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Why our footprint is US-led with engineering across Hyderabad and Bengaluru — and why we think the firms that thread this geography well will do disproportionately well in the next five years of AI services.
DeHaze Labs runs on a specific geographic structure. Partnership leadership and customer accountability sit in the United States. Engineering and delivery sit across South Asia, primarily in Hyderabad and Bengaluru. The structure isn't a cost play. It's a deliberate choice about where the right talent for AI services work actually is, and how to organize a firm so that talent can do production work for North American enterprise and mid-market customers without the friction that's traditionally come with offshore engineering models.
This post is about why we organized this way and what we think it means for the AI services market over the next several years.
The honest version of the offshore engineering reputation
We should start with what readers are bringing to a post like this.
"US firm with offshore engineering" is a category with mixed reputational baggage. Some of the baggage is fair. Plenty of customers have been burned by arrangements where the contracts were sold by US-based account managers and the work was done by teams the customers never met, with quality that didn't survive contact with production. Some of the baggage is unfair — the assumption that geographic distance automatically means lower quality, when in practice the variance within any geography is much larger than the variance between geographies. Both versions of the prior exist in the market. We've encountered both when we explain our model.
The way we think about it: the model works when the model is built honestly, and breaks when it isn't. The offshore engineering arrangements that fail tend to fail for predictable reasons — opaque team composition, weak technical leadership in the delivery geography, account-management layers between customer and engineer, no real accountability when something goes wrong. The arrangements that work avoid these specific failure modes by design. We've tried to design ours to avoid them.
That's the honest framing. From there, the question is what's different about doing this now, in the AI services market, that wasn't true ten years ago.
What changed in the last decade
The Indian AI engineering ecosystem in 2026 is not the Indian outsourcing ecosystem of 2015. Three things changed.
The talent depth is real and recent. Production AI engineering — data platforms, ML systems, multimodal pipelines, agentic architectures — requires engineers who've shipped these systems before. Ten years ago, those engineers were concentrated in a few US tech hubs. That hasn't been true for a while. Hyderabad and Bengaluru have produced a generation of senior AI and data engineers through a combination of strong domestic engineering programs, the global capability centers (GCCs) that Fortune 500 firms have stood up in both cities, and the Indian product companies that have shipped at scale internationally. The senior talent that builds production AI systems exists in both geographies, in serious numbers, and it didn't ten years ago.
The GCC layer changed the work culture. When Fortune 500 firms set up GCCs in Bengaluru and Hyderabad — Goldman, Microsoft, Google, Walmart, dozens of others — they didn't replicate "offshore" working models. They replicated their internal engineering cultures. The result is a workforce in both cities that has spent the last decade working inside Western enterprise engineering practices: code review, on-call rotations, design docs, post-mortems, architectural decision records, the normal artifacts of how production engineering is supposed to work. The senior engineers we hire have these reflexes already. We're not training them in.
Indian manufacturing, space, and infrastructure are real customers, not hypothetical ones. This is the part most US-headquartered services firms haven't caught up to yet. India's manufacturing sector has scaled meaningfully. The Indian space sector — both ISRO and a growing private layer — is a serious AI customer. The GCCs themselves are large engineering customers in their own right. We do not run as a firm whose only customers are in North America and whose only delivery is offshore. We run as a firm with direct relationships in the Indian industrial ecosystem, with active engagements that originate in South Asia and are delivered there.
This dual customer base — North American mid-market and enterprise on one side, Indian industrial and space and GCC on the other — gives us a perspective on the AI services market most US-only or India-only firms don't have. We see what each market is doing and where they're converging.
How we organize for this to work
The structural decisions we made when we set the firm up:
US partnership leadership owns customer accountability. Every customer has a US-based partner who owns the relationship, takes the call when something goes wrong, and is on the hook for delivery. The customer is not handed off to an account manager who doesn't speak with the engineering team. The partner is technical, knows the engagement in detail, and is the same person across the lifecycle of the relationship.
Engineering teams are embedded, not arms-length. Our engineers work as part of customer teams. They join customer Slacks and standups, write code in customer repos, attend customer planning meetings. The geographic distance is real but the working distance isn't. The customer's engineering team and our engineering team operate as one team, with the partner from our side and the customer's technical lead from theirs both accountable for outcomes.
Senior engineering leadership lives in the delivery geography. This is the structural piece most outsourcing arrangements get wrong. We have senior technical leadership — staff and principal-level engineers, architects, technical directors — based in Hyderabad and Bengaluru. They're not in a "team lead" role with a US-based architect making decisions for them. They're doing the architecture work, owning the technical direction, and being the senior voice on the engagement. The cost-advantaged geography has the senior talent, not just the cost-advantaged labor.
No layers between the customer and the engineers. No account managers. No project managers translating between customer requirements and engineering work. The partner from our side is technical; the engineers from our side talk directly to the customer's technical team. The model only works when the layers most outsourcing firms put between customer and engineer don't exist.
What we don't do
A few things we're explicit about not being:
We don't take engagements where US partnership leadership wouldn't survive. Every engagement gets a US partner. If we couldn't credibly staff a US partner who can own the customer relationship at depth, we don't take the engagement.
We don't do staff augmentation. We're not a body shop. We don't ship engineers to fill out a customer's team chart. We take whole-system engagements where we own the architecture and the outcome, with a US partner accountable.
We don't sell on cost arbitrage. The reason for the US-South-Asia split is that the senior AI talent is genuinely distributed and the model produces better outcomes for North American mid-market customers than tier-one global SIs. Cost is a consequence, not the lede. If your evaluation criterion is the lowest hourly rate, we're going to lose, and you're going to get what you pay for.
What we think about the market
Our prediction for the next five years of AI services: the firms that thread the US-South-Asia structure honestly will do disproportionately well, and the firms that mishandle either side will struggle.
The reason is that the customer base for AI services is bifurcating. Tier-one customers (hyperscalers, top-twenty banks, very large enterprises) buy from tier-one global SIs and will continue to. Mid-market customers and emerging-vertical customers — data center operators scaling buildouts, industrial operators digitizing operations, regulated mid-market firms shipping AI for the first time — are looking for a fundamentally different kind of partner. They want production engineering at price points the tier-ones can't economically meet, with the technical depth of a senior engineering team and the accountability of a small firm. The geographic structure that makes this possible is exactly the US-South-Asia structure.
The firms that get this right will be the ones with senior engineering depth in both geographies, US partnership accountability that's actually technical, and direct customer relationships in both the North American and Indian industrial ecosystems. We've designed DeHaze to be one of those firms. We think there will be a small number of others, and that the market will sort itself fairly quickly between firms that operate this way and firms that pretend to.
If you're a customer in one of the markets we serve and the structure we've described matches what you're looking for, we'd like to talk.
DeHaze Labs builds production AI and data platforms for the physical economy. US partnership leadership; engineering across Hyderabad and Bengaluru. Get in touch at hello@dhlabs.ai.