AI Strategy, Value & Governance
Your AI spend is real. Is the return?
Independent, vendor-neutral advisory for real business outcomes.
ArcheNex AI helps businesses figure out if their AI spend is actually working — what’s paying off, what’s not, and what to try next. We build every engagement around real, measured results, not whatever a vendor’s trying to sell. The upshot: sharper calls, smarter spend, and AI decisions you can actually stand behind.
The problem
Nobody is short of AI. Everybody is short of proof.
Most companies already have AI running somewhere — copilot seats, a chatbot, a pilot a team built last quarter. What they do not have is an answer when the board, the CFO or the parent company asks what it returned. MIT put the number at 95% of enterprise pilots showing no measurable P&L impact. The gap is in measurement, not technology.
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Spend without a denominator
Seats, tokens and platform fees are known. Cost per task and cost per outcome are not — so nobody can say whether a workflow got cheaper or just busier.
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Pilots that cannot graduate
A demo impresses a room, then stalls because no baseline was captured, no owner was named, and nobody agreed in advance what success would look like.
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Advice with a product attached
Your platform vendor, your integrator and your cloud provider each have a preferred answer. None of them is positioned to tell you to stop.
What we do
Diagnose first. Build only if the diagnosis calls for it.
Three stages, entered in order. You decide at the end of each one whether the next is worth it.
Select a stage to see what it involves.
Deepdive — find the leak and the opportunity
It starts with a short call: what you run AI on today, what it costs, and who is asking you about returns. If we are not the right people for it, we say so there.
Then a contained diagnostic of your AI estate, or of the process underneath it — what it costs per task and per outcome, and what it is actually returning. You get a scorecard and a cost model built on your own numbers, not a benchmark you cannot verify.
Decide — a real option to stop
Findings presented with effort and value against each one, and a ranked list of what to kill, what to fix and what deserves more money.
You leave with a costed shortlist and a clear decision, including the option to walk away. Plenty of clients act on the report alone, and that is a finished engagement rather than a lost sale.
Deploy — release the value, then govern it
The highest-value item made real: integrated, instrumented and built alongside your team, measured against a baseline captured before we started. After that, an ongoing independent read on the portfolio — what to fund next, what to retire, what needs a closer look before it scales.
Where a build is genuinely the answer, we build narrowly — a scoped agent, a voice assistant or a workflow automation, deployed, documented and handed over. Only ever once a diagnostic has identified the need, never as a starting point. Anything larger routes to a technical partner under our oversight.
The firm
Consulting judgement, with technical depth behind it.
A senior team by design: a management consultant leading every engagement, backed by specialists in AI engineering, machine learning and product analytics. The person you meet is the person who does the work.
Snehasis Dutta
Co-Founder & Principal Consultant
A management consultant working on AI — framing the problem, sizing the opportunity, building the business case, and staying in the room through implementation. He leads every engagement personally.
Behind him sits the technical bench. Doctorate-level machine learning experience from semiconductor and chip-design firms, practising data science from a global measurement company, and product analytics from engineers who have shipped and scaled software. Between them they cover the ground a value question actually touches: what the technology can do, what it costs to run, and whether the number at the end holds up.
That mix is deliberate. Advice on AI value is only as good as the technical judgement underneath it, and a consultant working alone tends to accept a vendor's account of what is possible.
Questions
The things clients ask first.
We already have consultants and an internal AI team. Where do you fit?
Alongside them, not instead of them. Teams cannot mark their own homework on ROI, and neither can the partner who built the thing. We give an independent second read your existing people can argue with on the merits — and often use to make their own case internally.
You are a new firm. Why should we trust the output?
Because the entry engagement is built so you do not have to take it on faith. It is small and fixed in scope, and you can judge the framework and the quality of the findings before any larger commitment exists. We would rather earn the next engagement than argue for it.
What does an engagement cost?
Entry diagnostics are fixed-fee and deliberately sized to be approved without a committee. Larger work is scoped to the problem. We price after the problem is agreed and sized together, and if budget is tight we reduce what is delivered rather than quietly reduce quality.
What happens to our data?
It stays yours. Access is scoped to what the engagement requires, retention is agreed in writing before we start, and nothing you share is used to train anything or reused for another client. We work inside your environment wherever your policies require it.
What if the answer is that we should not be spending on AI?
Then that is the finding, with the reasoning attached. A fixed process, a cleaner report, or a decision someone simply needs to own often beats a model at a fraction of the cost. Saying so is the whole point of being independent.
Get in touch
Start with the number you cannot currently answer.
Tell us what you are running today and roughly what it costs. Snehasis reads everything that arrives.