[ Contact ]

Tell us what you're building.

Initial conversations are direct, free, and frank. We'll tell you whether we're the right fit, what we'd need to deliver if we are, and recommend better options if we aren't. The fastest path to a serious answer is a 30-minute call.

→ The simplest path

Email us with a one-paragraph brief.

Tell us what you're building, the rough scope, and the timeline you're working against. We'll come back within two business days with a yes, no, or "let's talk." If we say yes, we'll propose a 30-minute call to dig in.

hello@dhlabs.ai →
→ What helps us be useful fast
  • Your context. What domain, what stage, what's already in place.
  • The thing you want to build. Specific is better than aspirational.
  • The timeline. Real deadlines or directional ranges both work.
  • Constraints. Cloud preferences, regulatory environment, model preferences, budget range if you have one.
  • What's failed before, if anything. Knowing what didn't work shortens the conversation considerably.
→ A note on what we don't do

We're useful when we're a fit.

We don't take engagements where we can't be the right team. We don't ship pure strategy decks or AI maturity assessments — there are firms much better suited to that work, and we're happy to recommend them. We don't deploy generic AI agents into customer-facing channels for cold outreach. We don't sell pilot retainers that don't lead to production deployment.

Where we are useful: data engineering at production scale, edge and perception ML, multimodal AI platforms, agentic operations on Claude, and the South Asia delivery bench that lets all of the above happen at price points global tier-one SIs can't match.

→ Frequently asked, briefly answered
Do you take fixed-price engagements?

For well-scoped milestones inside a larger engagement, yes. For end-to-end platform builds where scope evolves with what we discover in the first weeks, time-and-materials is generally a better fit for both sides.

What does an engagement typically look like?

An embedded team — typically 3–8 engineers — working alongside your team for 3–24 months, with US-based partnership leadership accountable for delivery. Most engagements include ongoing platform operation after the initial build.

What model providers do you deploy on?

We deploy Claude (Anthropic), AWS Bedrock model lineup, and others based on workload fit. We're an Anthropic Partner Network applicant building a Claude-first practice across our three pillars, while remaining model-agnostic where customer requirements demand it.

Do you work outside the US and South Asia?

Our delivery model is optimized for US customers served from a South Asia bench. We've taken selective engagements in the GCC and Europe where the customer profile fits. Best to ask.

Are you hiring?

Selectively. If your background is in production AI/ML engineering, data platform engineering, or applied research and you've shipped systems that operate in the real world, write to us at hello@dhlabs.ai.