CXO briefing · Vendor-agnostic
Don’t buy technology first. Prove the solution first.
The Technology Sandbox takes an enterprise problem from definition to a validated MVP — before you commit to a technology, a vendor or an enterprise roll-out. Controlled isolation, governed access, full auditability and reusable experimentation evidence are designed in, not bolted on.
- Problem first
- Vendors are invited after the problem and the pathways are defined.
- Evidence, not pitches
- Competing models, clouds and architectures tested on your own data.
- Cheap failure
- A wrong answer in the sandbox costs weeks. After roll-out it costs years.
01 · The problem
Pilot purgatory
- Vendors are approached before the problem is fully defined.
- Solutions are evaluated from presentations rather than evidence.
- Proofs of concept are already biased toward one technology.
- Budgets are committed before the real architecture is understood.
- Integration, security, data and scale problems surface late.
- The enterprise ends up locked into a platform or vendor.
- Promising projects are abandoned because the first choice was wrong.
02 · The alternative
A controlled environment
Identify → Analyse → Experiment → Compare → Prototype → Validate → Decide, all before an enterprise commitment. The objective is never to promote a provider. It is to determine which combination of technologies, platforms, models, infrastructure and implementation approaches best solves your problem.
03 · The journey
Seven stages from problem to decision
Stage 1
Define the problem
Function, process, pain point, cost, time, error rate, risk and desired outcome.
Problem statement
Stage 2
Assess maturity
Data, systems, APIs, cloud, security, integration, AI readiness, workforce, governance.
Technology readiness profile
Stage 3
Identify solutions
Multiple technology pathways — automation, generative, agentic, hybrid, advanced.
Comparable options
Stage 4
Technology shoot-out
Competing models, infrastructure, data stores, frameworks and security controls.
Evidence, not pitches
Stage 5
Build the experiment
Isolated environment, synthetic or anonymised data, controlled access, full audit trail.
Does it actually work?
Stage 6
Build the MVP
Functional, technical, economic, security, integration, scalability and user viability.
Validated MVP
Stage 7
Roll-out decision
An evidence-based recommendation the board can act on.
GO / MODIFY / PIVOT / STOP
04 · Technology shoot-out
Competing approaches, side by side
AI models
OpenAI · Anthropic · Google · Meta · Mistral · NVIDIA · Qwen · IBM · Open source · Enterprise-specific
Infrastructure
AWS · Azure · Google Cloud · NVIDIA · Private cloud · Sovereign cloud · On-premise · Hybrid · Edge
Data
Vector databases · Warehouses · Lakehouses · Knowledge graphs · Retrieval · Synthetic data · Structured stores
Development
APIs · Low-code · Agent frameworks · Open-source frameworks · Custom · Microservices · Containers
Security
Identity · Zero trust · AI security · Data-loss prevention · Model security · Vulnerability testing · Red teaming · Governance
05 · Cost engine
Transparent consumption, not a licence quote
Sandbox cost = engineering hours + compute hours + AI token consumption + specialist tools + mentor and research hours + project management. Everything is expressed in a single internal unit, the SBU (Sandbox Build Unit): 1 SBU = 1 hour of specialist sandbox engineering capacity, indicatively ₹6,500 per SBU, with a multiplier by skill.
| Skill | SBU multiplier |
|---|---|
| Full-stack engineering | ×1.0 |
| Data engineering | ×1.2 |
| AI / ML engineering | ×1.5 |
| Security & red teaming | ×1.6 |
| Quantum / advanced research | ×2.0 |
| Mentor / industry council | ×1.8 |
DISCOVER
1–2 weeks
Problem definition · Assessment · Technology discovery · Architecture options · Initial experiments
20–40 engineering hours
EXPERIMENT
2–4 weeks
Technology shoot-out · Controlled experiment · Data preparation · Comparative evidence
50–100 engineering hours
BUILD
4–8 weeks
Data pipeline · AI / automation · APIs · UX · Security · Testing · Working MVP
120–250 engineering hours
SCALE READINESS
8–12 weeks
MVP hardening · Integration design · Security & governance sign-off · Roll-out business case
250–400 engineering hours
06 · The decision
Four honest outcomes
GO
Proceed to enterprise implementation.
MODIFY
Change architecture, technology or workflow and re-test.
PIVOT
Take a different technology pathway.
STOP
Do not invest further — failure here is inexpensive.
Call to action
Bring the problem. We’ll design the experiment.
01
Create your account
Sign in so your brief, evidence and estimates stay private to you.
02
Capture the brief
Ten short steps: objective, function, complexity, data, AI need, integrations, output, timeline, value.
03
AI evaluation
The engine scores readiness and proposes three competing pathways with an SBU-based estimate.
04
Sandbox kick-off
A KCF engineer and an industry mentor confirm scope and start the experiment.