines, triggers and fallbacks end to end.
03LLM & RAG integrationRetrieval over your data, grounded and citation-backed.
04Human-in-the-loop controlsReview gates and overrides on every automated action.
05Monitoring & guardrailsDrift, cost and quality watched in production.

[ STACK ]

TOOLING
PythonLLMsRAGLangChainVector DBsAutomation

[ EXAMPLE_OUTCOME ]

ILLUSTRATIVE
−80%
MANUAL TRIAGE / 90 DAYS

A support team drowning in 500 repeat questions a week wired an assistant to its own docs — answers now draft themselves, humans just approve.

ILLUSTRATIVE ENGAGEMENT

Frequently Asked Questions

What is a RAG pipeline and why does it matter?

Retrieval-augmented generation grounds a model’s answers in your own documents instead of its training data, so responses cite real sources and stay current as your content changes.

How do you keep an LLM from making things up?

Grounded retrieval, citation-backed answers, output validation and scoped prompts — combined with monitoring for drift, cost and quality in production.

What is human-in-the-loop and do we need it?

Every automation ships with a review gate and override path — a person approves or corrects the action before it’s final. It’s standard on anything customer-facing or irreversible.

How is ROI measured on an automation project?

We baseline manual hours and error rates in discovery, then track the same metrics post-launch — most engagements target a measurable cut in manual triage within 90 days.

How is our data kept private?

You keep control of the model, prompts and data. Retrieval runs over your own infrastructure or a scoped vector store — nothing is used to train third-party models without consent.