Your forward‑deployed AI team.
We embed with growing businesses and ship AI into production across your product, your operations and your revenue engine, measured in money saved, not demos.
The model is no longer the bottleneck. Deployment is.
Big companies solve this by hiring forward-deployed engineers. Growing businesses can’t, so they work with us.
AI is easy to pilot and hard to ship
Almost every company is experimenting with AI agents; far fewer have them running reliably inside real systems. The hard part is now integration, evaluation and governance, not the model.Deployment engineers are the new bottleneck
AI labs and enterprises are racing to hire forward-deployed engineers, the people who embed with a business and make AI work in production. Postings for the role rose more than 1,000% this year.Source: Fortune, Sep 2026 (Lightcast data) ↗Growth became an engineering problem
Revenue now runs on a stack of enrichment, sequencing and CRM tools that rarely talk to each other. The teams that win engineer that stack, and most companies have no one to do it.Your product. Your operations. Your revenue.
Three equal disciplines, one standard: shipped into your stack, running in production and measured against the money it saves or makes.
AI Engineering
AI features and agents that survive real users.From prototype to production, with evaluation, failover, cost control and monitoring built in.- AI agents with guardrails
- RAG & knowledge systems
- LLM & API integrations
- Evals, failover & cost routing
Forward-Deployed Engineering
An engineer embedded in your team who owns the outcome.We sit inside your workflows, find where time and money leak, then automate and ship until the number moves.- Embedded with your team
- Process & workflow automation
- n8n, Make or custom code
- Human-in-the-loop operations
GTM Engineering
Pipeline systems that find, qualify and follow up.Clay, Apollo, your CRM and sequencers engineered into one revenue engine, so reps spend their time on conversations.- Enrichment & AI lead qualification
- Outbound & reply infrastructure
- CRM workflows & hygiene
- Pipeline reporting you trust
Click through the system before you pay for it.
Every engagement starts with a working demo of your future system, built on sample data that mirrors your business. Here are four we’ve already built.
Systems we’ve built.
For each project we say what the problem was, what was built and which part we did. Numbers appear only when verified and approved.
Stop false “hot deals” reaching your CRM
A cost-ordered classifier for English and Dutch cold-email replies that decides which replies become CRM deals, built so a false “hot deal” needs several layers to agree.Cut AI costs and survive provider outages
A provider-neutral LLM gateway: code asks for a job, the gateway routes it through a chain of models with safe failover, a deadline per call, and the cost of every call written to a ledger.Catch AI agents before they invent a number
Invariant-checked evaluation suites for tool-calling agents that run in CI and fail the build when an agent invents a number, leaks its prompt or obeys an injection.From first look to a system that pays for itself.
Four stages. You see the problem, the demo and the ROI before you commit to a build.
- Before we ever pitch, we review your website, search and AI-answer visibility, operations and sales process, and pinpoint the problems worth solving.Find
We find where time and money leak
- Website, SEO & AEO review
- Process and tool map
- The 2–3 problems costing the most
- An AI Workflow Assessment plus a working demo dashboard built on sample data that mirrors your business, so you see the system and its ROI before paying for a build.Prove
We prove the value before you commit
- AI Workflow Assessment
- Demo dashboard of your future system
- Savings & ROI estimate
- A fixed-scope build on the tools you already use, such as your CRM, Clay, Apollo, n8n and the leading models, shipped in weekly working releases.Build
We build it into your stack
- Fixed scope, fixed fee
- Weekly working releases
- Docs, monitoring and hand-over
- A monthly partnership: we monitor and improve what we built, measure the savings against the estimate and add new workflows as you grow.Run
We run and grow it with you
- Monitoring & incident response
- Measured savings, reported monthly
- New workflows each month
Engineering that lowers the cost of running your business.
We don’t sell AI for its own sake. Every engagement starts with an estimate of the hours and money a system will save, and we measure it after launch. If the numbers don’t justify a build, we tell you.
Hours removed
Manual data entry, copy-paste between tools and recurring reports handed to systems, measured against your baseline.Fewer tools and seats
Overlapping SaaS consolidated and per-credit tools replaced with bring-your-own-key builds where it is cheaper.Lower AI spend per task
Each step routed to the cheapest model that meets its quality bar, with caching and cost per run tracked.Less rework
Validation and evaluation gates catch errors before they reach customers, finance or the CRM.Models, automation and GTM tools, engineered into one system.
We use these tools every day. We don’t resell them; we connect them so data flows once, every step is logged, and your team stops paying for overlap.
A studio built to grow, led by the engineer who builds.
Kumar Sahil builds AI systems for production: the queues, evaluation gates, failover and integrations that keep an AI feature working with real users and messy data.
He also builds go-to-market infrastructure (enrichment, lead pipelines, CRM automation), so DWARY Intech understands both how a system is built and how a business grows with it.
Kumar Sahil
Founder & lead engineer · New Delhi, India- Production AI: agents, RAG, eval gates, failover
- GTM engineering on Clay, Apollo, HubSpot, Smartlead
- Automation on n8n and custom Python
Notes from building production AI.
Why AI automation projects fail in production, and what to build instead
Seven failure modes we see repeatedly, and the engineering that prevents each one.Read the post →How to calculate the ROI of an automation project
Clay waterfall enrichment: cut credit spend without losing coverage
Questions founders ask us.
An engineer who embeds with your team, learns how the business actually works and owns getting a system into production, rather than handing over a slide deck or a prototype. AI labs and large enterprises now hire for this role at a premium; DWARY Intech gives growing businesses that capability without the full-time hire.
A fixed $1,500 for one workflow, delivered in 1–2 weeks. If you go ahead with the build within 60 days, the full fee is credited against it, so the assessment costs nothing extra. Builds are quoted as a fixed fee once the assessment has scoped them.
It starts with an AI Workflow Assessment of one process. If there is a strong case, we scope a fixed build, ship it in weekly iterations into your existing tools, then either hand it over with documentation or keep running and improving it for you.
DWARY Intech is led by Kumar Sahil, who scopes and builds your system. As the studio grows, specialists join projects, and you always know who is doing what.
Claude, OpenAI and Gemini models; n8n, Make and Zapier; HubSpot and Salesforce; Clay and Apollo; and custom Python or TypeScript where off-the-shelf tools fall short. We build on the stack you already pay for wherever possible.
We use provider APIs and settings that do not train on your data, and build in your own accounts with your own keys wherever possible, so you own the system and the data.
Yes. We work with teams in the US, UK, Europe and Australia, as well as India, and overlap with your working hours for calls.
Yes. Mention it in the assessment form and we will sign one before you share any details.
Find the workflow worth automating first.
Tell us about one process that costs your team hours every week. We will tell you honestly whether AI or automation is the right fix, and what a first build would take.