Custom AI agents for the work you actually do — content, SEO, market briefings, inbox, lead research, meetings — starting at $1,500.
Stop renting generic chatbots. We build production-ready AI agents tailored to one specific job in your business — your content pipeline, your inbox, your lead research, your daily briefing, your meeting notes — and ship the first agent live in under three weeks. Starter agents from $1,500.
Why a horizontal chatbot doesn't move the needle — and a specialised agent does
A generic ChatGPT seat helps any individual save twenty minutes a day. A purpose-built AI agent — one with the right tools, the right memory, the right knowledge base, and the right guardrails — replaces ten to twenty hours of weekly work for an entire team. The difference is engineering: tool-use scaffolding, RAG over your actual documents, persistent memory, retry-and-recovery logic, and the calls-to-real-APIs that turn a 'smart chat' into a worker. We build that worker. Eight battle-tested archetypes, shipped as productized starter agents, customised to your exact data and workflow. The first agent ships in three weeks; teams typically run three to five agents in production within a quarter.
Production agents, shipped on four continents.
Most of our custom-agent work is for international teams — US founders, UK ops leaders, UAE family offices, EU MedTech — building serious AI infrastructure that has to clear an enterprise bar from day one. We treat that as the default, not an upgrade.
Countries served
Featured agent work: Nimit AI (Global) · Texas Nurse Staffing (USA) · Texas SaaS (USA) · ToothLens.ai (USA + Europe)
Capabilities of this ai automation stack.
Content & Social Agent
Idea → AI-generated short-form video (NanoBanana / VEO3-style) → multi-platform publish to TikTok, Reels, YouTube Shorts, LinkedIn. Full pipeline including caption rewriting, scheduling, and tracking. Best for content creators and founder-led brands.
SEO Research Agent
Daily keyword-rank tracking across SERPs with AI-generated insights — competitor moves, ranking drops, content gaps, and recommended next actions. Reports straight into Slack or Notion. Best for SEO-led growth teams.
Daily Briefing Agent
Multi-source brief in your inbox at 7 AM — news, competitor moves, market data, open tasks, and overnight Slack/email signals — summarised by an LLM with risk-and-opportunity framing. Best for founders and execs.
Inbox Triage Agent
Reads your Gmail / Outlook in real time. Classifies every message (important, can-wait, automated, sales). Drafts replies for the routine ones. Surfaces only what genuinely needs your judgement. Saves 6–10 hours/week per user.
Lead Research Agent
Drop a company URL, get back a 1-page enriched profile — funding stage, hiring signals, tech stack, recent news, key contacts, fit score against your ICP. Wired into your CRM so SDRs and AEs stop spending an hour per account on manual research.
Meeting Note-Taker Agent
Joins your Google Meet / Zoom / Teams calls, transcribes, summarises, extracts action items, and posts a clean recap into the right Notion page or HubSpot deal — automatically. Best for client-services and sales teams.
Document QA Agent (RAG)
Point it at a folder of PDFs, Notion docs, contracts, or wikis — get a private chat surface that answers any question with cited passages. Works for SOPs, legal, finance, and customer-support knowledge bases. The most-requested archetype.
Sales Pipeline Updater
Listens to your call recordings and email threads, extracts deal stage changes, updates HubSpot/Pipedrive autonomously, and flags deals that look stuck. Eliminates the 'CRM is rotting' problem most sales teams quietly accept.
A predictable path from kickoff to live.
- 01
Week 1 — pick the agent, scope the workflow
We start with a tight 90-minute discovery: which task, which data, which tools. Output is a one-page agent spec — inputs, outputs, tool calls, success criteria — that we both sign before any code gets written.
- 02
Week 1–2 — build the brain + tool-use layer
Model selection (GPT-4o, Claude 3.5, or open-weights via Groq for cost-sensitive workloads), prompt + system message, function-calling layer, retry + safety guardrails. The reasoning core is in working state by end of week 2.
- 03
Week 2 — wire the knowledge (RAG) + integrations
Ingest your actual data — Notion, Google Drive, PDFs, CRM — into a Pinecone or Weaviate vector store with proper chunking and metadata. Connect the agent to the real APIs it needs (Slack, Gmail, HubSpot, Calendly, whatever the workflow requires).
- 04
Week 3 — calibration + handover
Run the agent against real workload for 5–7 days, side-by-side with the human it's replacing. Tune the prompt, expand the safety rules, harden the failure modes. Handover with a written runbook — how to retrain, how to update RAG, how to add new tools.
- 05
Ongoing — observability + iteration
Every agent we ship has a live observability dashboard — call counts, success rate, hallucination flags, cost-per-run. Optional retainer for monthly tuning, RAG refresh, and new-tool additions as your workflow evolves.
Outcomes we target on this work
The stack we use.
What this typically costs in India.
Starter Agent
$1,500 (one-time, 3-week build)
Single agent, one specific workflow, one data source, up to four tool integrations. Includes 4 weeks of post-launch support. Best fit: founders shipping their first agent.
Multi-Agent Build
$4,000–$6,500 (one-time, 6–8 week build)
2–3 agents that share memory and a RAG knowledge base. Includes a unified ops dashboard, 12 weeks of support, and weekly tuning calls during ramp. Best fit: ops-heavy teams replacing a real chunk of work.
Agent Fleet (Retainer)
$10,000+ build + $2,500–$5,000/mo
4+ agents in production, custom observability, dedicated tuning retainer, monthly RAG refresh, on-call response on agent failures. Best fit: $5M+ ARR companies running AI as a core operations layer.
We've actually shipped this.
Nimit AI
A study of 350+ B2B sales calls found 68% of lost deals come from unaddressed objections. Nimitai's AI GTM Brain fixed this — doubling conversion from 5% to 10% with zero ad spend.
Texas Nurse Staffing Agency
An AI agent replaced the manual recruiter workflow — screening 3,200 resumes in seconds, cutting 87% of screening time, and saving $6,800/month in payroll.
Texas SaaS Startup
AI agents became their always-on SDR team — zero salaries, zero PTO, zero burnout. 22 meetings booked in 21 days at 72% lower cost per meeting.
Questions, answered.
Can you build agents like the ones in n8n's template library?
Yes — n8n is in our default stack, and we've shipped agents very similar to the popular community templates (the NanoBanana + VEO3 viral video generator, the Bright Data + GPT keyword tracker, the TwelveData + Groq market briefing). The difference between a community template and a production agent is the engineering around it: error handling, RAG, memory, observability, and integration with your actual systems. That's what we charge for.
Which model do you build on — GPT, Claude, or open-source?
All three, depending on the workload. GPT-4o for the broadest tool-use and complex reasoning. Claude 3.5 Sonnet for long-document and writing-heavy tasks. Llama 3 via Groq for cost-sensitive, high-throughput workloads (e.g. classifying 50,000 emails a day). Model choice is part of the agent spec we write in week 1 — it's a deliberate engineering decision, not a religion.
What's the difference between an AI agent and a workflow automation?
A workflow automation runs a fixed sequence (when X happens, do Y, then Z). An AI agent decides what to do — picks the right tool, chains its own steps, recovers from errors, asks for help when blocked. We build both, and a properly engineered agent is usually a thin reasoning layer on top of dozens of small workflow automations under the hood.
Will my data be safe? Where is RAG hosted?
Customer data stays in your environment by default — Pinecone or Weaviate hosted in your AWS/GCP account, or self-hosted on a VM we provision. Embeddings can be generated through OpenAI or run locally on a private model if your industry (finance, health, legal) requires data residency. We sign NDAs by default and DPAs where regulation requires it.
What happens if the agent makes a mistake?
Three layers of defence. First, every agent has explicit guardrails — a list of actions it cannot take without human approval (sending external email, modifying records over a value threshold, etc.). Second, every action is logged with full reasoning trace; you can audit any decision after the fact. Third, the observability dashboard flags hallucinations and unusual behaviour patterns automatically. Mistakes will happen — what matters is that they're contained, visible, and learnable.
Can I start with one agent and add more later?
Yes — that's how most engagements grow. The Starter Agent is deliberately priced at $1,500 to make 'try one and see' a reasonable decision. Once that agent is in production and earning its keep, ~70% of clients add a second within 60 days, often using the same RAG knowledge base. The Multi-Agent and Fleet tiers are simply reformatted pricing for the path most clients organically walk.
Ready to ship a custom ai agents?
Book a free 30-minute call. We'll come back with a fixed-scope proposal within 24 hours.