AI-Powered Analytics Platform
Enterprise-grade AI analytics platform that transforms raw business data into actionable intelligence, cutting analysis time from days to minutes.
Project Background & Objectives
Enterprise Analytics SaaS sells analytics SaaS to mid-market manufacturing and retail companies. Their existing platform was a BI dashboard — valuable, but passive. Customers still needed data analysts to build queries, interpret outputs, and translate findings into decisions. This created a 5–7 day lag between a business question arising and an answer materialising. Dristi Technologies reimagined the platform as an AI-native intelligence layer: natural language querying, predictive models, anomaly detection, and automated narrative reports — all accessible to non-technical business users.
The most powerful AI feature we shipped was not a model — it was natural language querying. When a regional sales director can ask 'Why did our Northern region underperform last quarter?' and receive a causally reasoned answer in 90 seconds, AI adoption across the business becomes inevitable.
What Problem Were We Solving?
Enterprise Analytics SaaS had valuable historical data from 200+ enterprise customers but no machine learning capabilities. Their platform could show what happened, but not why, or what would likely happen next. Customer churn was highest among technically unsophisticated users who found the query builder too complex. Competitors were shipping GPT-4 powered natural language interfaces, and Enterprise Analytics SaaS faced losing their competitive differentiation within 18 months without a credible AI roadmap.
How We Delivered Results
We built a three-layer AI architecture: a data ingestion layer (Apache Kafka + Delta Lake), a model serving layer (FastAPI with custom-trained forecasting models fine-tuned per customer vertical), and an LLM-powered interface layer using GPT-4 for natural language query translation. A proprietary Retrieval-Augmented Generation (RAG) pipeline grounds model outputs in each customer's specific dataset, eliminating hallucinations in business-critical contexts. Automated narrative reports — plain-English summaries of anomalies, trends, and predictions — are generated nightly and delivered to stakeholders without requiring platform login.
Audited data pipelines for 200+ customer tenants, identified schema inconsistencies, and designed a Delta Lake medallion architecture to support reliable model training at scale.
Trained vertical-specific forecasting models (retail demand, manufacturing yield, supply-chain risk) with customer data and validated against 18 months of held-out actuals.
Integrated GPT-4 with a proprietary RAG pipeline grounded in each tenant's schema, ensuring natural language query responses are factually accurate and traceable to source data.
Deployed to 12 design-partner customers first, iterated on prompt engineering and model accuracy, then rolled out to the full 200+ customer base with a self-service onboarding flow.
Built With Industry-Leading Tools
Every technology was selected for its production readiness, scalability potential, and fit with Enterprise Analytics SaaS's long-term roadmap.
In Their Own Words
“We went from a dashboard company to an intelligence company. Our customers used to need a data analyst to extract value from the platform. Now a VP of Sales can ask a question in plain English and get a boardroom-ready answer in 90 seconds. That is a genuinely different product.”
Results That Speak for Themselves
Quantifiable impact delivered for Enterprise Analytics SaaS across every key performance dimension.
Full List of Outcomes
How We Deliver Enterprise-Grade Projects
A proven four-phase framework that consistently delivers on time, on budget, and above expectations.
Discovery & Scoping
Stakeholder workshops, existing system audit, user research, and definition of success metrics with all key decision-makers.
Architecture & Design
System architecture blueprint, UX/UI wireframes, API contracts, and technology stack validation against scalability requirements.
Agile Development
Two-week sprint cycles with CI/CD pipelines, automated test coverage, and weekly demos to ensure continuous alignment.
Launch & Optimise
Zero-downtime deployment, real-user monitoring, performance tuning, and a dedicated hypercare period with SLA guarantees.
