How we work.
Four operating principles that make the difference between a slide deck and a working system in production.
Forward-deployed engineers
We don't send consultants with slide decks. We embed engineers directly inside your team — working in your Slack, your codebase, your infrastructure, alongside your people day-to-day.
They attend your standups. They understand your constraints. They build capability in your team continuously as the project progresses — so when they exit, your engineers can operate and extend what was built without ongoing dependency.
- →Works inside your team, not from a remote office
- →Builds capability in your engineers as the project runs
- →Exits cleanly with full documentation and runbooks
- →No lock-in — you own every line of code we write
“The best way to transfer knowledge is to build together.”
Our model is based on how the best engineering teams work — not how consultancies bill.
Token cost · Same workload
Indicative comparison · Same task, same output quality
Open-source cost architecture
Enterprise AI doesn't have to mean hyperscaler pricing. We select and run production-grade open-source models on optimised hardware — the same models we've proven in our own products.
The result: 5–10× lower token costs than Anthropic Claude or OpenAI GPT-4o for the same task, on the same data, with the same output quality. No per-token API bills. No usage-based scaling. Infrastructure cost is fixed and predictable.
Privacy-first deployment
On-premise is our default architecture, not a premium add-on. Every system we build is designed so that your data never leaves your infrastructure — no shared model weights, no cloud logging, no third-party API calls during operation.
This matters for regulated industries, Australian Privacy Act compliance, and any organisation with operationally sensitive data. Where cloud models are used (rare), we implement data masking by default before any external call.
- →Data never leaves your infrastructure
- →No shared model weights or training on your data
- →Australian Privacy Act and data sovereignty compliant
- →Suitable for health, finance, agriculture, and defence
Self-learning agent factory
For Full Transformation engagements, we leave behind a meta-system that builds and tunes specialised AI agents from operational briefs — without requiring code changes from your team.
In AgriTwin, this let us stand up a new agent for “vet scheduling” or “feed plan optimisation” in days, not weeks. Agents learn from operator feedback loops and improve automatically over time. When we exit, your team has the factory, not just the agents.
- →Build new agents from a plain-language brief
- →Agents learn from operator feedback and corrections
- →No code changes required for new agent types
- →The factory itself is the handover artifact
Agent lifecycle
Brief
Spec
Live agent
Brief → live agent in days, not weeks
Ready to see what AI can do inside your business?
Start with a two-week Discovery Sprint. Fixed price. No lock-in. Walk away with a clear roadmap — or a working prototype.
Email KK.Santhanam@cxomirror.com