We're an AI-native agency for businesses that are tired of demos. Custom AI development, automation, and adoption consulting — built around your data, your processes, and what you actually need to ship. Founded by engineers with sixteen years of shipping real systems between them, now pointed at the problems most AI vendors won't touch: startups, MSMEs, and the long tail of enterprises that need AI to work, not just look good in a deck.
Working with founders, operators, and CTOs across India, the GCC, and beyond. Engagements have ranged from two-week pilots to multi-quarter retainers — from early-stage startups to a handful of Fortune 500 teams.
Four services. We don't pretend to do everything. If your problem fits one of these, we can probably help. If it doesn't, we'll tell you and point you somewhere that does.
For when off-the-shelf AI doesn't fit your problem.
We build production AI systems on your data, in your stack, behind your auth. RAG pipelines that actually retrieve the right thing. Agents that handle real workflows, not toy demos. Fine-tuned models when fine-tuning is genuinely the right call — and we'll tell you when it isn't.
What we typically build: retrieval systems over private knowledge bases, document understanding pipelines, AI agents for internal workflows, classification and extraction at scale, custom model deployments with proper evals and monitoring.
For leaders who need a real plan, not another deck.
Most AI projects fail before any code is written. They fail because nobody asked the boring questions first: what data do you actually have, what does the workflow really look like, what will adoption look like in month six.
We start there. You get a written assessment of where AI will and won't help, a phased roadmap with cost ranges, and a clear-eyed view of what your team will need to make it stick. Some clients run the build themselves afterward — that's a fine outcome.
For the work your team shouldn't be doing manually anymore.
Disha leads this practice. Eight years of putting bots into production for Fortune 200 clients tends to teach you which automation projects pay back and which ones quietly break in week three. We build the first kind.
We work across UiPath, Power Automate, n8n, and custom Python automation — picking the right tool for your scale and budget, not the one with the highest commission. For more complex flows, we combine RPA with LLM-driven steps so the bot can handle the messy edge cases that traditional automation gives up on.
For products that need AI baked in, not bolted on.
Sometimes the AI is the product. Sometimes it's a feature inside a product you also need built. Either way, we ship the whole thing — backend, frontend, data layer, model integrations, the auth, the billing, the boring parts that make it actually work in production.
Kinjal leads this. She's spent eight years building full products for MNCs across domains. She'll be the one telling you when a feature isn't worth the build cost, which is more useful than someone telling you everything is a great idea.
Same process whether you're a five-person startup or a Fortune 500 division. The work scales; the discipline doesn't change.
Before we propose anything, we spend time with the people who will actually use the system. Operators, not just executives. We map the real workflow — including the workarounds, the spreadsheets nobody talks about, and the parts where AI definitely shouldn't go.
We design the smallest version of the system that will prove or disprove the value. Architecture, data flow, models, evals, costs at scale. You see all of it before any code is written.
Sprints, working software at the end of each one, demos to your team — not a six-month black box that lands in production with surprises. We integrate with your existing stack rather than asking you to rebuild around ours.
Going live is the start of the work, not the end. AI systems drift. Models age. Edge cases surface that nobody saw in testing. We stay on retainer to monitor, tune, and improve — for as long as you want us to.
Not sure which step you're at?
That's the most common reason people contact us.
Tell us where you're stuckIndustries where we've delivered work to date. Sector experience genuinely matters in AI — the regulations, the data realities, and the failure modes are very different across these. If yours isn't listed, we may still be a fit, but we'll say so honestly when we talk.
We get asked this a lot in first calls. Honestly, here's the answer.
Both founders are working engineers, not ex-consultants. We were building enterprise systems back when "AI" mostly meant rule-based engines and the occasional regression model. The current wave is genuinely different, but the discipline of putting working software into production hasn't changed — and most of the AI agencies that opened in 2023 are learning that part right now, on your dollar.
A surprising amount of "AI work" we get hired for ends with us recommending a SQL query and a cron job. That's not a sales tactic; it's just what's true. If a problem doesn't need AI, AI will make it slower, more expensive, and harder to debug. We'd rather lose a project than oversell one.
Most AI shops chase enterprise contracts because that's where the revenue is. We do enterprise work, but we also work with MSMEs and early-stage startups — at price points that match. The same engineers do both. The same discipline applies to both. Smaller engagements get the same eval coverage and observability that the big ones do.
Every engagement ends with running code, a runbook, and a system your team can maintain or extend. Not a forty-slide deck and an invoice. We'll write the deck if you genuinely need one for a board, but it's an artifact of the work, not the deliverable.
Sales and discovery from Dubai. Engineering from Gujarat. You get whichever team is most useful for the conversation you're trying to have, without the awkward "let me check with the team in [other country]" delay that kills momentum.
Most AI agencies hide pricing because the answer is “it depends.” It does depend. But we’d rather give you ranges up front so you can decide whether we’re worth a call. These are honest current ranges, current as of mid-2026.
A two-to-three-week engagement where we map your processes, audit your data, and deliver a written assessment plus a phased build roadmap. Most clients use this either as a standalone deliverable or as the first step before deciding whether to engage us for build work.
₹2,50,000 – ₹6,00,000 ($3,000 – $7,500)
Final price depends on team size, data sprawl, and number of stakeholders to interview.
A four-to-eight-week engagement to build a working version of the highest-priority system identified in the audit. Goes far enough to either validate the approach or prove that we should change course before scaling.
₹6,00,000 – ₹20,00,000 ($7,500 – $25,000)
Full production builds — typically three to six months of work, sometimes longer. Priced on T&M because real builds change shape mid-project and fixed-price projects either get padded or fail when scope shifts.
₹15,00,000 – ₹80,00,000+ ($18,000 – $100,000+)
Depending on scope, integrations, and team size.
For clients who want continuous improvement, on-call engineering, and regular evals on already-shipped systems. Also used for embedded AI engineering inside in-house teams.
Starts at ₹3,50,000 / month ($4,500 / month)
Founder hourly rate, when relevant for advisory work or expert reviews: ₹8,000 – ₹12,000 ($100 – $150) per hour. All prices in INR are exclusive of GST. International pricing in USD; we invoice in USD or INR depending on client preference and tax position.
Get answers to common questions about our AI solutions, process, and partnership approach.
Whether it's a vague "we should be doing something with AI" or a specific "we have this exact workflow we'd like automated," start there. We'll write back within one working day, usually faster.
No upsells, no surprise fees, no twelve-page MSAs at the start. Email reply for the first round, calls only when you want one.