Services / 01

Hire an AI agent developer who ships production agentic systems.

Custom AI agents, multi-agent orchestration and agentic automation for startups and SMBs. Claude, GPT-4, LangChain, MCP. Voice and video interfaces. Real production systems — PatFace, Iklavya — not demos.

§ 01

What you get

Custom AI agents

Single-purpose and multi-purpose agents with planners, critics, tool use, memory and schema-validated outputs. Built to replace repetitive decision work, not to impress in a demo.

Multi-agent orchestration

Coordinator + specialist pattern — a planner routes tasks to focused workers, a critic validates. Shipped at scale in PatFace (patent drafting) and Iklavya (AI interviewer).

Voice & video agents

Realtime agents with ElevenLabs TTS, HeyGen/Simli avatars, streaming ASR. Lip-synced, interruptible, fast enough for live interviews and customer support.

MCP tool integration

Model Context Protocol servers so Claude and other agents call your tools safely and deterministically. Typed schemas, auditable tool calls, no hallucinated parameters.

Guardrails & cost control

Token budgets, retry bounds, output schemas, critic agents, and replay logs. The system fails small and fails visibly.

Deployment & operation

AWS / GCP / Vercel / Railway deployment. Monitoring with CloudWatch or similar. Ongoing retainer available for iteration and oncall.

§ 02

How I build it

  1. 01
    Discovery
    Thirty-minute scoping call to identify the task, success metrics, data sources and integration surface. Output: a one-pager with scope, cost and timeline.
  2. 02
    Prototype
    A working agent against your real data within a week. Shown to stakeholders for feedback before any hardening work.
  3. 03
    Hardening
    Schemas, retries, token caps, critic agents, tests. The agent behaves predictably on edge cases and bad inputs.
  4. 04
    Deployment
    Production environment, monitoring, cost dashboards, runbooks. Handover docs for your team.
  5. 05
    Operation
    Optional monthly retainer: new features, prompt tuning, model upgrades as the frontier moves, oncall for production issues.
§ 03

Stack used

LLMs

Anthropic Claude (Opus, Sonnet, Haiku) · OpenAI GPT-4 / GPT-4o · Google Gemini · Ollama · Mistral

Agent frameworks

LangChain · LangGraph · CrewAI · Haystack · custom orchestrators (TS/Python)

Retrieval & memory

Pinecone · ChromaDB · Weaviate · Qdrant · pgvector · Redis

Voice & video

ElevenLabs · HeyGen · Simli · Deepgram · Remotion

Tool protocol

MCP (Model Context Protocol) · OpenAI tool calling · Anthropic tool use · custom REST adapters

Deploy

AWS (EC2, ECS, Lambda) · GCP · Vercel · Railway · Docker · GitHub Actions

§ 05

Engagements

Prototype

$2,500 – $6,000

Single-purpose agent with one or two tool integrations, deployed for internal use. 1–2 weeks.

Production

$8,000 – $20,000

Hardened agent with guardrails, monitoring, CI/CD, handover docs. 3–5 weeks.

Platform

$25,000+

Multi-agent system with RAG, voice/video, admin, billing, multi-tenant. 6–12 weeks. PatFace / Iklavya scale.

Fixed-price or monthly retainer. NDA and IP-assignment standard. Hourly available on request.

§ 06

Frequently asked

What is AI agent development?

AI agent development is the practice of building autonomous software agents that use large language models (LLMs like Claude and GPT-4) to plan, reason and take actions — calling tools, reading data, writing to systems, and coordinating with other agents. A production AI agent is not a chatbot; it is a bounded system with a goal, a memory, a tool-belt and a critic.

Who should hire an AI agent developer?

Startups building AI-powered products, SMBs replacing manual workflows, and enterprises integrating LLMs into internal operations. If your business has a repetitive task that involves reading documents, making decisions, and taking actions — an AI agent can probably do it. Typical engagements range from $3,000 MVPs to $30,000 multi-agent platforms.

How long does it take to build a custom AI agent?

A single-purpose agent MVP with one tool integration ships in 1–2 weeks. A multi-agent system with RAG, voice, and production guardrails typically takes 4–8 weeks. PatFace (agentic patent drafting) and Iklavya (agentic interviewer) are examples of the larger end.

What frameworks do you use for AI agents?

LangChain, LangGraph, CrewAI, and custom orchestration in TypeScript/Python. For tool use I prefer the Model Context Protocol (MCP) where applicable because it keeps the agent’s interface explicit. LLM providers: Anthropic Claude (Opus, Sonnet), OpenAI GPT-4, Google Gemini.

How do you prevent AI agent hallucinations and runaway costs?

Every agent has a schema-enforced output (JSON), a token budget per task, a retry bound, and a critic agent that validates before acting. Costs are capped per session. Tool calls are logged and replayable.

Do you build voice and video AI agents?

Yes. I’ve shipped voice and video agents using ElevenLabs (TTS), HeyGen and Simli (avatars), and streaming ASR for real-time conversation. See the Iklavya case study.

Can you integrate an AI agent with our existing systems?

Yes. Typical integrations include CRMs (HubSpot, Salesforce), databases (Postgres, MongoDB, MySQL), SaaS APIs (Slack, Notion, Linear, Zendesk), payment gateways, and custom internal tools via REST or MCP.

What does a typical AI agent development engagement look like?

Week 1: discovery call, scope, success metrics. Week 2: prototype with real data. Week 3–4: production hardening with guardrails, tests, cost monitoring. Week 5+: deployment and handover. Monthly retainer optional for ongoing development.

Ready to build?

Same-week start. Email reply within 24 hours. Written enquiries welcome.