AI is in our name because it is in the work
We build AI into products people use every day — either inside the software you already run, or as something new from scratch. Practical features with measurable outcomes, not demos.
Two ways in, depending on what you already have
Most clients arrive with one of these two situations. Both are normal starting points.
Add AI to what you already run
You have a working product. We layer AI into it — an assistant over your data, document extraction, smart search, automated triage — without a rewrite and without downtime.
- Audit of your current stack and data
- AI features shipped behind your existing login
- Integrates with the database and APIs you already have
- Rollout behind feature flags, so you can switch it off
Build it new, AI-native
A web app, a mobile app, or both — designed from the first sketch around what AI makes possible, not bolted on afterwards.
- Web apps in Next.js and React
- Android and iOS from a single codebase
- AI agents and automation in the core flows
- Design, build, launch and app-store release
Run and support it
Hosting, domains, deployments and monitoring on Azure — so the thing we build keeps running and keeps improving after launch.
- Azure hosting, domains and SSL
- CI/CD from your GitHub repository
- Monitoring, backups and disaster recovery
- 24×7 support for live systems
What we build with AI, in plain terms
No jargon for its own sake. Each of these is something we have shipped into a real workflow.
AI agents
Software that does the work rather than just answering: reads the ticket, checks the system, drafts the reply, updates the record — with a human approving anything that matters.
Multi-agent workflows
Several specialised agents working a process end to end, each handling the step it is good at and handing off to the next.
Chat over your own data
Retrieval-augmented generation across your documents, policies and records, so answers cite your material instead of guessing.
Document & image understanding
Pull structured fields out of invoices, forms, IDs and scans. OCR, classification and validation, wired into your existing workflow.
Predictive models
Classic ML where classic ML wins — demand forecasting, churn and risk scoring, anomaly detection — trained on your own history.
Speech & voice
Transcription, call summaries and voice interfaces, including Indian-language support where the models allow it.
Evaluation & guardrails
Test sets, output scoring and human review built in from the start, because an AI feature you cannot measure is one you cannot trust.
Private & on-premise AI
Open-weight models self-hosted on your own infrastructure when data cannot leave your network for regulatory reasons.
We pick the model to fit the job, not the other way round
Cost, latency, accuracy and where your data is allowed to live all point at different models. We are not tied to one vendor, and we will tell you when the cheapest option is good enough.
| Model family | Provider | Typically used for |
|---|---|---|
| Claude | Anthropic | Long-context reasoning, agentic workflows and code generation |
| GPT | OpenAI | General reasoning, tool calling and structured output |
| Gemini | Multimodal work across text, image and video | |
| Llama | Meta | Open-weight, self-hosted deployments where data must stay in your network |
| Mistral | Mistral AI | Efficient open-weight models for cost-sensitive, high-volume tasks |
| Whisper | OpenAI | Speech-to-text and transcription |
| Embedding models | Multiple | Semantic search and retrieval over your own content |
| Diffusion models | Stability, others | Image generation and editing inside product workflows |
Model versions move fast, so we deliberately build against families rather than pinning to one release. Swapping the underlying model is a configuration change in our systems, not a rewrite — which is how it should be.
Current tools, chosen to last
Everything here is mainstream, well supported and hireable for — so you are never locked into us to keep your own product running.
Mobile
Data
Cloud & DevOps
AI tooling
We will tell you when you don’t need AI
Plenty of problems that get pitched as AI problems are really a missing report, a badly designed form, or a database query nobody wrote. Those are cheaper and more reliable to fix directly, and we would rather say so than sell you a model.
Where AI genuinely wins — unstructured text, documents, images, conversation, prediction over messy history — we scope it against a number you care about, put evaluation in place before launch, and keep a human in the loop wherever a wrong answer would be expensive.
Talk through your use caseTell us the problem, not the technology
Describe the process that is slow, manual or error-prone. We'll come back on whether AI is the right answer, what it would take, and what it would cost.