We work with teams facing technology choices that are costly to get wrong. Which workloads belong in the cloud, and which at the edge? Where can AI improve a real workflow? What should be automated, and what should stay with people? How should software be built so it keeps improving after launch?
Our advice is shaped by what it takes to build and run these systems, so recommendations stay practical. We are based in Mumbai and work remotely with teams across India.
Turn technology plans into working systems
Our aim is to help teams choose the right technology and get it working in production. A strategy document on its own changes nothing, so we stay involved through the build, not just the plan.
Consulting that stays close to the build
Cloud, software, and platform delivery
We design cloud and edge platforms on AWS, Azure, and Google Cloud. The work covers landing zones, infrastructure-as-code, CI/CD pipelines, cost tagging, and monitoring. It ends with runbooks for the team that will own the platform.
Applied AI and automation
We build LLM systems that answer from your own approved documents, a method called retrieval-augmented generation (RAG). We also build AI agents that handle multi-step tasks with limited tool access and human approval points. Both come with test sets and guardrails that check quality before each release.
Private AI and process automation
We deploy language models on your own servers or private cloud, so sensitive data stays inside your network. We also automate repeatable business processes such as approvals and document intake, using AI where a step involves reading or judgment and a person for the exceptions.
What our consultants do
Our consultants cover technology strategy, architecture, and hands-on engineering. Their work runs through four stages, from discovery to handover.
In discovery, they map the workflow, its constraints, and what a good result looks like. In design, they set the architecture and the quality checks. Next comes a small prototype on real data and tools, built to test the riskiest assumption first. In production, they harden security, monitoring, and documentation, then hand the system to the team that will run it.
Each service page shows how these stages apply to that kind of work.
Problems we help teams solve
We organize our work around problems, not sectors. Each problem below matches one of our six services.
- Cloud & Edge Infrastructure: Cloud costs and security exceptions keep growing, and latency-sensitive workloads run too far from the machines and users that depend on them.
- Private AI Deployment: Teams want AI, but contracts, records, and source code cannot be sent to an outside service.
- LLM Fine-Tuning & RAG: An LLM pilot works in a demo, but answer quality is uneven and nobody can explain why one answer is reliable and another is not.
- Agentic AI & Orchestration: An agent prototype looks impressive but has no clear tool permissions, monitoring, or escalation path.
- Business Process Automation: Staff re-key data between systems, approvals wait in inboxes, and nobody can say how long a routine process takes.
- AI Guardrails & Evaluation: AI changes ship without tests, so every release is a judgment call and every failure is a surprise.
How we work with clients
Define the problem before the build
We agree on the problem, the constraints, and how success will be measured before we recommend any architecture or tools.
Plans tied to delivery
A plan is only useful if someone can carry it out. Each recommendation comes with implementation steps, an owner, and a view of what your team must support after launch.
The simplest tool that does the job
We recommend new technology only when there is a clear reason for it. If a rules-based workflow or a plain API integration can do the job, we say so rather than propose an AI agent.
Tradeoffs in the open
We spell out each major choice: what you gain, what is deferred, and what needs to be true for it to work.