Which models do you use?

We are model-agnostic — latest Claude, OpenAI, or open-weights — chosen per task on quality, latency, and cost.

Can you work with our private data?

Yes — RAG over your documents with access controls, and self-hosted options when data cannot leave your environment.

What does THWorks actually build?

Three pillars. (1) AI-driven MVPs — production-ready full-stack products in 30 days. (2) AI automation — agents, n8n / Make / Zapier workflows, RAG systems, and custom LLM applications. (3) FinTech & trading infrastructure — OMS/RMS, broker APIs, reconciliation engines, risk dashboards, dealer & admin panels, trade analytics, compliance tooling, and internal CRMs for trading firms.

How long does a typical MVP take?

30 days from discovery to production-ready. The playbook is: ruthless scoping at week 1, daily progress in week 2-4, no scope creep without a re-spec. Founder Selvaraj has shipped this way at WazirX, ShopX, and Multipli.fi.

Can you build trading infrastructure, or only AI agents?

Both. At Multipli.fi, founder Selvaraj built the matching engine, trade executor, and order management systems on Rails / Kafka / Redis / RabbitMQ. We use the same stack and discipline today for OMS/RMS, broker integrations, reconciliation engines, and risk dashboards for trading firms.

What is your engagement model and pricing?

Fixed-scope project pricing for MVPs and well-defined builds. Monthly retainer for ongoing AI automation, infrastructure, or platform work. Fortnightly sprints with transparent progress tracking. Email contact@thworks.org for a scoped quote.

Do you work outside India?

Yes. Most of our clients are in the UK, US, and Canada. We work asynchronously across timezones from our Bengaluru HQ at HAL 2nd Stage, Indiranagar. Founder is reachable on +91 97883 43294 by call / WhatsApp / email.

All Services

Production AI

AI Agents & Data Systems

Production-grade AI agents and data pipelines — not demos that break in week two.

Build Your AI System

We build AI agents, RAG systems, and the data infrastructure behind them — embeddings, vector search, model hosting, and the databases that feed them. Everything is built to run in production: evaluated, observable, and cost-aware.

What We Do

AI agents

LangChain / LlamaIndex agents with tools, memory, and guardrails.

RAG & embeddings

Retrieval pipelines over your documents with Pinecone / Milvus vector stores.

Data systems

Postgres / MongoDB schemas, ETL, and pipelines that keep models fed with fresh data.

Model hosting & integration

Hosted or self-managed model serving wired directly into your application.

How We Work

We treat AI like software — with evaluation, observability, and a cost budget — not a one-off prompt. We build a thin vertical slice first, measure it against real data, then harden and ship.

Define success metrics before building anything

Build a thin vertical slice to validate the approach early

Evaluate against real data with eval sets and guardrails

Harden for production: monitoring, cost controls, and fallbacks