Engineering work,
not a capability deck
Six areas we take client work in. Each one links to a product we have already shipped using it, so you can check the claim rather than take it.
AI product engineering
End-to-end product work: an idea becomes a system with real users, an operations story, and someone accountable for its uptime.
- Discovery and technical scoping, with an honest read on what AI adds and where it does not
- Full-stack build — data model, API, interface, and the admin tooling nobody remembers to budget for
- Deployment, monitoring, and handover documentation written for whoever inherits it
Applied ML and language models
Models built against your data and your accuracy bar, integrated into a workflow rather than parked behind a demo endpoint.
- Question generation, summarisation, classification, and extraction over domain text
- LLM integration with evaluation harnesses, so quality is measured rather than assumed
- Predictive analytics on operational data, with the failure modes stated up front
Computer vision and document intelligence
Turning paper and images into structured data — handwriting, forms, and scans, at the volumes that make manual entry untenable.
- OCR across Indian scripts and mixed-language documents
- Handwritten answer-sheet and form processing with human-in-the-loop review
- Object detection and visual inspection for industrial and operational use
Conversational and messaging automation
Systems that live in Telegram or WhatsApp because that is where the users already are — multilingual, and designed for people who will never open an admin panel.
- Telegram and WhatsApp bots with real workflow state behind them, not menu trees
- English, Hindi, and Marathi as first-class languages, chosen per user
- Reminder, escalation, and follow-up loops that close without human chasing
Cloud, edge, and offline-first
Deployment that matches the operating environment, including the environments where the network is unreliable or simply absent.
- Cloudflare Workers and Azure deployments with cost and latency reasoned about, not inherited
- Offline-first and on-device systems for kiosks, field devices, and low-connectivity sites
- Observability and alerting configured before launch rather than after the first outage
Public sector and institutional platforms
Portals and workflow systems for government bodies, boards, and regulated institutions, where the compliance and audit requirements are the specification.
- Citizen-facing e-services portals with role-based workflow and audit trails
- Multi-institution administration, hierarchical access, and district-level reporting
- Data protection and retention designed to survive an audit
The disciplines behind the work
Cloud architecture
Azure and Cloudflare deployments sized for the actual load, with the cost model understood before launch.
AI and machine learning
Custom models, NLP pipelines, and LLM integration measured against a stated accuracy bar.
Computer vision
OCR, handwriting recognition, and visual inspection across Indian scripts and real-world scan quality.
Data and analytics
Analytics that answer a question someone asked, surfaced where the decision is actually made.
Security and compliance
Encryption, access control, and audit trails designed in, because retrofitting them into a live system is far more expensive.
Interface engineering
Interfaces for mixed literacy and mixed language users, on the devices they own rather than the ones we would prefer.
Short cycles,
visible progress
We start with a scoping phase that produces a written technical plan and a cost you can hold us to. If that plan says AI is the wrong tool for your problem, it says so — we would rather lose the engagement than build something you will switch off in a year.
From there, delivery runs in short cycles with something reviewable at the end of each. You see the system in a working state throughout, not at the end.
Scoping
Problem definition, data review, technical plan, and a fixed cost for the build. Deliverable is a document, not a slide deck.
Build
Delivery in two-week cycles with a reviewable increment at the end of each. Staging environment live from cycle one.
Deploy and instrument
Production deployment with monitoring, alerting, and runbooks in place before real traffic arrives.
Operate
We run it. The engineers who built the system handle its incidents, because they are the ones who can resolve them.
Start with a scoping conversation
No commitment. Describe the problem and we will tell you whether it is one we can solve well.
Tell us what you're
trying to build
Describe the problem rather than the solution — we will tell you honestly whether AI is the right tool for it, and what it would take.