AI for SaaS that lives in your product
In-product copilots, AI-native features and support deflection — built API-first by a team that ships and operates its own AI products in production.
Where AI actually moves the needle in SaaS
For a SaaS company, AI is usually a product surface, not an internal tool. Customers now expect a copilot inside the app — something that understands their data, answers questions, and takes actions on their behalf. Building that well is deceptively hard: it has to be multi-tenant, secure, fast, observable, and reliable enough to put your brand on. That’s exactly the engineering we do every day for our own products.
The fastest ROI is often support and onboarding. A RAG agent over your docs and changelog plus a user’s account state can deflect a large share of tickets while improving answers, and an in-app onboarding guide can lift activation. These are bounded, measurable features that move retention and support cost — the metrics your board actually tracks.
Beyond support, AI-native features become a differentiator: natural-language querying of a user’s workspace, automated workflows, and intelligent summaries. We build API-first with evals, observability and cost controls baked in, because the hard part of production AI in SaaS isn’t the demo — it’s reliability, latency and unit economics at scale. We’ve shipped that for India and the World in our own products.
What we build for SaaS teams
In-product copilot
A multi-tenant assistant over a user’s workspace data that answers questions and takes actions inside your app.
Support deflection RAG
Resolves tickets grounded in your docs, changelog and the user’s account state, escalating the hard cases with context.
AI-native feature build
Natural-language querying, smart summaries and automated workflows that become a differentiator, not a gimmick.
Onboarding & activation guide
An in-app agent that walks new users to their first value moment to lift activation and retention.
Eval & observability harness
Test suites, tracing and quality monitoring so your AI features stay reliable and improvable in production.
Cost & routing optimization
Model routing and caching that hold quality while controlling token spend as usage scales.
How we deliver
| Capability Parameter | System Specification |
|---|---|
| Integrations | Your APIs and database, auth/SSO, vector store, helpdesk, analytics and billing systems |
| Models | Frontier LLMs with cost-aware routing; grounding on your product data and docs |
| Guardrails | Per-tenant isolation, evals and observability, grounded answers, latency and cost budgets |
| Engagement | Fixed-scope build, 4–10 weeks, then optional operate retainer |
| Typical budget | ₹20L–₹50L / $20k–$60k per production system |
| Data & compliance | Multi-tenant data isolation, SOC2-aligned practices, no training on customer data |