Methodology
Two tracks, because the two problems are different
Building something new rewards speed and disposability. Modernising something that has to keep running rewards reversibility. We run them differently on purpose.
Innovation track
For a new capability that does not exist yet.
Optimised for speed of evidence. Each stage has an explicit go / no-go so the work can be stopped cheaply. Typical cadence: a prototype in weeks, not quarters.
- Idea
- Proof of concept
- Prototype
- MVP
- Production
Enterprise transformation track
For an existing estate that has to keep running.
Optimised for reversibility. Incremental migration behind a facade, with the existing path available until the new one has earned trust. No big-bang cutovers.
- Assessment
- Roadmap
- Modernization
- Integration
- Transformation
Discover → Define → Design → Prototype → Build → Deploy → Scale
The phases apply inside either track. What changes between tracks is the pace and the tolerance for reversal.
- 01
Discover
Business outcomes, constraints, data reality, stakeholders.
- 02
Define
Scope, success metrics, architecture decisions, risks.
- 03
Design
Experience, system and data design. Agent boundaries and guardrails.
- 04
Prototype
Fast, disposable, evidence-generating. Built to be judged.
- 05
Build
Iterative delivery in two-week increments. Tested and reviewed.
- 06
Deploy
CI/CD, DevSecOps, observability, rollback. Boring on purpose.
- 07
Scale
Performance, cost, governance, and the next use case.
The AI journey: strategy to managed AI
- 01
Strategy
Where AI creates measurable value in your business — and where it does not.
- 02
Use Case Discovery
Candidate use cases scored on value, feasibility, data readiness and risk.
- 03
Prototype
A working thing in weeks, on your data, that people can judge.
- 04
MVP
Narrow scope, production quality, real users, measured outcomes.
- 05
Production
Hardened, evaluated, observable, and safe to depend on.
- 06
Integration
Wired into the systems of record where the work actually happens.
- 07
Scale
From one use case to a portfolio, on shared platform foundations.
- 08
Managed AI
We run it: monitoring, evaluation, model updates, cost control.
How we run the work day to day
Agile & Scrum
Two-week sprints, demonstrable increments, a prioritised backlog.
Iterative delivery
Scope adjusted on evidence rather than defended to a plan.
MVP approach
Narrow scope at production quality, rather than broad scope at demo quality.
Enterprise delivery
Change control, environments, cutover planning and stakeholder governance.
DevSecOps
Security scanning and controls inside the pipeline, not appended before go-live.
See how this applies to your problem
Bring us a process or use case and we'll tell you which track it belongs on, and where in the journey to start.
Or reach us directly at info@ontogenesistechnologies.com · +91-9999101853