AI · Consulting & strategy
Banao's AI consulting is run by the engineers who put models into production, not a strategy team that hands the build to someone else. We tell you which AI use cases are worth the money, prove the riskiest one on a real two-week build, and hand back a roadmap your own team — or ours — can execute, with the ROI maths attached.
A strategy you can't build is just an expensive opinion. We decide what AI to build inside our own 300-person company the same way — by costing it, building the hard part, and killing the ideas that don't pay — and that is the discipline we bring to yours.
Banao— InterviewGod and Vikaas were our own build-vs-buy calls; we ran the same analysis on ourselves before betting the company's hiring and pipeline on them.
Strategy without engineering is a wish list; engineering without strategy is wasted spend. We do both — the use-case economics, the feasibility, the architecture, and the roadmap your team can actually build against.
We work your operation, not a template — mapping where AI removes the most cost, error, or delay, and ranking each candidate by the money it moves rather than how impressive it demos.
An honest grade of your data, systems, and team against what each use case actually needs to ship — so the plan accounts for the integration work instead of pretending your data is already clean.
Every candidate scored on value, feasibility, data availability, and time to payback, so the portfolio you fund starts with the ones that earn their keep first — and the rest wait or die.
We build the riskiest part of the top use case on your real data, not a slide — because the only honest test of whether an idea ships is to put working code in front of it early.
A sequenced, costed plan with dependencies, milestones, and the team it takes — written so your engineers can pick it up and start, not so it photographs well in a board pack.
Unbiased guidance on where an off-the-shelf product beats a custom build, which model and platform fit each step, and where a vendor's pricing will quietly outgrow the value.
The reference architecture, data pipelines, and access model the roadmap depends on — designed once, up front, so use cases two and three reuse the foundation instead of rebuilding it.
The controls, audit logging, and data-residency design your risk team needs to sign off — including the EU AI Act, PDPL, and DPDP obligations that decide which use cases are even allowed.
Whether to build in-house, partner, or both — and the workflows, ownership, and upskilling that keep AI running after the engagement ends, instead of stalling when the consultants leave.
Token, inference, and infrastructure costs modelled before you commit, so the business case survives contact with a production bill rather than a benchmark run on a free tier.
Most AI strategy work starts with a maturity matrix and ends with a deck. Ours starts with your P&L and your systems, and ends with a roadmap that has already been pressure-tested by building part of it. We are an engineering company that consults, not a consultancy that subcontracts the build — so the advice is constrained by what we know actually ships.
The first job is to separate the AI use cases that move real money from the ones that move a slide. The second is to prove the hardest assumption with code before you fund a year of it. Everything we hand over is shaped so your team can act on it on the Monday after we leave.
We anchor every candidate to a number — hours removed, errors caught, revenue protected — so the portfolio is ranked by impact on the business, not by which idea sounds most advanced in the room.
Rather than a paper feasibility score, we build the part most likely to fail — the messy data, the hard integration, the accuracy bar — so the roadmap rests on evidence instead of optimism.
We model the real cost to build, run, and maintain each use case — including the inference bill and the integration work — so the business case is one a CFO can defend, not one that collapses in production.
The deliverable is a sequenced, costed roadmap with architecture and dependencies spelled out — readable by the engineers who will execute it, whether that is your team, ours, or both.
We get called in after the first strategy engagement — the one that produced a glossy roadmap and no working software a year later. The deck is rarely wrong about the vision. It is wrong about what it would take to build, because it was written by people who have never had to make a model behave on a production system.
The same handful of failures repeat across nearly every stalled strategy. We would rather name them on the first call than bill you to rediscover them on the third. If your last AI plan is sitting in a drawer, it most likely died of one of these.
A roadmap from advisors who have never shipped a model underestimates the integration, the data cleanup, and the evaluation — so the timeline is fiction and the first real use case blows the budget.
Use cases get prioritised by how much they promise, ignoring whether the data exists or the workflow can be reached. The flashiest item goes first, stalls on reality, and burns the mandate.
Committing a year of spend on a feasibility score nobody tested is how good strategies die. Without an early build, the riskiest assumption stays hidden until it is the most expensive thing to fix.
When residency, audit, and the inference bill are afterthoughts, the use case that looked approved turns out to be illegal or uneconomic — and the whole roadmap reshuffles after the money is spent.
The fastest way to de-risk a year of AI spend is to spend two weeks building the part most likely to break. That is what our engagement is built around: not a longer assessment, but an earlier proof. By the end you have evidence, not just a recommendation, about whether the top use case clears the bar your business actually needs.
The proof is not a throwaway prototype. It is built on your real data, scoped to the riskiest assumption, and designed so the production build continues from it rather than starting over. The roadmap that comes out the other side is costed against what we just learned, not what a benchmark promised.
We pick the assumption the whole plan rests on — the accuracy, the integration, the data quality — and build enough to know if it holds, while the cost of being wrong is still small.
You leave with a working proof, a measured read on feasibility, and ROI maths grounded in it — the difference between funding a plan on conviction and funding it on a result you watched.
The output is a prioritised, costed sequence of use cases with the architecture they share — so use case one funds the foundation and use cases two and three build on it instead of restarting.
We hand over the proof, the design, and the plan in a form your engineers can run with. If you want us to build it, we can — but the strategy is useful whether or not you do.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The decisions and the systems behind them are real.
Banao — own AI portfolio
InterviewGod and Vikaas exist because the same prioritisation we sell said they would pay — and the ideas that didn't clear the bar were killed before a line was written. We screen our own hires and run our own demand generation on the two that survived the maths.
RAK Ceramics
We scoped where computer vision would actually pay on a RAK Ceramics production line, proved it on real footage before the budget was committed, and built the system that now runs there — the engineering behind the recommendation, in the same hands.
Enterprise services firm (anonymized)
A readiness assessment found that three of five board-favoured AI ideas had no data path to ship. We re-sequenced the roadmap around the two that did, costed them honestly, and saved a year of spend on items that would have stalled.
Banao runs a ~300-person engineering company on its own AI, and every one of those systems started as a build-vs-buy decision we made on ourselves. We assessed the ideas, costed them, built the hard part, and funded only the ones that paid — InterviewGod for hiring, Vikaas for demand generation — while quietly shelving the ones that didn't.
That is the difference between a consultancy that advises on AI and one that lives with the consequences of its own AI calls. When we tell you to kill a use case or sequence it later, it is the same judgement we apply when it is our own money and our own operation on the line.
We deliver from offices in India, the UAE, the UK, and the US, and we shape each strategy to the economics, regulation, and data-residency rules the market actually works under.
Boards across Dubai and the free zones have funded the pilots and now want a costed AI portfolio with an owner and an audit trail. We advise on which use cases pay first and design residency in from the start, because the PDPL decides which are even allowed — the same footing as our production work with RAK Ceramics.
Vision 2030 and the giga-projects move on aggressive timelines, so a strategy that can't start building in weeks is a strategy that misses the window. We build roadmaps that keep data in-Kingdom to meet PDPL and SDAIA expectations and that prioritise Arabic-first use cases ready to ship, not study.
For California and New York enterprises the pull is cost — the research, support, and back-office hours that have grown expensive to staff. We frame the strategy around where AI removes the most expensive labour first, and build the SOC 2 governance and ROI case US procurement and risk teams now demand before any spend.
Our Cambridge UK presence supports enterprise and public-sector strategy under UK GDPR and ICO guidance, with the EU AI Act now reaching UK suppliers. Explainability and a clear accountability trail are part of how we prioritise use cases, not a compliance pass bolted on after the plan is set.
Bangalore and Chandigarh hold our delivery bench, so a strategy moves into a build in weeks rather than waiting on a hire. We advise to the DPDP Act and run cost-efficient delivery close to the engineering that proves and ships each use case.
Most consultancies will sell you a strategy phase regardless. We would rather tell you when you don't need one — it is why technical leaders take our second call.
You have likely been pitched an AI strategy phase already. We start by proving which of your use cases is worth building, not by quoting a year of advisory.
We assess your candidate use cases, build the riskiest part of the top one on your real data, and hand back a scoped design, a prioritised roadmap, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We turn the proof into a sequenced, costed portfolio — build-vs-buy per use case, the shared architecture, the team, and the governance — written so your engineers can pick it up and start.
We build the priority use cases or steer your team as they do, with the architecture and evaluation in place from the start and a standing review as each system reaches production.
What does AI consulting actually include?
An opportunity and readiness assessment, use-case prioritisation with ROI modelling, a proof of the riskiest assumption built on your real data, build-vs-buy and vendor guidance, the reference architecture, governance and residency design, and a costed implementation roadmap your team can execute. The point is a plan that ships, not a deck that files.
What is the difference between AI consulting and AI strategy?
AI strategy is the decision layer — which use cases to fund, in what order, build or buy, and why. AI consulting is the broader engagement that produces it and proves it: the assessment, the feasibility build, the architecture, and the roadmap. We do both, and we tie the strategy to a real build so it is grounded in what actually ships.
We already have an AI strategy deck — why would we need you?
If it's shipping, you don't. We're usually called in when a previous deck produced no working software, because it was written without engineers and underestimated the integration, data, and evaluation work. We pressure-test the plan by building the hardest part, then re-cost and re-sequence it around what's actually feasible.
How do you decide which AI use cases are worth building?
Each candidate is scored on the money it moves, the feasibility, whether the data exists, and the time to payback — then the riskiest assumption of the top one is built on your real data before any budget is committed. Ambition doesn't set the order; evidence and economics do, and we'll recommend killing the ones that don't pay.
Do you build what you recommend, or just advise?
We build. Banao is a ~300-engineer company that consults, not a consultancy that subcontracts delivery — so the advice is constrained by what we know ships, and we can build the roadmap ourselves. The strategy is still yours to take to any team; we just won't hand you a plan we couldn't execute.
How long does an AI strategy engagement take?
The core is a 2-week Discovery Sprint that produces a proof, a prioritised roadmap, and an ROI model. A fuller portfolio assessment across many use cases runs a few weeks longer. Either way our delivery bench means the plan moves into a build in weeks, not the months a fresh hire would take to begin.
How do you prove ROI before we commit budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, a working proof on your hardest use case plus a costed roadmap and ROI model you keep whether or not you continue. Worst case you have an evidence-based assessment; best case you have your board business case, built on a result you watched.
How do you handle AI governance, risk, and data residency?
Governance is part of how we prioritise, not a pass at the end. We design audit logging, access control, and data-residency into the architecture and rule out use cases that the EU AI Act, PDPL, or DPDP would block — so the roadmap your risk team sees is one they can sign off, not one that unravels in review.
Which models, platforms, and frameworks do you recommend?
We are model- and vendor-agnostic and choose per use case, defaulting to the most capable Claude models for reasoning and routing simpler steps to cheaper ones. We give unbiased build-vs-buy guidance — including when an off-the-shelf product beats a custom build — so you are never locked into one provider by our recommendation.
Do we need clean data or an in-house AI team to start?
No. Assessing your data and systems honestly is part of the work, and the roadmap accounts for the cleanup and integration the use cases actually need. On the team, we advise on build-in-house versus partner and can deliver the first builds ourselves while your people skill up alongside us.
Bring the AI use cases on your roadmap — or the deck that never shipped. In 45 minutes we'll tell you which one to prove first, and what it would take to turn it into a system that runs.
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