InterviewGod
Screens and shortlists every hire across our own company.
In productionAI · Consulting & Strategy
Your AI strategy deck reads beautifully and no one can build a line of it.
Prove the riskiest part of it in two weeks, at a fixed price, before you fund the rest.
Most strategy work stops at the deck — a roadmap with quarters on it, no engineer in the room, and no code at the end. That's not a writing problem. It's who wrote it.
A Discovery Sprint puts engineers on your highest-risk use case for two weeks, at a fixed price, and hands back a working build plus the ROI math — yours to keep whether or not you go further.
30+ enterprises — Swiggy, Myntra, PhonePe, Indian Oil, HCL, CP Plus, RAK Ceramics — have run one.
Book a Discovery Sprint →
The first call is free · 45 minutes · no obligation
02 · HOW THE SCOPE MOVES
One engagement, four stages, ten deliverables — in sequence, not in parallel chaos.
Assess what to build. Prove it. Architect it. Run it. Each stage narrows the plan before the next one costs money.
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01
Assess
- Opportunity assessment
- Readiness assessment
- Use-case prioritisation & ROI
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02
Prove
- POC / feasibility build
- Build-vs-buy evaluation
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03
Architect
- AI architecture & data strategy
- Governance, risk & compliance
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04
Run
- Implementation roadmap
- Operating model & enablement
- Cost modelling & FinOps
03 · HOW WE START
Money first. Model second.
Four steps, always in this order. The first one carries the most weight because it is the one most decks skip.
01
Start from the money
We anchor to what the failure is costing you, or what the outcome needs to be worth, before a single model gets discussed. Everything after this step is measured against that number.
02
Prove the riskiest assumption first
A two-week Discovery Sprint, working code on your own data.
03
Cost it like an engineer
Compute, integration, maintenance — line-itemed, not a day rate.
04
Hand over a buildable plan
Roadmap and ROI math. You own the system — no lock-in.
04 · WHY MOST AI STRATEGY DECKS NEVER SHIP
Four ways a strategy deck dies before it ships — and which one killed yours.
Here's what the deck claimed, set beside what actually stopped it.
The deck said
"Every recommendation is prioritized and ready to execute."
Why it stalled
01Written by people who don't build
None of it accounts for what the data actually looks like, or what breaks first.
The deck said
"The roadmap sequences twelve months of AI initiatives."
Why it stalled
02Ranked by ambition, not feasibility
The most impressive slide gets funded first, not the fastest path to proof.
The deck said
"Phase one ships next quarter."
Why it stalled
03No proof before the budget
The full roadmap is approved before anything has touched real data.
The deck said
"Security and cost are covered in the appendix."
Why it stalled
04Governance/cost bolted on last
It shows up after the technical direction is locked and too expensive to unwind.
07 · Dogfooding
The bar we hold clients to, we hold ourselves to first.
We run hiring, outreach and upskilling for our own ~300-person operation on AI we built — InterviewGod, Vikaas, Vidya. We do not sell you software we hope works. We sell you the software we depend on.
Vikaas
Runs outbound and client communication for the same operation.
In productionEvery system above passed the same test we run on a client's riskiest idea: does it pay for itself before it ships.
08 · WHERE WE DELIVER
Delivery hubs
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India
Bengaluru · Chandigarh — since 2016
Swiggy, Myntra, PhonePe, Times Internet, Indian Oil, HCL, CP Plus
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GCC & UAE
Dubai
RAK Ceramics, Majra
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United States
California
FootLocker
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United Kingdom
Cambridge
In-region delivery base
Regulatory fluency
Where a client's data can sit is decided by law, not by which office is convenient for us. Every engagement is scoped against the residency and data-protection rules of the client's own jurisdiction before delivery is assigned to a hub — India, the UAE, the US or the UK.
That scoping happens once, at the start, and is written into the engagement plan — not re-litigated mid-project.
10 · HOW WE START
Book a Discovery Sprint
Three steps. Sprint first, nothing skipped.
Every engagement starts the same way — not with a proposal, with a working proof. What happens after depends on what the proof shows.
01
AI Discovery Sprint
We scope your highest-risk AI use case and build a working proof on your own data — costed like an engineer, not pitched like a slide.
02
Roadmap & business case
A prioritized build plan and the ROI math behind it — yours to keep whether or not you build with us next.
03
Build & continuous advisory
If the numbers hold, we build it, then stay on as your technical advisors. No lock-in — the system is yours.
11 · FAQ
Q01
Q02
Q04
Q05
Q06
Q07
Q08
Q09
Q10
Read the objection first. The pitch comes after.
Ten questions, in the order buyers actually ask them — starting with the one that ends most vendor conversations before they start.
Q03 · Highest-priority objection
We already have a deck — why do we need this?
A deck tells you what could work. A Discovery Sprint tells you what does — on your data, in two weeks, at a fixed price, kept either way.
What does AI consulting include?
Discovery Sprint scoping, a production build, and handover — not a slide deck.
Consulting vs. strategy — what's different?
Strategy without a build is a guess with better formatting. We prove the riskiest use case before recommending the rest.
How do you decide which use case comes first?
Ranked by risk and payoff, not novelty. The use case most likely to fail — and most likely to pay — gets proved first.
Do you build, or just advise?
Build. We run our own hiring, outreach, and training on the AI we build — InterviewGod, Vikaas, Vidya.
How long does an engagement take?
Discovery Sprint: two weeks, fixed price. Production build: weeks, not quarters.
How do you prove ROI before full spend?
The Sprint output is a working proof of your riskiest assumption — roadmap and ROI math attached.
What about governance and data residency?
Scoped up front, inside the Discovery Sprint — never bolted on after the build starts.
Which models or platforms do you use?
Not tied to one vendor. The model is chosen for the use case, during the Sprint's feasibility work.
Do we need clean data or an in-house AI team first?
No. The Sprint runs on your data as it exists today. You own what comes out of it — no lock-in.
Next step
Find out which of your AI ideas is worth building first.
Bring the deck that never shipped. In 45 minutes we'll tell you which use case to prove — and what it takes to make it real.
No slideware. A working answer, scoped to your data.
Book a Discovery Sprint