LILA AI Assistant's answer

What makes LILA unique

• Embeds into YOUR product - Not another standalone tool your users have to learn. Lives inside their existing dashboard.

• Schema-aware from day one - Understands your database structure, relationships, and business logic. No weeks of training or fine-tuning.

• Built for multi-tenancy - Each user only sees their own data. Tenant isolation is automatic, not an afterthought.

• White-label ready - Your branding, your domain. Users never know it's LILA.

• Self-hosted LLM option - Sensitive data never leaves your infrastructure. No OpenAI dependency.

• Answers, not just SQL - Returns formatted results with auto-generated charts. Users get insights, not database output.

LILA AI Assistant's answer

• No vendor lock-in - Bring your own LLM or use ours. Switch anytime. Your data, your infrastructure, your choice.

• Minutes to integrate, not months - Drop in a widget, connect your database, done. No complex setup wizards or professional services required.

• Built for B2B SaaS reality - Multi-tenant from the ground up. Competitors bolt it on later and it shows.

• Predictable pricing - No per-query charges that explode with usage. Know your costs upfront.

• Actually understands your schema - Reads your database structure, not just column names. Knows that "revenue" means orders.total minus orders.refunds.

• Support that responds - Small team, direct access. Not a ticket queue that takes days.

LILA AI Assistant's answer

SaaS founders and product teams who are tired of building custom reporting features that never satisfy everyone. They want to give users self-serve data access without hiring a BI team.

B2B platforms with non-technical users - CRMs, ERPs, e-commerce dashboards, marketplaces - where customers constantly ask "can you pull this report for me?"

Companies with sensitive data who need AI capabilities but can't send customer data to third-party APIs. Healthcare, finance, legal tech.

Dev teams stretched thin who need to ship analytics features without dedicating engineers to build and maintain dashboards.

Not for: Data scientists who love writing SQL. Enterprise giants with dedicated BI departments. Consumer apps.

LILA AI Assistant's answer

After 16 years of building custom software for clients, one request never stopped coming: "Can you add a report that shows X?"

Every project. Every client. Endless dashboard iterations that were outdated the moment they shipped. Business users waiting days for a developer to write one SQL query.

We built reporting modules for ERPs, CRMs, e-commerce platforms. Each time, the same pattern: users needed answers faster than dev teams could build dashboards.

When LLMs became viable for code generation, the solution clicked. What if users could just ask their database directly? No tickets. No waiting. No "we'll add that to the backlog."

LILA started as an internal tool for our own client projects. It worked. Clients stopped asking for new reports because they could find answers themselves.

Now we're making it available to every SaaS team facing the same problem we solved.

Built by AALA Solutions. Backed by two decades of knowing what business users actually need from their data.

LILA AI Assistant's answer

AI Engine - Python / FastAPI - Proprietary LLM orchestration layer - Multi-provider inference routing (OpenAI, Anthropic, self-hosted models) - Real-time WebSocket streaming

Backend - Node.js / NestJS - PostgreSQL with TypeORM - Event-driven microservices architecture - JWT-based multi-tenant authentication

Frontend - EmberJS (Enterprise Admin Dashboard) - Astro (Static-optimized Marketing) - Framework-agnostic Embeddable Widget (Vanilla JS, <200kb)

Infrastructure - Containerized deployment (Docker/Kubernetes-ready) - Reverse proxy with SSL termination - In-memory caching layer (Redis-compatible) - Cloud-native object storage for schema management