AiGanakPrivate AI systems, engineered to cost less.
We bridge language model research with production systems engineering. Multi-agent workflows, hardened prompts and private model deployments that scale without token bloat, plus an audit that shows exactly where your LLM bill goes.
# sample run on fictional data $ aiganak audit --client orvella monthly spend $14,001 changes found 9 · risks 4 saving $5,851–$7,377 / mo (42–53%) $ prompt-lab scan support_persona.xml alignment score 25% → rewrite proposed steerability 4/10 → 9/10 $ slm status aiganak-slm-1b params 1B · context 131,072 tokens phase 2 convergence · not released
One team from the workflow layer down to the model.
Most AI vendors resell an API. We engineer the system around it and, where it pays, replace it with a smaller model you control.
See what your OpenAI or Anthropic bill is really paying for.
We measure usage from your own logs, test cheaper setups on your own answers and price every change. Free Quick Check, full audit in five to seven working days.
Run the free Quick Check → SystemsMulti-agent workflows
n8n and Make agents wired into your databases, CRM and messaging.
SecurityPrompt hardening
Injection defence, structured outputs and CI tests for every prompt.
InfrastructurePrivate deployment
Open models in your cloud or on-premise. Data stays inside.
R&DCustom small language models
Domain models delivered as proof of concept, MVP, then production, built on the pipeline behind AiGanak-SLM-1B.
Tools you can use today
- Prompt Lab: structural audit for failing system prompts
- Model Lab: benchmark models on your own examples
- Hiboto: no-code chatbot builder
- Registry: early access to sandbox model releases
Measured first. Built second. Run with numbers.
Audit
We stress-test your data architecture and AI usage, find token leaks, injection risks and bottlenecks, and price the fix.
Build
A fixed-scope proof of concept or pilot on real data, with weekly demos and a cost-per-task report.
Harden
Guardrails, evaluation suites and private deployment move the system from experiment to production.
Run
Monitoring, re-evaluation and cost tracking keep quality and spend where they should be.
AiGanak-SLM-1B: a 128k-context model small enough to own.
Our in-house decoder-only model, built first for logistics and supply-chain documents. It is still in training. What we learn from it powers the custom models we build for clients today.
I help organisations move beyond API wrappers to private AI environments, where agents reason and run workflows inside their own infrastructure.
Akshay Bankar · Founder & Director · Enterprise AI Architect, 16+ years
Senior architecture on every engagement.
Every audit, proposal and production system is reviewed by an architect with 16 years across Tech Mahindra, Deloitte, Optiva and Crossover before founding AiGanak.
Questions people ask
Can't find it? Press ⌘K and ask.
What does AiGanak do?
AiGanak builds production AI systems: multi-agent workflows on n8n and Make, prompt guardrails, private model deployments and custom small language models. It also runs LLM cost and reliability audits that show companies where their AI spend goes and how to cut it without losing quality.
Where is AiGanak based and who does it work with?
AiGanak is based in Pimple Saudagar, Pune, India. It works with companies in the US, Europe and India, mostly remotely.
Is AiGanak-SLM-1B available?
Not yet. AiGanak-SLM-1B is in Phase 2 convergence. Companies can join the Registry for early access, or commission a custom model proof of concept built on the same research.
How do I start?
Run the free Quick Check on the LLM Cost Audit page, or describe your project to AiGanak Agent on the contact page for a draft proposal that an engineer reviews with you.
Tell us what you're building.
AiGanak Agent drafts a scoped proposal in a few minutes. An engineer reviews it with you.