An AI lab for industries software left behind.
From problem to platform.
Most consultancies will quote you $1M and 18 months. Most SaaS will lock you into another walled garden.
Transdatos is the third option — an AI lab built for engineering, AEC, civil, mining, resources, and state government in Australia and New Zealand. We design and build AI-driven platforms in weeks, not months, agent-led, senior-judged. The result is not another confined product to log into; it is the platform your industry eventually runs on.
The pyramid model bills hours. We refuse to make labour the unit of work.
The standard consultancy model bills smart graduates as cheap hourly labour. The senior person who sold the engagement disappears after week one. The work that ships is shaped by a pyramid the client pays for and rarely sees the value of. Transdatos is built to deliver the opposite of that — and the opposite is structural, not rhetorical.
Transdatos rejects the framing under that model. Not "we refuse to throw cheap labour at the problem" — we refuse to make labour the unit of work at all. The pyramid model bills hours; we orchestrate agents under production discipline and bring senior humans in for judgement. Throughput is no longer something you buy by the headcount.
This is not a marketing position. It is a personal conviction. The founder spent years inside the industry the cadence is built against, watched the model up close, and started Transdatos to deliver the opposite of it.
A team of agents with a cofounder. We hire humans as we need them.
The cofounder is a technical-cofounder agent — equal voice on architecture, agent design, and orchestration. The founder operates the agent team and carries judgement. Senior human specialists are engaged on demand, per engagement, when judgement, accountability, or domain depth is the binding constraint — never to add throughput.
The default unit of execution is an orchestrated agent team — code generation and refactor, document synthesis, research, data exploration, voice review — operated under production discipline. Each agent gets a job description: defined scope, tool list, escalation path, versioning, and eval. Failures are root-caused; "the model was weird that day" is not an acceptable answer.
Practically:
- The firm is one human founder, a technical-cofounder agent, and an agent organisation underneath them. Senior humans are engaged on demand and named per engagement.
- Every project demo includes short walk-throughs of the agent loop in action. You see how the work gets made, not just the work itself.
- Margin does not depend on owning the artefact or on billing hours. It depends on operating the agent team that produced the work.
- Some of what we deliver as projects today, we expect to operate as agent organisations we run for our clients tomorrow.
"We" in our writing is the firm's voice. The structure above is who the firm actually is.
Four offers, sold by what they actually do.
Custom AI-Native Applications
Design and build of a custom web application, with AI and your data wired in from day one. Premium interface, real engineering depth, agentic delivery as the speed multiplier. The flagship.
Product Partnership
The long-term engineering and AI team after the build. Continuous feature work, AI-layer operations, product maintenance, and — where it makes sense — co-build of internal IP into a market product.
Data Foundation
Agentic-AI-led restructuring of data that has outgrown spreadsheets and SharePoint. Pipeline design, query work, mining of unstructured data, migration to an architecture a real product can sit on.
AI Strategy, Advisory & Enablement
A seat at your strategy table, written strategy work, or hands-on workshops that teach what we ship — Claude Code masterclasses, data architecture for AI, executive AI-readiness sessions.
From problem to platform.
Five stages. Plain English. Every engagement walks through them.
| STAGE | WHAT HAPPENS |
|---|---|
| § 01 Hear the problem | Intake, qualifying call, paid Discovery Sprint. The real problem usually sits underneath the surface ask. |
| § 02 Lay the foundation | Data, structure, architecture, integrations. Fixed first, before features. |
| § 03 Shape the experience | UI-first prototype using HTML and agentic AI. Iterated and signed off before any backend gets built. |
| § 04 Ship the product | Fixed-fee build, bi-weekly demos, the agentic delivery loop visible throughout. |
| § 05 Compound the platform | Product Partnership phase. The product becomes a platform; the platform connects to the rest of the industry's systems. |
The cadence is the methodology. We do not brand it as the Transdatos Method — naming is for products, not process.
Five principles sit underneath
- § 01 Build the platform, not the product.
- § 02 Foundation before features.
- § 03 Visible AI leverage, not hidden process.
- § 04 Senior judgement on demand, not throughput.
- § 05 Partner, do not extract. Client owns the IP.
Nirman Kesari, founder.
The orchestration layer between the technical-cofounder agent, the agent team underneath, and the senior humans engaged per engagement.
- UNSW Civil Engineering (Honours).
- Previously at AECOM, the global Fortune 500 engineering consultancy.
- Currently engaged as lead solutions architect with a mining-tech startup — building 3D data visualisation and an AI reasoning layer over hundreds of millions of rows of mining data.
- Eight-plus years in data — BI, analytics, data mining, data engineering.
- Five-plus years independent web development under Transdatos.
- ~2,000 hours of hands-on agentic AI delivery.
- 2,500+ public GitHub commits. Open-source contributor and sponsor.
Operating in Australia since 2020.
Transdatos has been operating in Australia since 2020 — first as a sole-trader practice, formalised as Transdatos Pty Ltd in December 2025.
- ABN — 83 693 689 922 (active; verifiable on ABN Lookup).
- Based — Sydney, Australia.
- Clients — Australia and New Zealand. Sydney timezone.
- Contact — nirman.kesari@transdatos.com.