Service
AI & Machine Learning
Agents, chatbots and ML models that survive real users.AI agents, agentic workflows, chatbots, RAG systems, custom ML models and computer vision — engineered with evaluation, guardrails and cost control from the first line of code.
Overview
There is a wide gap between an AI demo and an AI product. The demo works because you tried it five times. The product has to work on the ten-thousandth query, in a language you do not speak, when the user asks something absurd, at a unit cost that does not destroy your margin.
We build the second kind, across the whole spectrum: autonomous agents that take real actions in your systems, chatbots your customers actually get answers from, retrieval over your own documents, and custom models trained on your data when a general-purpose one will not do.
We are not new to this. We designed, trained, deployed and now operate the AI inside our own products — a voice-first agricultural assistant serving farmers in nine languages, and a generative interior-design engine constrained by a deterministic rule system. Every recommendation on this page is something we run in production ourselves.
Capabilities
What we build
Everything below is AI and Machine Learning work we have shipped to production.
AI agents & agentic workflows
Autonomous and semi-autonomous agents that plan, call your tools and APIs, and complete multi-step work — with scoped permissions, bounded retries, full execution traces and human approval gates on anything irreversible. Single agents or multi-agent systems where specialists hand off to each other.
Chatbots & conversational AI
Customer support, sales and internal-helpdesk bots that answer from your own knowledge base with citations, hold context across a conversation, escalate cleanly to a human, and deploy to your website, WhatsApp, Slack or app.
LLM applications & copilots
Domain assistants with a real persona, long-term memory of the user, and context assembled from your live systems — plus in-product copilots that draft, summarise, extract and classify inside the workflow rather than beside it.
Retrieval-augmented generation (RAG)
Chunking tuned to your document structure, multilingual embeddings, pgvector or a dedicated vector store, hybrid keyword-plus-vector search, reranking, and citations so every answer is traceable to a source.
Custom ML model development
When an API model is the wrong tool: forecasting, recommendation, ranking, churn and risk scoring, anomaly detection, NLP classifiers. Feature engineering, training, validation against a real baseline, and an honest answer on whether the lift justifies the model.
Model fine-tuning & optimisation
Fine-tuning and LoRA adaptation of open-weight models on your data, distillation of a large model into a small fast one, plus quantisation and ONNX conversion to cut latency and inference cost.
Computer vision
Image classification, object detection, OCR and document extraction, and automated quality inspection — trained on your data and deployed on-device with TensorFlow Lite or behind an API, depending on latency and privacy needs.
Voice AI in Indian languages
Speech-to-text and text-to-speech across Indian languages, tuned for the accents and code-mixing real users actually speak in. We ship a product whose primary interface is voice, in nine languages.
MLOps & AI infrastructure
Training pipelines, model registries and versioning, staged rollout with shadow deployment, drift and quality monitoring, GPU and inference cost management, and retraining that runs on a schedule rather than when someone remembers.
AI strategy & discovery
A costed roadmap that maps your workflows against what models are actually good at, prototypes the highest-value one, and tells you plainly which of your ideas do not need AI at all.
Evaluation, guardrails & AI safety
Golden datasets, regression suites in CI, hallucination and refusal testing, PII redaction, prompt-injection defences, output schema validation, and dashboards that tell you when quality drifts before your customers do.
Private & on-premise AI
Open-weight models running inside your own cloud account or data centre, so sensitive data never leaves your perimeter — with the serving, scaling and cost work that makes self-hosting actually viable.
Technology
What we build it with
Foundation models
Agents & orchestration
Retrieval
ML & training
Speech & vision
Serving & MLOps
Deliverables
What you get
- Working AI feature integrated into your product, not a notebook
- Agent tool definitions, permission scopes and execution traces
- Versioned prompt templates with a documented change process
- Evaluation dataset and automated scoring running in CI
- Trained model artefacts, training code and reproducible pipelines
- Cost model: per-request, per-conversation and per-active-user projections
- Guardrail policy covering refusals, PII and escalation to a human
- Fallback behaviour for every provider outage path
Delivery
How the engagement runs
Same five stages every time, so you always know what week you are in and what comes next.
Discover
We start by understanding the business, not the feature list. Who is the user, what decision are they trying to make, what does success look like in numbers, and what is genuinely fixed versus assumed. Usually this reshapes the brief.
Design
Flows before pixels, then high-fidelity screens covering every state that matters — including the empty, offline and error states most projects discover late. Architecture is decided here too, and written down with its trade-offs.
Build
Two-week iterations, something in a real environment every single week, and a demo you can use rather than a status report you have to read. Typed contracts and CI keep the frontend and backend honest with each other.
Ship
Launch is a process, not a date: staged rollout, monitoring and alerting live before the first user, store submissions prepared against current policy, and a rollback path that has been tested rather than assumed.
Scale
After launch the questions change: what do users actually do, where does it cost too much, what breaks first at ten times the load. We instrument, review monthly, and plan the next quarter from evidence.
FAQ
AI & Machine Learning questions
Related
Often paired with
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Cross-platform apps that work offline, survive patchy networks and actually make it through Play Store and App Store review.
ExploreLet us talk about your AI & Machine Learning project.
Tell us the problem and the constraints. You will get an honest read on scope, timeline and whether we are the right people for it.