AI, Data & Analytics. Decisions at machine speed.
Relic's AI & Data practice turns raw enterprise information into products, predictions and profit. We build the platforms, data lakehouses, feature stores, MLOps pipelines, and the applications: generative AI copilots, forecasting engines, computer-vision QA, and personalization at scale.
What you get with Relic.
Production-grade AI
Governed, monitored and measured, never a science project that stalls in review.
Single source of truth
Unified lakehouse replaces spreadsheets and duplicated marts.
Faster decisions
Self-service analytics for the business, powered by trusted data contracts.
Responsible by design
Model cards, bias tests and human-in-the-loop workflows.
What we're built to solve.
Data trapped in silos
Every function has its own warehouse; no one agrees on the numbers.
GenAI pilots without ROI
Chatbots demoed to leadership never make it past a proof of concept.
Model drift & risk
Deployed models decay quietly, exposing the business to compliance risk.
Cost of experimentation
GPU and vector-store spend explodes with each new use case.
How Relic delivers.
Lakehouse platforms
Databricks or Snowflake on modern iceberg storage with governance built in.
GenAI product studio
RAG copilots, agents and enterprise search grounded in your data.
MLOps foundation
Feature stores, CI/CD for models, drift detection and automated retraining.
Analytics acceleration
Semantic layer, LLM-powered BI and executive decision hubs.
A modern stack.
Our delivery rhythm.
Deep-dive workshops to align technology moves with business outcomes and quantify ROI.
Reference architectures, target operating models and a phased delivery roadmap.
Sprint-based delivery with senior engineers, embedded QA and continuous stakeholder demos.
Blue/green rollouts, feature flags and a runbook-first approach to production readiness.
Observability, FinOps and iterative tuning to compound value after launch.
Selected outcomes.
Deployed a submissions triage AI grounded in policy history and market data.
Built a customer copilot with RAG over accounts, transactions and policy.
Replaced spreadsheet-based demand planning with a probabilistic ML engine.
Frequently asked.
Do you build your own models?+
We fine-tune, ground and orchestrate the best available foundation models, proprietary only when needed.
How do you keep our data private?+
Private endpoints, tenant isolation, no data used for training, full audit trails.
Where do you start?+
A 6-week discovery to identify 2–3 use cases with quantified ROI and a delivery plan.
How do you prevent GenAI pilots from stalling?+
Every use case needs a business owner, evaluation harness, cost model and production path before we invest past discovery.
Can you build on our existing lakehouse?+
Yes. We extend Databricks, Snowflake and similar platforms with governance, MLOps and applications rather than replacing them by default.