Laraware — India's First AI-Powered Fintech Stack™

Generative AI, engineered into your product.

RAG knowledge systems, in-product copilots, fine-tuned models, and private LLM deployments — built like production software, because that's what they are.

Every board now has a "we should do something with AI" agenda item. We turn that item into shipped product: assistants that know your data cold, copilots that make your software stickier, and models tuned to your domain instead of the whole internet. The demo is the easy 10%. We're built for the other 90% — evals, cost control, monitoring, and the unglamorous engineering that keeps an AI feature trustworthy at scale.

What we build

  • RAG knowledge systems — your policies, catalogs, contracts, and SOPs, answerable in plain language, with citations
  • In-product copilots — AI features inside your SaaS that users actually return for
  • Fine-tuning and evaluation pipelines — smaller, cheaper models tuned to your task, with eval suites so quality is measured, not guessed
  • Multilingual and vernacular AI — Hindi, Hinglish, and regional-language experiences for Bharat-first products
  • Private and self-hosted LLM deployments — open-source models on your infrastructure when data can't leave
  • Document intelligence — extraction, classification, and summarization for invoices, KYC documents, and contracts

How we build

We treat LLM features like any other production system: versioned prompts, automated evaluations on every change, cost budgets per feature, live monitoring, and graceful fallbacks when a model misbehaves. A two-week proof of concept comes first; it only graduates to production once it clears the eval gate we agree on together.

FAQ

Which model will you use — GPT, Claude, or open-source?

Whichever wins on your task, your data constraints, and your unit economics. We benchmark before we commit, and we design so the model can be swapped later without a rewrite.

Is our data safe?

Your data is never used to train third-party models under our default architecture, and for stricter needs we deploy fully self-hosted models inside your infrastructure.

How do we keep AI costs from exploding?

Per-feature cost budgets, caching, model right-sizing, and monthly reviews. Most clients are surprised by how cheap a well-engineered AI feature is — and how expensive a careless one becomes.

Can you add AI to our existing app?

Yes. Most of our generative AI work lands inside existing Laravel, mobile, and SaaS products rather than greenfield builds.

Have a project in mind?

You own the code. We own the outcome.

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