Is my customer data safe?

Yes when set up right. Anthropic and OpenAI offer zero data-retention modes on API calls. Open-source models can run fully on your own server with no external API. Vector DBs (Pinecone, Qdrant, Weaviate) can be self-hosted. PII redaction happens in the retrieval pipeline before anything hits the model. Every project starts with a data-flow document so you know exactly what leaves your stack.

Operate Pillar · AI Agent Development

Autonomous agents that do work, not chatbots that answer questions.

I build Python agents that qualify leads, run tier-1 support, draft proposals, and orchestrate your ops tools, with retrieval grounding, eval harnesses, and human handover where it matters. I built ARIA, my own autonomous Python agent, before I sold this to anyone. The work is hands-on, model-agnostic, and grounded in your data, not its training memory.

Send me the repetitive task→ See agents in production

ARIA

My own Python autonomous agent

5-7 tools

Wired per agent on average

50-100

Eval cases scored each release

60-70%

Tier-1 deflection on a good build

[ 01 / What is included ]

What a real agent project actually ships.

The flashy demo is easy. The production agent that survives 90 days is harder. Here is what the build covers.

Use-case scoping

What can be safely autonomous (looking up policies, drafting replies, scheduling), what stays human (refunds, escalations, anything legal), and what is in between with confidence thresholds. Scoped on paper before any code.

Knowledge base ingest

Your SOPs, product docs, pricing sheets, returns logic, internal wikis loaded into a vector store (Qdrant, Pinecone, or self-hosted). Retrieval grounded so the agent quotes you, not its training memory.

Tool wiring

The agent's hands. CRM read and write (HubSpot, Zoho), calendar booking, email send, n8n triggers, WhatsApp send, Slack alerts, internal API calls. Typically 5-7 tools per agent.

Guardrails + handover

Confidence scoring on every response, escalation paths for low confidence, hard rules for never-do actions, full audit log of every action taken. The list of things the agent must escalate is written, not assumed.

Eval harness

50-100 real customer interactions captured from your existing channels, scored each release for accuracy, helpfulness, and safety. Regressions caught before deploy, not after a customer complains.

Human-in-the-loop UI

A simple inbox where your team reviews agent actions on a sample, overrides bad answers, and flags new training examples. Closes the loop so the agent gets better in production, not worse.

[ 02 / Process ]

How an agent build avoids the demo trap.

01 · SCOPE

Pick one job, write it down

Agents fail when the scope is "be helpful". They ship when it is "qualify inbound real-estate leads, ask 5 questions, push qualified ones to HubSpot". One job, written, signed off.

02 · BUILD KB + TOOLS

The agent's brain and hands

Knowledge base ingested with chunking that survives retrieval. Tool APIs wired with auth, rate limits, retries. The agent is only as good as the tools and KB it can reach.

03 · WRITE EVALS

Real cases before any deploy

50-100 customer interactions pulled from your channels. Each scored by a human for what the right answer would have been. Eval set is the ground truth, not the prompt.

04 · SHIP + MONITOR

Live with observability

Deployed behind a feature flag, started at 10% of traffic, logs and confidence scores on every action, escalation rate watched daily for the first month.

[ 03 / Comparison ]

Agent vs. chatbot vs. human team.

What matters	AI agent	Chatbot (rule-based)	Human team
Takes multi-step action	Yes - calls tools, updates CRM, books slots	Replies only	Yes
Memory across the conversation	Full context	None or scripted	Yes
Handles ambiguity in the question	Reasonable on grounded data	Falls off the decision tree	Strong
Volume capacity	1,000-10,000/day at low marginal cost	High but shallow	Headcount-limited
Cost per interaction	Rs. 0.50-3	Pennies	Rs. 30-150
Time to deploy	4-6 weeks	1-2 weeks	Months to hire and train

[ 04 / Proof ]

Foundations that an agent layer sits on top of.

Agents need a CRM, a knowledge base, and clean tool APIs to be worth anything. Three projects where the foundation is laid and the agent layer is the natural next step.

See all 18 case studies→

[ 05 / Engagement ]

Three ways to get an agent live.

▽

Use-case audit

1-2 weeks

Your current support / lead / ops workflow mapped. 3-5 candidate agent use cases ranked by feasibility and ROI. Model and tool stack recommended. Monthly run cost projected. Written plan you can execute yourself or hand to your engineering team.

◇

Build sprint

5-7 weeks

One agent end to end: scope locked, KB ingested, 5-7 tools wired, eval harness with 50-100 cases, guardrails and handover paths, human-in-the-loop UI, deployed behind a feature flag at 10% traffic.

◈

Retainer

ongoing

Eval set grown weekly with new edge cases. Prompt and retrieval tuning. Model upgrades when better ones ship (and they ship monthly now). New tool wiring as your stack evolves. Friday WhatsApp report on agent accuracy, escalation rate, and cost per resolution.

[ 06 / FAQ ]

The questions every founder asks before paying for an agent.

A chatbot answers questions. An agent takes actions across multiple steps. An agent qualifies a lead, looks up your CRM, books a calendar slot, emails the customer, and updates the deal stage - autonomously, with memory across the conversation, calling real tools. A chatbot can only reply in one screen with no memory.

I pick per use case. Claude (Anthropic) for reasoning-heavy agents and tool use because of better instruction following. GPT-4 or GPT-4o for high-volume support. Open-source models like Llama 3 or Mistral on Together AI or Groq when cost or data sovereignty matters. I show the trade-off in writing before locking in.

Three layers. One: retrieval grounding so the agent quotes your knowledge base, not its imagination. Two: an eval harness of 50-100 real interactions scored each release, so regressions get caught before deploy. Three: confidence thresholds and human-in-the-loop on high-stakes actions like refunds, contract changes, or large transactions. Hallucination drops sharply when you stop letting the agent answer from training memory.

Two components. Inference cost from the model provider: roughly Rs. 0.50 to Rs. 3 per agent interaction depending on model and context size, so a tier-1 support agent handling 3,000 tickets a month costs Rs. 1,500-9,000 in API fees. Plus hosting, observability tools, and a vector DB if you have one. Total monthly run cost lands Rs. 5,000-25,000 for most SME use cases. I show the projection before we build.

Yes when set up right. Anthropic and OpenAI offer zero data-retention modes on API calls. Open-source models can run fully on your own server with no external API call. Vector DBs like Pinecone, Qdrant, and Weaviate can be self-hosted. PII redaction happens in the retrieval pipeline before anything hits the model. Every project starts with a data-flow document so you know exactly what leaves your stack.

Usually not. It deflects the repetitive 60-70% of tier-1 questions so your team handles the harder 30-40% better. Pure replacement projects fail; augmentation projects ship and stick. The eval set decides which questions stay agent-only and which always escalate.

[ 07 / Related ]

What pairs with an agent.

Related services

Industries where agents pay back fastest

All Services→

Describe one repetitive task only your senior team can do today.

Send me the task, the tools involved, and how many times it happens a day. I will reply within 24 hours with whether an agent is the right fit, what the agent looks like (model, tools, KB shape, eval cases), and a rough monthly run cost. No sales call needed.

Send me the brief→ WhatsApp me directly