AI that does the job, not AI that opens a chat window.
Sales agents, support agents, research agents, and workflow automations — built on real LLMs (Claude, GPT, Gemini) with custom tool use, retrieval, memory, and audit trails. Ship in weeks.
Why this matters now.
There are two AI industries in India right now. One sells chatbots — a search widget with a rebrand. The other sells real agents that plan, use tools, remember context, and take actions on behalf of the business. The gap between them is enormous, and most Indian mid-market clients don't yet know which one they're being sold.
We build the second. Our agentic engagements start with a very small, very specific job: 'reply to inbound leads with the correct 3-question qualification, hand off to a human in Slack when the lead reaches a score threshold, log everything to the CRM'. That is a real agent. It is also inexpensive to build, easy to trust, and impossible to confuse with a chatbot.
Our stack is Claude and GPT for reasoning, Cohere and OpenAI embeddings for retrieval, LangGraph and custom orchestration for control flow, and Postgres + Cloudflare for state and infrastructure. We are model-agnostic and honest about which model wins which task — Claude wins reasoning, GPT wins speed, Gemini wins long context. We test all three per project.
For every agent we ship, there is a human-in-the-loop kill-switch, an audit log, and a written policy for what the agent is NOT allowed to do. AI without governance is a lawsuit waiting to happen. We refuse the shortcut.
Deliverables, spelled out.
Sales-qualification agent
Handles inbound leads across email, WhatsApp, or web chat. Runs 3–5 qualification questions. Routes to human in Slack or handoff CRM.
Support agent (tier-0/1)
Answers first-line support from your real documentation and Zendesk history. Deflects when confident, escalates when not.
Research agent
Runs multi-step research tasks: competitor watch, deal-signal monitoring, industry news synthesis. Delivers to Slack digest or Notion database.
Workflow automations
Non-conversational agents that connect Gmail, Slack, Google Drive, Airtable, Notion, HubSpot. Real tool use, not Zapier zaps.
Custom RAG systems
Retrieval-augmented generation over your internal knowledge, product docs, sales playbooks. Hybrid search, re-ranking, quote attribution.
Governance layer
Audit logs, human-in-the-loop reviews, prompt injection defence, PII redaction, rate limits, kill-switches. Not optional.
The whole surface area.
Every sub-capability lives on the map. Click one and we'll build a working prototype in a week — we don't handwave scope.
4 deep specialisations. Each its own page.
Click any tile to open a full engagement brief — deliverables, process, KPIs, and honest FAQs.
From input to outcome.
Every agent is deployed with observability, kill-switch, and audit log from day one.
6-step engagement, no filler.
- 01Use-case auditTwo-week sprint: we identify 5–8 candidate jobs, score each on value × feasibility × safety. Pick one to ship first.
- 02Data & knowledge auditWhere does the truth live? Zendesk, Notion, Slack, Google Drive, HubSpot? We inventory, dedupe, and normalise before any retrieval work.
- 03Prompt & tool designWrite the agent's role, tools, guardrails. Build the tool graph in LangGraph or custom orchestration. Test cases before implementation.
- 04Retrieval layerHybrid search (dense + BM25), re-ranking, quote attribution, freshness signals. Evaluated on a real held-out set before production.
- 05Human-in-the-loop rolloutFirst two weeks: every agent output reviewed by a human before it acts. Learnings feed prompt + tool refinement.
- 06Production + governanceFull audit log, kill-switch, PII redaction, rate limits, weekly review of edge cases. Compliance-ready by design.
The numbers we ship.
What we won't ship.
- ✕We won't ship a 'chatbot' with a rebrand.
- ✕We won't skip human-in-the-loop for the first two weeks of any agent.
- ✕We won't build agents without audit logs. Governance is not optional.
- ✕We won't lock you into one model vendor. Model-agnostic by principle.
Questions smart clients ask.
It depends on the task. Claude 4.5 for reasoning-heavy agents, GPT-5.x for speed-sensitive interactions, Gemini 2.x for long-context research, open-weight models (Llama, Qwen, DeepSeek) for cost-sensitive or on-premise deployment. We test all three per project.
Yes. We integrate with HubSpot, Salesforce, Zendesk, Freshdesk, Slack, Zoho, Zapier, n8n, Notion, Google Workspace, Microsoft 365. We do not require ripping out anything.
PII redaction before any LLM call, DPA agreements with every model vendor, EU / India / US data-residency options, and an audit log that captures every input and output. Compliance is designed in, not bolted on.
First agent: ₹8–20L for build + first two months of governance. Subsequent agents on the same platform: ₹4–10L each. Ongoing platform retainer: ₹2–6L/month depending on volume and complexity.
Sometimes — usually the answer is 'you need better retrieval, not fine-tuning'. When fine-tuning is genuinely the right call (very narrow domain, high-volume classification), we do it on open-weight models with a small team.
Kick off with a 30-minute strategy call.
We diagnose live, on the call. No decks. If we're the wrong fit we'll say so — and point you to who isn't.