AiGanakMulti-agent workflows that run your operations, not just chat.
Autonomous n8n and Make workflows that link your database layers with stateful LLM routing, so complex processes run end to end with guardrails and a cost meter on every step.
AiGanak builds n8n and Make multi-agent workflows that connect databases, CRMs, ERPs and messaging to language models with stateful routing, guardrails and cost tracking.
Problems this solves
Manual handoffs between tools
Data is copied between CRM, ERP, email and spreadsheets by people, with errors and delays.
Chatbots that can't act
Your assistant answers questions but can't update a record, open a ticket or trigger a workflow.
Prototype agents that break
An agent demo works, then fails on real data, loops, or runs up the token bill.
Deliverables
- Process map with each agent's role, tools and limits
- Production n8n or Make scenarios connected to your systems
- Stateful LLM routing with fallbacks and human hand-off
- Guardrails and structured outputs on every model call
- Cost per task and failure dashboards
- Runbook and handover to your team
Engagement steps
Map
We map the process, data sources and decisions, and price the first workflow.
Build
Agents are built in n8n or Make, extending what you already run where possible.
Harden
Guardrails, retries, step caps and evaluation tests before go-live.
Run
Monitoring and monthly tuning of cost and quality.
Often paired with
Prompt hardening and LLM guardrails
AiGanak hardens LLM integrations against prompt injection, format failures and token waste using XML-structured prompts, negative constraints, input isolation and CI validation, backed by Prompt Lab diagnostics.
Learn more →LLM Cost & Reliability Audit
The LLM Cost & Reliability Audit measures your OpenAI, Anthropic, Azure, Bedrock or Gemini usage from your own logs, prices every saving (model routing, caching, prompt trimming, retrieval, batch) against your approved answers, and ranks reliability risks. A free Quick Check gives an instant estimate.
Learn more →Private and on-premise LLM deployment
AiGanak deploys and optimises open-weight language models in a client's own cloud account or on-premise, including offline setups, so enterprise data never reaches public AI APIs.
Learn more →Questions people ask
Can't find it? Press ⌘K and ask.
What is a multi-agent workflow?
A multi-agent workflow splits a business process into steps handled by separate AI agents, each with its own tools and instructions, coordinated by an orchestrator. In n8n or Make, each agent becomes a node that reads from and writes to your systems.
Which systems can the agents connect to?
Anything n8n or Make can reach: databases, CRMs, ERPs, email, WhatsApp and internal APIs, plus custom middleware where a connector does not exist.
Tell us what you're building.
AiGanak Agent drafts a scoped proposal in a few minutes. An engineer reviews it with you.