Capability
Artificial intelligence, put to work in production.
We help you move AI past the demo and into the day job, where it saves hours, cuts errors and holds up under real users, real data and real audits.
Overview
Most organisations do not have an AI problem. They have a pilot that never left the sandbox. The demo impressed everyone in the room, then stalled the moment it met messy data, unclear ownership and a security review. We spend our time on the unglamorous part that decides whether AI pays off: getting it into production, keeping it accurate and making it something your people trust and actually use.
We work end to end. We advise on where AI genuinely earns its place and where a simpler tool would do the job better and cheaper. We build the applications, copilots, agents and retrieval systems that sit on top of your own knowledge and workflows. Then we help you run them, because a model that drifts, hallucinates or quietly gets more expensive every month is a liability, not an asset.
Our roots are in heavy industry: oil and gas, mining, manufacturing and logistics, sectors where a wrong answer has a cost and where documents, standards and history matter. That grounding shows in how we build. We care about traceability, about citing sources, about a human being able to check the machine, and about the guardrails that keep a helpful assistant from becoming a confident liar. The same discipline serves clients in finance, healthcare, retail and the public sector just as well.
With offices in Pune, Bengaluru, Indore and Kolkata, we combine strong Indian engineering talent with the delivery standards global clients expect. You get senior people who will tell you plainly what AI can and cannot do for your business today, and a team that will still be there when the pilot needs to become a platform.
What we deliver
Inside our artificial intelligence work.
AI Strategy & Roadmap
We find the handful of use cases where AI clearly beats the alternative, size the effort and the payback honestly, and give you a sequenced plan rather than a wish list.
Generative AI & Microsoft Copilot
Assistants and copilots built on your own documents, systems and rules, including Microsoft 365 and Copilot on SharePoint, so teams get fast, grounded answers from approved content instead of generic ones, with sources they can check.
AI Agents & Automation
Agents that carry out multi step tasks across your tools, from triaging tickets to drafting reports, with clear boundaries, approvals and a full audit trail.
RAG & Enterprise Search
Retrieval augmented generation that turns scattered manuals, contracts and records into one place your people can ask questions of and trust the reply.
Responsible AI & Governance
Guardrails, evaluation, monitoring and policy so your AI stays accurate, safe and explainable, and passes the scrutiny of legal, risk and your regulators.
How we work
The same senior team, all the way through.
Frame
We start with the job to be done, not the technology. We agree what good looks like, what data we can use and how we will measure whether it is actually working.
Prototype
We build a working version fast on your real data, so you can judge quality with your own eyes rather than a slide, and we tune it against clear evaluations.
Harden
We add the guardrails, security, cost controls and testing that production demands, then integrate cleanly with the systems your teams already use.
Run & improve
We monitor accuracy, cost and usage, catch drift early and keep improving the system as your data, models and needs change.
- Weeks
- From idea to a working AI prototype on your data
- -30% typical
- Time saved on the tasks we target first
- Every answer
- Traceable to its source, so people can trust it
Selected work
Artificial Intelligence in practice.
An enterprise services firm
Turning a document backlog into minutes, not weeks
- Processing time per document
- Days → minutes
- Manual keying removed
- ~70%
- Exceptions caught earlier
- +3x
A global management consultancy
An AI radar that turns one sentence into a meeting-ready target list
- Research to target list
- Weeks → minutes
- Decision-makers found
- 231
- Contacts verified real
- 90%
FAQ
Questions we hear a lot.
What is the difference between generative AI and traditional AI?
Traditional AI usually predicts or classifies: it scores a risk, forecasts a number or spots a defect. Generative AI creates new content such as text, code or images, and powers copilots and agents. Most real solutions use both, and part of our job is choosing the right tool rather than reaching for the newest one.
How do you stop AI from hallucinating or giving wrong answers?
We ground models in your own trusted data using retrieval, so answers come with sources you can check rather than being invented. We add evaluation, guardrails and human review for anything high stakes, and we monitor quality in production. No system is perfect, so we are honest about where a person still needs to sign off.
Is our data safe when we use AI and large language models?
Yes, when it is built properly. We keep your data within your own cloud or an approved environment, control exactly what a model can see, and avoid training public models on your private information. Access, logging and retention are set up to meet your security and compliance requirements from the start.
How much does it cost to build an AI solution for our business?
It depends on the use case, but we deliberately start small. A focused prototype on your data typically takes a few weeks, which lets you see real value before committing to a larger build. From there we scope production and running costs openly, so there are no surprises when usage grows.
Ready to put this to work?
Tell us the problem you are trying to solve. We will tell you how we would approach it, and whether this is even the right place to start.